Abstract

Most of the companies’ sustainability and growth depend on how well its employees perform. However, the measurement of employees’ performance until now is inconclusive and inexhaustive. To accurately assess and predict an employee’s performance, numerous external factors (physical/environmental, social, and economic) related to an employee’s life have been taken into account in this work. The purpose of this research is to explore an unbiased AI algorithmic solution to predict future employee performance considering physical, social, and economic environmental factors that affect employee performance. We collected data of 1109 employees from the ‘For-Profit Organization’ in Bangladesh from both employers and employees to cover all the factors that justified the unbiased outcome. We utilized a few machine learning tools in this study including the Logistic Regression classifier, the Gaussian Naive Bayes, the Decision Tree classifier, the K-Nearest Neighbors (K-NN), the SVM classification, etc., in order to predict the employee performance evaluation. Then, we compared the effectiveness of those machine learning models by analyzing their precisions, recall, F1-score, and accuracy. This work can be utilized to obtain bias-free employee performance reviews. This fair employee performance assessment can aid decision-makers in making moral choices regarding employee promotions, career advancement, and training needs, among other things. The study also describes notes for future researchers.

Introduction

Performance evaluation, which includes assessing current performance, identifying good and poor performers, and providing feedback to staff, is one of the most challenging components of Human Resource Management (HRM) (Cherian et al., 2021, Fogoroș et al., 2020, Stone et al., 2015). Employee performance evaluations are not practiced systematically by numerous organizations. As a result, the evaluation method becomes erratic and ineffective. A systematic approach should be adopted in order to evaluate employees at the planning stage on a regular basis (Ahmed et al., 2013). Employees with these attributes—skills, dedication, attitudes, and knowledge—are valued as assets by the company (Al-Tit et al., 2022, Li et al., 2008, Yang and Lin, 2009). By creating new knowledge at firms’ level, an organization’s human resources can the firms to innovate (Terán-Bustamante et al., 2021). The accurate assessment of the employees’ performance contributes to the mission of the company with the maximum satisfaction of the employees (Pap et al., 2022). Since company progress depends on employee advancement, numerous executives search for efficient ways to improve performance drastically (Abbas and Yaqoob, 2009, Salam, 2021). To boost performance, employers first need to know the performance condition of the employee in any organization. An article in the Harvard Business Review (Antonio, 2018) claims that essential functions of the organizations, such as prediction, upselling, cross-selling, and performance management can be remarkably influenced by AI technologies (Ledro et al., 2023). In the future, businesses, communities, and nations will be significantly impacted by big data, automation, and machine learning (Lada et al., 2023, Tao et al., 2023). In this regard, AI has started to play a role in business, particularly in HRM, concerning the prediction and decision-making (Nilashi et al., 2023, Qureshi et al., 2023).

Conclusion

The performance of the company’s personnel determines a large portion of its sustainability and growth. Many external aspects (physical/environmental, social, and economic) relevant to an employee’s life have been included in this work in order to measure and anticipate an employee’s performance effectively. The goal of this project is to create an AI algorithmic-based ethical decision-making framework that takes into account the various environmental factors—physical, social, and economic – that have an impact on worker performance. Our results were impartial since we gathered information from a few “For-Profit Organizations” in Bangladesh, both objectively and subjectively. In this study, we used a variety of machine learning methods, and we were able to acquire a reasonable accuracy score. The Random Forest model obtained the highest accuracy score, and the Gaussian naïve Bayes model had the lowest. An impartial employee performance review can be obtained by using this work. Decision-makers can use this equitable employee performance evaluation to help them make moral decisions about training requirements, career advancement, and employee promotions, among other things.

Abstract

Cloud computing has revolutionized the way businesses and individuals manage and utilize computing resources, providing significant benefits such as scalability, flexibility, and cost-effectiveness. However, the increasing use of cloud computing to store and transmit sensitive data has raised concerns about data security. Ensuring confidentiality and data integrity while enabling effective data transmission is a significant challenge. To overcome this issue, the proposed approach integrates encryption and compression methods to enhance transmission performance in the cloud and prevent unauthorized access to confidential information. This approach applies multiple layers of robust encryption algorithms, followed by LZMA, which aims to compress data size while ensuring data security. The proposed approach is well-suited for real-time implementation and delivers high encryption quality and compression capabilities. Various performance metrics evaluate its performance, including space-saving percentage, processing time, and the NIST randomness test. It provides a substantial improvement in space-saving percentage from 58.63% to 81.8%, ensuring efficiency. The security analysis confirms that ciphertexts generated by this approach pass all NIST tests, generating a 99% confidence level regarding the randomness of the ciphertext. The proposed hybrid approach offers an effective solution for addressing the challenges of securing sensitive data while taking advantage of the benefits of cloud computing.

introduction

Cloud computing has become an increasingly popular way in the networking and computer science field. It has given small and medium-scale businesses with a cost-saving opportunity to avoid the expense of purchasing hardware resources. It gives us an equal opportunity to shine as it enables users with access to a powerful computing resource without having to invest in expensive hardware or software. Cloud computing operates on the principle of virtualization, wherein a single large machine is utilized by multiple clients with a view that they have their dedicated resources. Due to the significant benefits of managing resources in unlimited storage in the most inexpensive way, business continuity, and scalability, cloud computing has had rapid growth in the IT industry, therefore, this technology has emerged as one of the most powerful innovations that has attracted the interest of technologists globally (Sajay, 2019). Cloud computing services can be classified into three primary categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). SaaS provides software packages, system software, and application-related server storage networks to end users such as Gmail. PaaS is used by application developers to create applications that are hosted in the cloud such as Google App Engine. IaaS deals with hardware services, virtual machines, and network architecture infrastructure such as Microsoft Azure (Mahesh et al., 2023). The deployment models include private cloud, public cloud, community cloud and hybrid cloud. Public clouds are available to everyone while private clouds are used by organizations. Community clouds are shared by multiple organizations and hybrid clouds combine both private and public clouds (El-Booz and Attiya, 2017, Thabit et al., 2021).

Conclusion and futurework

This research utilized a multi-layered security approach with compression to secure and compress text data streams while preserving the original data sequence without any changes. This research focused on developing a secure and efficient way of storing and transferring data streams in the cloud computing environment by incorporating existing encryption and data compression techniques, including symmetric key encryption techniques Rc4, AES, and the substitution cipher Vigenère, while AES is a more advanced encryption standard. Combining these techniques provided a more robust and comprehensive solution for data security while maintaining low storage of text data through LZMA compression. The proposed approach consists of seven stages in the encoding phase (sender Side) Initial Encryption, Decomposition, Substitution, Data size treatment, Compression, Encryption2, and Uploading Encrypted-compressed data in the cloud and the inverse on the (receiver side). Experimental results proved that the proposed approach achieved faster processing time compared to performing the encryption and compression techniques as separate steps. Security analysis demonstrated that the proposed approach for generating ciphertexts passed all NIST tests with a high level of confidence (99 %) in terms of ciphertext randomness. Additionally, compression efficiency analysis demonstrates that the proposed approach produced a significantly higher range of space-saving percentages (58.63 % to 81.8 %) compared to standard techniques while also providing adequate security.

Abstract

Quantum computing is a new paradigm that will revolutionize various areas of computing, especially cloud computing. Quantum computing, still in its infancy, is a costly technology that can operate in highly isolated environments because of its rapid response to environmental factors. This makes quantum computing a challenging technology for researchers to access. These problems can be solved by integrating quantum computing into an isolated remote server, such as a cloud, and making it available to users. Furthermore, experts predict that quantum computing, with its ability to swiftly resolve complex and computationally intensive operations, will offer significant benefits in systems that process large amounts of data, like cloud computing. This article presents the vision and challenges for the quantum cloud computing paradigm that will emerge with the integration of quantum and cloud computing. Next, we present the advantages of quantum computing over classical computing applications. We analyze the effects of quantum computing on cloud systems, such as cost, security, and scalability. Besides all of these advantages, we highlight research gaps in quantum cloud computing, such as qubit stability and efficient resource allocation. This article identifies the advantages and challenges of quantum cloud computing for future research, highlighting research gaps.

Introduction

The concept of quantum first entered the literature when it was discovered by atomic physicists in the early 1900s (Heim et al., 2020). The idea that a computer could be produced using quantum mechanics was first expressed by Richard Feynman in 1981 (Kandala et al., 2017). Since it was very difficult to maintain the stability of qubits operating based on quantum superposition and quantum entanglement, the first quantum computer prototypes began to emerge in the early 2000s only with the efforts of pioneering companies such as IBM (Singh and Bhangu, 2023). Quantum computers are still in the development phase, and it is predicted that they can perform a task in as little as 200 s, which would take 10,000 years for the world’s best supercomputer (Sasaki et al., 2014). For this reason, complex and long-lasting processes such as the behavior of molecules can be carried out very quickly with quantum-based computers (von Lilienfeld et al., 2020). In addition, it is obvious that quantum computing will be one of the technologies that will shape the future as it begins to enter the military, civil, and commercial fields (Nahar et al., 2023).

In addition to the advantages they offer, quantum computers require a highly insulated environment because they are sensitive to external factors such as heat, temperature, and noise (Bernstein and Lange, 2017). For this reason, quantum computers are still an expensive and difficult-to-stabilize technology for end users. The idea of researchers being able to access quantum computer resources by integrating quantum computers with cloud computing was put forward as an extremely bright idea Ma et al. (2022). In this way, quantum computers can be placed in a highly isolated data center and provide service to the endpoints of the server. This model is called quantum cloud computing and a platform service can be offered for researchers where they can apply quantum applications and algorithms (Lou et al., 2024).

Conclusions

Quantum computing is a new paradigm that is still in its infancy but has the potential to revolutionize many scientific and technological fields in the future. Due to quantum mechanics, quantum computers operate in well-insulated environments, making them susceptible to environmental factors like heat, temperature, and noise. As a result, it is still a difficult technology for quantum researchers to reach. By integrating quantum computing into cloud platforms, a well-isolated server can provide service to the network’s edge, reducing costs. This brilliant idea may be possible with quantum cloud computing, which combines quantum computing and cloud computing. This article aims to provide guidance for researchers by highlighting the quantum cloud computing paradigm, future application areas, advantages, challenges, and research gaps.

Abstract

Cloud computing has completely changed IT (information technology) by providing IT resources as services on the internet. However, certain types of attacks, such as interdependency attacks, impede its wide adoption. With the latter, an attacker who succeeds in compromising the VM of a user can traverse the hypervisor to launch an attack on the VM(s) of other users on the same hypervisor. Unfortunately, we note a lack of secure and performant allocation policies against this problem. Existing policies focus on security but ignore other factors, including workload balance and energy consumption, which are vital for commercial cloud platforms. In this context, we propose different allocation policies for choosing the datacenter server to which we allocate a new virtual machine. These policies aim to minimize the interdependence of different users’ VMs while keeping the system performant regarding workload balance and/or power consumption. By default, our allocation policies treat all legitimate users as attackers and host their virtual machines according to their efficiency and coverage. We first design a secure and balanced solution that increases workload balance to prevent the servers from being overused. Afterward, we propose an algorithm that addresses security, power consumption, and workload balance objectives simultaneously. Based on our simulation results, our solutions perform better than existing algorithms regarding security, workload balance, and power consumption. The balanced solution reduces the chance of an attacker to zero and increases workload balance linearly. In other words, the workload balance is between [5,35] , and it utilizes slightly more hosts than existing proposals, with gains between [2,8] . Although our final proposal is less secure than previous algorithms, it performs better, so it has a good workload balance ( [5,30] ) and consumes less energy.

Introduction

Cloud computing is one of the most remarkable advances in IT in the last two decades. It offers resource consumption on demand, a flexible environment, and easy to use. These facilities make it widely adopted by the customers. However, in cloud computing, the hypervisor allows multiple virtual machines (VMs) of different users to run simultaneously on the same physical server. Ideally, each of these users’ virtual machines should operate in isolation to maintain optimal security conditions. Unfortunately, perfect logical isolation has not been achieved in practice, leaving attackers with the possibility of launching attacks such as interdependency attacks, etc. [1], [2], [3], [4], [5], [6], [7]. With the interdependency attack, a malicious user who has compromised the VM(s) of a user i can traverse the hypervisor to launch an attack on the VMs of another user j≠i on the same hypervisor.

Hence, to tackle this issue, most of the proposals tried to satisfy security and/or performance constraints by using optimization methods such as heuristic algorithms [8], [9], [10], game theory approaches [3], [11], [12], [13], the multi-objectives optimization [14], [15], [16] since the problem is NP-hard [14]. However, we note the absence of a secure and performant virtual machine allocation technique against the interdependency problem. For instance, in [16], the authors proposed a secure solution against the interdependency attack that minimizes both attacker’s efficiency and coverage, which respectively represent the probability of success of the attacker and the probability that the virtual machine of a legitimate user will be compromised. Nevertheless, this solution overlooked essential performance constraints related to minimizing power consumption and maximizing workload balance in the datacenter. These two performance constraints are very important for commercial cloud platforms. The first one motivates a provider to allocate a lot of VMs to fewer servers to reduce the cost of energy consumption and the emission of carbon dioxide (CO2). Significant energy consumption leads to high energy costs among providers. On the other hand, maximizing the workload balance spreads users’ virtual machines among the servers to prevent the hosts from being over-utilized. To accommodate these two performance constraints essential for commercial cloud platforms, we propose extending [16]. We address the interdependency problem and the interdependency attack interchangeably throughout the paper. We also refer to the performance by workload balance and power consumption.

Conclusion

In this paper, we develop the first secure and performant solution against the interdependency attack between cloud users sharing the same hypervisor. It focuses on minimizing security metrics while considering power consumption and workload balance. This approach considers all legitimate users as attackers who attempt to hack the host’s hypervisor and gain unauthorized privileges on the VMs it contains. Specifically, we define a secure allocation policy that maximizes workload balance (SALAEC-B) and a secure and performant allocation policy that simultaneously optimizes security, workload balance, and power consumption (SPALAEC). We also show that these solutions are optimal with polynomial complexities synonymous with “feasible” and “efficient”. In addition, results from the simulation show that SALAEC-B is secure and balanced, and it performs better than its counterpart in the related work, PSSF-Balanced [14]. Finally, SPALAEC is also secure against the interdependency attack while being efficient regarding workload balance and power consumption. Furthermore, our VM allocation policies prevent the negative impact that can be caused by the failure of one of the servers, unlike in PSSF-LEAST [15] and PSSF-Balanced [14]. Our allocation policies do not consider the possibility of migrating a virtual machine from one server to another. We propose an energy-aware approach with virtual machine migration as future work to deal with the high energy consumption in cloud computing and service level agreements (SLAs) violations. In addition, during allocation, our algorithms use the amount of security investment, which does not change over time (i). Our future work may investigate (i) and other simulation scenarios with many users.

Abstract

Live migration of virtual machines (VMs) is a technique that moves active VMs between different physical hosts without losing any running states. Although it is desirable for administrators that the live migration is completed as quickly as possible, the pre-copy-based live migration, widely used in modern hypervisors, does not satisfy this demand on the current trend that VMs on which running applications are performance-critical such as database management systems (DBMSes) have quite large memory. DMigrate , presented in this paper, shortens the time for live-migrating VMs with even large memory DBMSes. To quickly produce the running state of the migrating VMs on the destination, DMigrate performs regular memory transfers while simultaneously constructing the DBMS’s buffer-pool by fetching the data items from the shared storage. We prototyped DMigrate on MySQL 5.7.30, QEMU 5.1.0, and Linux 4.18.20. The experimental results show that the migration time of the prototype is up to 1.71 × and 1.71 × shorter under workloads, including sysbench and TPC-C, than the default pre-copy and post-copy schemes, respectively.

Introduction

Live migration of virtual machines (VMs) is a technique that moves active VMs between different physical hosts without losing any running states. Within a local area network (LAN), the main task of the live migration is to transfer VM memory pages to the destination while the VM disks are assumed to be stored in the shared storage. Live migration is helpful to intra-datacenter administrations including load balancing [1], [2], power saving [3], [4], and effective software upgrades [5], [6]. It is used in real-world data centers [7].

Although it is desirable for administrators that the live migration is completed as quickly as possible, the pre-copy-based live migration [8], widely used in modern hypervisors [9], [10], [11], does not satisfy this demand on the current trend that VMs on which memory-intensive applications such as database management systems (DBMSes) are running have quite large memory. For example, Amazon RDS offers VMs whose memory sizes are up to 3904 GiB [12]. Pre-copy transfers updated pages of a VM from the source to destination iteratively until the number of dirty pages is below a threshold, suspends the VM, and then passes the control to the destination after sending all dirty pages and CPU states such as register values. The migration of such VMs requires a long time due to the large number of memory page transfers. In addition, a larger VM memory size makes each iteration longer and could result in the VM spawning dirty pages that have to be transferred in the next iteration.

Conclusion

The live migration of DBMS-running VMs is non-trivial due to their tremendously large memory footprint, disturbing live migration-based administration in datacenters. This paper presented DMigrate that shortens the time for migrating DBMS-running VMs. To produce the running state of the migrating VMs on the destination, DMigrate performs regular memory transfers while simultaneously constructing the DBMS’s buffer-pool by fetching the data items from the shared storage. We prototyped DMigrate and conducted several experiments. The experimental results show that our prototype successfully shortens migration times of the pre-copy and post-copy schemes, and the prototype is effective under our synthetic migration scenarios, namely, VM eviction, batch migration, and migration in congestion.

Abstract

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.

Introduction

Cloud services that may be accessible online are how computational hardware and software resources are packaged in cloud computing. There are three different types of cloud computing applications that have been developed: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [ [1] , [2] , [3] ]. In order to optimize [ [4] , [5] , [6] ] streamline operations and deliver acceptable levels of speed for the consumers, handling massive data sets necessitates a number of strategies. By maintaining effective management of cloud resources, it is possible to accomplish the efficient and scalable properties of cloud computing [ 7 ]. Users might cite better utilisation of distributed resources and their application to increase throughput [ 8 ], performance [ 9 ], and problem-solving at a big scale [ 10 ] as the primary objectives of cloud computing (see Fig. 2 , Fig. 3 , Fig. 4 , Fig. 5 , Fig. 6 , Fig. 7 ).

The chances of failures that could simultaneously harm the services in cloud systems are reduced by the use of load balancing [ 11 ] and redundant mirrored databases in cluster techniques [ 12 ], which span several availability zones. The load balancer can move to another resource if one system has an outage [ 13 ]. By maximising resource availability and minimising the amount of downtime experienced by organisations during outages, load balancing techniques among the circumstances towards cloud computing help to lower expenses related to document management systems [ 14 , 15 ].

Conclusion

Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards to the load balancer, there it perform the Bi-LSTM technique by using Genetic Expression Programming (GEP) optimization algorithm and the collected schedules sent to Virtual Machines (VMs) and finally it balances the input loads in VMs, When the load was unbalanced, VM migration was done. According to the results, better prediction is provided by Bi-LSTM based modelling than by traditional LSTM based models because it is based on extra data training, provides better prediction than conventional LSTM based models. In load balancing, the proposed Bi-LSTM and GEP optimization are performed, the Bi-LSTM is the optimized by GEP optimizer. The optimized cloud resources are goes to VMs which performs in cloud based and there the VMs balances the tasks. The proposed method has been evaluated in terms of MVM, PLBVM, VMIS and the proposed Load Balancing of Virtual Machine (LBVM). The proposed method reduces the Migration Time of 49%, 41.7%, 17.8% than MVM, PLBVM, VMIS existing techniques. Because cloud computing offers virtualization, scalability, infinite computational resources, and the capacity to store massive volumes of both organized and unstructured data, it is perfect for deep learning. We intend to enhance this type of load balancing for buildings that have dependent activities in the future.

Abstract

     Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer’s credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.

Introduction

     Cloud computing is a new way of outsourcing IT services as an online service. Although varying to the field of distributed, cluster or grid computing the use of the Internet in cloud computing separates it from the cluster and the grid computation. As per the terminology of cloud computing implies, the word cloud refers to the Internet that rawhides the basic enigmas of cloud computing users and conveys the seamless thinking of accepting Information Communication Technology (ICT) computation facilities from the cloud service provider (CSP). The domain of cloud computing includes three components namely Cloud Infrastructure, Cloud Provider and Cloud Consumer. Cloud computing services are used in other forms of conveyance such as Software as a Service, Platform as a Service and infrastructure as a service. According the definition of SaaS, code and databases furnished to several clients. In PaaS, software development platform is provided. IaaS allows the consumer to produce a complete and fully functional virtual machine with network capability, data storage devices, CPU or GPU processing, run time memory and almost all other resources needed for integration.

     Cloud services can be used likewise we use utilities in our daily life i.e. pay what you use. IaaS model is a fundamental layer for the cloud environment. At this layer computing resources are designed for virtualization purposes, in order to achieve widespread distribution and deployment. Due to high deployment, there is a high risk of security attacks and it is very difficult for a cloud buyer to rely on such a multitenant site. Cloud computing and IaaS security issues such as data security and privacy, trust, multiple tenants, damage, and data leaks, malicious VM etc are shown in [2] and [3]. Caring these issues it is very difficult for the consumer to rely on the infrastructure provided to the consumer. User stores data in bought machines (VM) swung on the provider facet for processing and other purposes. It is important to examine the cloud equipment for privateness and integrity before, after and at some stage in VM implementation. The literature reviewed for this motive conveys the clean impact that there may be a loss of such structures that permit the user to discover a VM controlled with integrity and privateness. In the IaaS framework all environmental safety measures are done via way of means of the provider, so the user ought to depend now no longer best on his or her infrastructure however additionally on his or her personal countermeasures

Conclusion

     Cloud computing is an internet-centric technology for outsourcing IT services such as software, platform, and infrastructure. In this research work accessibility model for the guest VM is proposed. In the proposed model integrity values of guest VM are shared and synchronized with more than one authenticated client devices to access the guest VM from everywhere and anywhere.

Abstract

     Today, industry 4.0 is becoming a major target for cybercriminals due to its hyper-connectivity. Fortunately, there are several advanced means of securing industrial systems such as Intrusion Detection Systems (IDS). However, one of the main limitations of industrial IDS is the high rate of false positives and how to distinguish a real attack from an industrial failure. This paper deals precisely with the two latter points and proposes a way to reduce the rate of false positives and to distinguish attacks from industrial failures. The proposed approach combines two kinds of IDS using Neural Network (NN) through a Decision Making System (DMS). It was tested on a real industrial environment. The performance results are promising with a high percentage of accuracy and a low false positive rate.

Introduction

     Nowadays, Industrial Control Systems (ICS) exist in many different industrial sectors such as meatpacking, chemistry, construction, automotive, electronics industry. But also in vital industrial sectors such as energy, health, military and food sectors. Therefore, the suspension or the stopping of these systems could be costly for industrialists and cause consequent damage. Today, securing such equipment becomes more than necessary. Over the past decade, industry has become the center of attackers’ focus and has been the victim of several attacks starting with Stuxnet, Black Energy, WannaCry. This wave of attacks has been succeeded by several ransomware attacks in 2020 during the coronavirus pandemic, especially with the increase in the number of remote workers and a lack of security in this new working model [1]. Kaspersky’s ICS CERT researchers forecast a list of attacks likely to target industries in 2023 [2]. Among these attacks are phishing pages and emails, Torjans, N-day vulnerabilities, attacks on cloud services, exploiting vulnerabilities in legitimate software, the spread of malware via removable media …

This cyber-criminality phenomenon is favored with the emergence of the industry 4.0. This 4th industrial revolution is characterized by the convergence of the worlds of Information Technology (IT) and Operation Technology (OT), the huge amount of generated data, the use of Cloud as new storage means. All these reasons increase the risk of cyber attacks in industry.

Fortunately, there are several solutions to secure the industry and its equipment. Among these mechanisms, we mention firewalls, anti-virus, auditing processes and IDS. Each of these securing mechanisms has a specific role such as detecting and removing malware, preventing unauthorized access or detecting intrusions by IDS. These latter give visibility to the system’s activities, which allows a timely detection and response to any suspicious events [3], [4].

Conclusions

     BIANO-IDS is a new intrusion detection approach combining two kinds of IDS: anomaly-based IDS and specification-based IDS. The approach has been tested on a real environment and the detection accuracy rate is high in addition to the different performance metrics. However, to further improve performance metrics, we intend to explore other traces such as system logs and apply reduction or selection features methods in the future to improve the computation and training time of neural networks.

Abstract

     Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by classifying the activities as normal and suspicious. In this paper, we propose a two-phase IDS for IoT. In the first phase, we categorize data into four sections according to the data types (i.e., nominal, integer, binary, and float). We then classify them using different versions of the Naive Bayes classifier. After that, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which behave normally or are benign in the first phase and classify them using an unsupervised elliptic envelope. We validated our work using the standard NSL-KDD, UNSW_NB15, and CIC-IDS2017 datasets. We found the proposed method more efficient than existing IDS techniques and achieved reasonable accuracy in the first phase. Furthermore, the benign data is sent to the second phase of the analysis. After the second phase, we achieved a 97% accuracy in the NSL-KDD dataset, 86.9% in the UNSW_NB15 dataset, and 98.59% accuracy in the CIC-IDS2017 dataset.

Introduction

     In the evolutionary era, the Internet has always been performing a most significant role. Globally, the total estimate of Internet users is projected to increase from 3.9 billion in 2018 to 5.3 billion by 2023, as stated by Cisco Annual Internet Report [1]. Furthermore, the Internet of Things (IoT) is becoming increasingly widespread. IoT integrates many heterogeneous objects (such as in a smart home: intelligent bulbs, refrigerators, fans, air conditioners, automated doors, and TVs.) with various connecting technologies such as Bluetooth Low Energy (BLE), WiFi, and ZigBee. There are also other domains and applications in which the IoT can play an important role and enhance our lives quality. These applications include smart transportation, industrial automation, agriculture, and healthcare [2].

     The IoT model [3] has been emerging towards formulating a cyber–physical environment where everything can be found, operated, investigated, and modernized. Because of being connected, the chances of attacks on the network increase. Many attacks and malicious incidents can affect different layers of the IoT architecture, creating security concerns. Makhdoom et al. [4] discussed the commonly known attacks on different layers, depending on the anatomy of the malware, and IoT-enabled cyber-attacks are also illustrated in a survey [5]. Similarly, Zarpel et al. [6] elaborated on intrusion detection systems in IoT. They have classified IDS based on placement strategies, detection methods, security threats, and validation strategies. Zargar et al. [7] explained in detail about Distributed Denial of Service (DDoS) attacks and also classified the countermeasures.

Conclusion

     This paper presents a machine learning-based two-phase IDS. Firstly, we categorize data into four sections according to the data types (e.g., nominal, integer, binary, and float). Then classify them using different versions of the Naive Bayes classifier. After that, with the help of majority voting, we choose the final result of the classification. In the second phase, we pass those data which behave like normal in the first stage, and these data are classified using an unsupervised elliptic envelope. It draws an imaginary envelope and assigns value 1, which lies inside the Envelope, and −1 outside the Envelope. Our proposed model is also performing very well in the imbalanced distribution of the data by providing the weight initialization to each class. Finally, we got an overall 97% accuracy with a meager false positive rate. The drawback of this model is that it does not work pretty in multiclass classification.

     In future work, we will improve the multiclass classification and feature engineering techniques model, expand this procedure in real-time for network traffic analysis, and evaluate performance. We will further attempt to capture network data by deploying IoT devices in the real world. Moreover, the IDS system will detect malicious incidents in real-time and immediately take appropriate action to prevent damage.

Abstract

The use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more people are coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observed pattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection.

Introduction

Over the past few years, there has been an increase in the usage of computer systems to make the lives of consumers easier and more convenient. When people try to takeadvantage of the amazing capabilities and processing capacity of computer systems, however, security has been one of the most significant problems in the field of computer science. This is because attackers frequently try to break into systems and act maliciously, such as stealing vital information from a corporation, rendering the systems useless, or even destroying the systems. Internal attacks, such as pharming, distributed denial-of-service (DDoS), eavesdropping, and spear-phishing attempts, are often among the most difficult to identify of all well-known attacks. This is due to the fact that firewalls and intrusion detection systems (also known as IDSs) often guard against attacks from the outside. At this time, the majority of systems authenticate users by analysing a login pattern consisting of the user ID and password. As a result of this, we have proposed in this study a security solution that we have dubbed the Internal Intrusion Detection and Protection System (IIDPS) [1]. This solution recognises hostile or malicious behaviour carried out against a system at the System call level. IIDPS uses data mining and forensic profiling techniques in order to mine system call patterns, also known as SC-patterns, which are the longest system call sequences (SC-sequences) that have repeatedly appeared numerous times in a user’s log file for the user. SC-patterns can be used to identify malicious activity. The user’s computer usage history is used to compile the user’s forensic features, which are then defined as a SCpattern that commonly appears in the user’s own submitted SCsequences but is rarely utilised by other users. This information is gleaned from the user’s computer.

Conclusion

As a consequence of this, within this work, we suggest asecurity system that we refer to as the Internal Intrusion Detection and Protection System (IIDPS). This system is capable of identifying hostile behaviour that is aimed towardsa system at the SC level. The IIDPS minessystem call patterns, also known as SC-patterns, which are defined as the longest system call sequences (System Call-sequences) that have repeatedly appeared numerous times in a user’s log file for the user. These are the operations carried out by the user, such as sending a file, updating a file, or viewing a file, and they are validated by an administrator. The user’s computer usage history is used to compile the user’s forensic features, which are then defined as a SC-pattern that commonly appearsin the user’s own submitted SC-sequences but is rarely utilised by other users. This information is gleaned from the user’s computer.

Abstract

     Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results.

Introduction

     The assessment of various financial aspects of banks occupies an essential place in the academic literature because of the crucial intermediation role of the banking industry in financial markets (Ioannidis et al., 2010; Tzeremes, 2015; Zopounidis et al., 2015). Along with an increasing need to use more sophisticated methods in banking research, several studies in this area employ operational research (OR) and artificial intelligence (AI) methods. Thus, the existing literature examines some fundamental research questions in banking research using OR and AI techniques, such as addressing the fairness issue in banking performance evaluation (Chen et al., 2020) and increasing the accuracy of the prediction of default risk and bank failure (Boussemart et al., 2019), as well as helping centralized organizations (e.g., headquarters of banks) to incentivize their units (i.e., bank branches) and optimize their performance (Afsharian et al., 2019). A rising trend in the utilization of OR and AI techniques to address banking challenges indicates their increasing importance and relevance for this field (Akkoç, 2012; Manthoulis et al., 2020; Yao et al., 2017).

Conclusion

     This article presented an extensive review of the crucial role played by OR and AI methods in banking research by analyzing a total of 338 studies published between 2010 and 2020. We described six general topics that employ OR and AI methods to address various crucial banking issues: banking efficiency, risk management, bank performance, banking regulation, M&A, customer-based studies, and fintech in the banking industry. We also outlined the most widely used OR methods, including DEA, ABM, MC, fuzzy logic, and AI techniques, including SVMs, NNs, and ensemble methods. This article contributes to the literature by complementing the prior bibliographic surveys, covering various general banking topics, and summarizing the different methods applied.

     We also suggested potential future research directions from both topic and methodology perspectives. Researchers could explore and verify various OR and AI methods in banking studies from a methodological perspective. Thus, regarding future research topics, efficiency forecasting related to the evaluation of financial stability could justify further exploration, as could the investigation of non-financial risks, such as conducting risks, which has received very limited attention in the academic literature to date. Future studies might also explore the impacts of government regulations and managerial behaviors on risk-taking by banks. Finally, future research could also apply other AI methods (e.g., unsupervised machine learning) or fresh combinations of OR and AI techniques to banking research.

Abstract

     This paper surveys the published work on how blockchain technology will impact accounting in general, but AI-enabled auditing specifically. The purpose is to investigate how blockchain technology can improve transparency and trust in accounting practice and how professionals can use blockchain data to improve decision-making, based on the qualities of immutability, append-only, shared, verified, and agreed-upon (i.e., consensus-driven) blockchain data. The multi-party validation of blockchain protocols adds real-time trusted data for the AI systems used by auditors to improve assurance and efficiency. This review summarizes four themes emerging from the literature focusing on how blockchain technology has changed record-keeping in accounting: event approach to accounting; real-time accounting; triple entry-accounting and continuous auditing. The research interprets the findings using agency theory and stakeholder theory to advance how using blockchain to mitigate information asymmetry and improve stakeholder collaborations is understood. The investigation also summarizes the challenges and clarifies organizations’ reasons to be cautious about adopting blockchain. Lastly, the study suggests that future researchers use this study in two ways that enrich blockchain literature: first, to apply the themes and answer the questions identified within this review to improve the business methods of practitioners and policymakers; and second, to encourage stakeholders such as practitioners, system designers/developers, and policymakers to collaborate in designing blockchain ecosystems that suit accounting and auditing as they transform digitally.

Introduction

     Making companies’ systems digital has enabled them to adopt new technological tools to simplify business processes and transform business models to innovate their operations (Gomber et al., 2018) because they can increasingly access advanced computing power and large databases (de Sousa et al., 2019). Today, the world’s most valuable businesses are Internet-driven and platform-based (Iansiti and Lakhani, 2017). Academics, social media, industries, and governments spend much time attending to the digital forms of technology: blockchain, artificial intelligence (AI), big data, the Internet of things (loT), and cloud computing. These innovations change organizations and individuals greatly (Benlian et al., 2018), with blockchain specifically providing the foundation for what Tapscott and Euchner (2019) call an Internet of value that will fundamentally reshape society and its business. Blockchain, now regarded to be the fifth pillar of the IT revolution (Thakkar, 2019) is expected to become the foundational technology as the next-generation Internet (Iansiti and Lakhani, 2017, Shermin, 2017). Since Nakamoto (2008) set the groundwork for what would become blockchain technology in 2008, the banking, financial, insurance, education, health care, and government sectors have been using blockchain technology to the extent that 10 % of the global GDP will be recorded and stored on blockchain by 2027 (World Economic Forum, 2015). PricewaterhouseCoopers (PwC) (2020) estimates blockchain could boost global GDP by 1.76 trillion US dollars by 2030. Deloitte’s, 2020 global blockchain survey indicates that organizations are more committed than ever to implementing blockchain in their business (Deloitte, 2020). With blockchain’s growing maturity, innovators are discovering new opportunities to create value and enhance trust and resilience to digital transformation by combining blockchain with other technology forms, notably, AI, IoT or cloud computing (Cuomo, 2020).

Conclusion and future research

     This study reviews and summarizes four themes marking the changes in recordkeeping in accounting with blockchain technology. This technology can provide shared, verified, and agreed-upon (i.e., consensus-driven) auditable data. Auditing can enhance audit effectiveness with AI tools by using traceable and auditable blockchain data. This review interprets the results using agency and stakeholder theories to explain how blockchain-enabled accounting can avoid information asymmetry and include all stakeholders because blockchain offers new ways of organizing collaboration. However, it is a new, evolving technology that will challenge organizations faced with possible risks from embracing blockchain in accounting. Therefore, more research is needed to explore more cases of using blockchain-enabled accounting. Lastly, this study suggests some questions that future research could seek to answer and thus broaden blockchain literature with empirical research. They could ask: What types of accounting transactions can be recorded on a blockchain, and at what cost? How can blockchain data be synchronized in AI-enabled auditing? What are the data standards that blockchain and AI can reach? How can businesses govern blockchain-based accounting information systems? How can regulations be adjusted to guide and support innovation in blockchain-based accounting information systems and AI-enabled auditing? What different responses and challenges face large accounting and auditing practices and SMPs from adopting blockchain? Blockchain needs to be developed, standardized, and improved to overcome technical, organizational, and regulatory challenges to become truly an integral part of the financial system.

Abstract

Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading, where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Further, we characterize the performance gap between cost under our proposed distributed algorithm and the minimum cost in terms of Price of Anarchy (PoA) when the cost of using cloud servers is high. Finally, we perform extensive simulations to validate our theoretical findings, demonstrate the efficiency of our proposed distributed algorithm under various scenarios such as hyperexponential service times, imperfect server utilization estimation, and asynchronous threshold updates, and reveal the superior performance of threshold-based policies over their probabilistic counterpart.