Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

The sixth generation (6G) of cellular transmission is extremely adaptable. 6G will equip everyone with pervasive wireless connectivity and meet the needs of a fully connected world. It is anticipated that revolutionary ideas will increase in support for a fast-expanding variety of smart devices and applications. The sophisticated characteristics of 5G portable cellular network infrastructures produce novel threats and requirements. Comparing 6G cellular network technologies to 5G cellular networks, this paper discusses various security and privacy issues in 6G cellular networks based numerous security services. Proposes AA approach to evaluate the effectiveness of the methodology. Propose a new 6G wireless security architecture based on the analysis of secret key authentication and flexible position based identification, which serves as the foundation for an examination of identity management and flexible authentication. Demonstrates the benefits of the proposed architecture, analyzes BER against SINR and Measure Throughput Against various SINR values. Finally, Limitations and future recommendation for 6G cellular network security is presented.

Introduction

Over the past few years, wireless networks technologies have made significant technological advancement. The revolutionary changes brought about by the most recent 5G innovations have benefited a wide range of stakeholders, including commercial solution suppliers, university research organizations, standards bodies, and mobile-users [1,2]. In contrast, networks comprise a large number of Base Stations (BS) to enable high data transmission rates and adequate network capacity due to the low penetrability of high-frequency signals used in 5G [3,4]. The foundation of mobile network service continuity is multi-hop network technology, which allows users to seamlessly switch between source to destination that are outfitted with various access methods. The complexity of device changeover in 5G networks grows as a result of the diversity of service networks, increasing transmission times and affecting user experiences. As opposed to 5G networks, high-density access networks have a high transmission rate among tiny nodes. Existing wireless networks have been struggling to handle an increase in traffic due to the constant growth of data and devices in cellular networks [5,6]. In response, the 6G of cellular communication technologies has arisen to satisfy these high expectations. With many different communication networks, including Long Term Evolution, the Internet of Things, Mobile multi-hop Networks, wireless personal area networks and several versions of Wi-Fi, 6G is incredibly adaptable [7,8].

The 6G communication network is a network with several additional capabilities in addition to being an upgrade of the existing 5G network [9,10]. Research and development for 6G aims to provide a variety of advanced qualities, including greater bandwidth than existing 5G networks, higher mobile internet connection facilitating communication between devices among users as well as extensive technological communications [11,12]. Planned 6G networks also seeks to reduce energy consumption, latency and enhance security, for better wireless network deployment [13,14]. Additionally, a rise in connection density will lead to demands for higher energy efficiency, which 5G is not intended to provide. As a result, the research community has focused on solving the aforementioned key problems [15,16]. Accordingly, we assert that current work in the fields of terahertz band communications, intelligent surfaces and environments, and network automation. The general architecture of 6G wireless systems is shown in Fig. 1.

Conclusion

Cellular networks have been successful because of their emphasis on secure transmission. When everyone’s access to the internet is ensured in 6G, the networks will grow to enormous sizes. We have presented a thorough analysis of current advancements in 6G wireless security in this paper. We proposed a 6G wireless security architecture based on existing research. From the suggested security framework, an analysis of position based identification and adaptable authentication based on different security architectures has been provided. Demonstrated the benefits of the proposed architecture, Analyzed the performance of BER at the User Interface is 94 % improved than the CoMP, GKM and OSPR. Furthermore, Enhanced Throughput 96 % achieved for AA Approach due to integration of data plane and control plane in the proposed architecture. The efficacy of this class of solution is verified by using Riverbed Modeler 17.5 simulation tool.

Abstract

Network security is the main content of network management, but in the process of network security management, it is vulnerable to hacker intrusion and communication interference, which reduces the level of network security, and causes the loss of crucial communication data and the wrong results of security assessment. Based on this, this paper proposes a machine learning method to machine study network communication data, enhance network communication data, and shorten network communication time. The network communication data is then analyzed by messages. Finally, the machine learning method is used to judge the security of communication data and output the final security assessment results. The results show that the machine learning method can accurately carry out network security analysis, reduce the interference of hackers and communications, and the security level is greater than 9 to 5%, which is better than the online security monitoring method. Therefore, the machine learning method can meet network security requirements and is suitable for network management.

Introduction

Some scholars believe that network security is a comprehensive analysis process of network management [1], and it is necessary to conduct a comprehensive analysis of network communication data and encrypted data. It is prone to problems such as wrong transmission and incomplete network communication data [2]. Currently, network security often has the problems of low-security level [3], long security judgment time, and redundancy of network communication data of different servers in network management [4]. Therefore, some scholars propose to apply intelligent algorithms to network security judgment, identify interference factors such as hackers and communications, and analyze convex analysis to assess network security [5]. However, the data transmission between the central server and the local server is still not ideal [6], and there is a problem of lowsecurity level. To this end, some scholars have proposed a machine science method through the convex analysis of critical values, identifying the more significant probability and prominent values in the key values [7], and conducting approximate analysis of key network communication data to achieve practical judgment of network security [8]. Therefore, based on the machine method, this paper analyzes the key values in network management and verifies the safety judgment results of the method.

Conclusion

For network security machine learning cannot accurately perform network security. Based on this, this paper proposes a machine learning method to set data for image and approximation analysis and uses an approximation to analyze the feature value of safety judgment. Network communication messages and network communication analysis are carried out through machine learning to reduce the mapping of hackers and industries in security judgment. The characteristic value is used as the node for safety judgment analysis to realize the safety judgment of critical values. The results show that the completeness and safety level of the machine learning method is greater than 90%, and there is no significant difference in the changes of image, text and audio, but the difference of the online safety monitoring method is more significant. In the machine method, the time of network security is relatively short, and it is not affected by the prediction level of images, proximity analysis and industry, and security judgment. In contrast, in the machine learning method. The safety judgment time is relatively long, and the time variation range is 6.45~11.57. Therefore, the machine learning method can meet network security requirements and is better than the machine learning method.

Abstract

Document Sections

I. Overview of the Framework

II. Background Works

III. Proposed Framework

IV. Consensus Algorithm and Flowchart of Proposed Framework

V. Results and Discussion

VI. Conclusion

Abstract:

This manuscript describes since the resources are heterogeneous, a smart city is vulnerable to several security attacks, and to devise an effective response, it is necessary to recognize certain risks and their potential implications. The framework is responsible for the data transfer security issues in the smart city to incorporate and maintain physical, social, and industrial systems to provide high-quality secure data transmission to its residents. The proposed method of integrating the Blockchain and Internet of Things (IoT) with a consensus algorithm framework (BCIoT-CAF) to overcome the issues and secure data sharing in smart cities. The data security is enforced by splitting the blockchain network into separate networks, with any channel consisting of a limited number of approved entities, the data stored in the cloud gate server, and analysis of data achieved by IoT applications. The consensus algorithm plays an important role in retaining the blockchain’s speed, protection, and performance. Using an appropriate security algorithm, blockchain applications may greatly improve their performance. The modeling simulation of this research improves the speed, protection, and efficiency of 96% and tolerance error as 55%. The integrated evaluation results suggest that the proposed system integrates blockchain with IoT to provide a shielded communication platform in smart cities.

I Overview of the Framework

Blockchain used to share the data information securely and avoid any other fraudulent data threats[1]. IoT system implementations can be challenging and a transmitting ledger ideal for defining, authenticating, and protected data securely from IoT devices [2],[3]. Blockchain and IoT are two innovations rising in importance since they started. IoT can affect nearly ordinary things shortly. In the increased use of this tool, the risk of violence decreases [4]. To cope with this, existing techniques are not adequate. Blockchain has proven to be an innovative approach to IoT security problems and is a distributed network that relies on a consensus algorithm, ensuring agreement between distributed nodes on the status of some data [5],[6]. The central factor of the consensus algorithm that specifically defines a program operates and its output [7]. Blockchain technology attracting more coverage, revolutionize, automate the digital network of internet-based technologies [8]. This is a shared database that tracks each transaction that takes place on a network[9],[10]. It has a block spread over a node network. The consensus algorithm is a mechanism in which all the blockchain system peers decide on the existing state of the decentralized directory [11]. This ensures that consensus algorithms build stability in the blockchain network and confidence in the distributed computing system among unidentified peers [12]. The consensus protocol guarantees that a new block stored on the blockchain is the valid iteration accepted by all of the blockchain peers [13], [14].

Abstract

Introduction

Section snippets

References (103)

Recommended articles (6)

Abstract

Internet of Things or IoT is a rapidly growing network of interconnected ‘things’ embedded with sensors to collect and exchange data over the internet without the need for human intervention. As the applications of IoT continue to skyrocket, it introduces major security, ethical, privacy, and legal challenges that have a substantial impact on our lives. There is a need for a comprehensive overview covering all of these challenges. Therefore, this paper provides a clear overview of the security, ethical, and privacy challenges faced by the common users and examines the current and emerging IoT laws and standards enacted by governments across different countries to combat the vulnerabilities of IoT. Trust and the potential challenges of smart contracts have also been discussed. In addition, the multitude of use cases described in this paper provides an insight into how the threats and vulnerabilities of IoT influence our lives. This study emphasizes a need for globalized IoT laws and that the common user be made aware of the security, ethical, and privacy threats imposed by modern IoT devices. Lastly, this paper identifies the gaps and proposes certain recommendations to direct future researchers.

Introduction

It was in 1967 when Lawrence G. Roberts was appointed as program manager for the ARPANET project, the development of internetworking protocols began piecing together a single network known as the Internet- a shorthand for internetworking [1]. The 21st century has seen a massive advancement in technology, bringing the world closer through high-speed internet available on our computers and mobile devices. Let it be texting, calling, shopping, or just surfing the web, everything is within our grasp. We have adapted to this technology which is now considered a ‘smart’ ecosystem. A terminology called the ‘Internet of Things’ or IoT, in short makes this possible. With IoT growing as a new business and consumer sector, it exhibits us with immense potential of capabilities.

The concept of IoT was first advised in 1999 by Kevin Ashton, the founder of MIT Auto Identification Centre, 1999. He attributed it to an interconnected network of things connected with RFID i.e., Radio-Frequency-Identification Technology. RFID uses Radio waves and their automation to identify or authenticate objects, collect data, and control the objects. RFID comprises transmitters (tags) and receivers (readers). The tag acts as an identifier that transmits or communicates with the reader through radio waves, thus making RFID imperative for the Internet of Things.

Abstract

Keywords

References

Abstract

At present, wireless sensor networks are developing rapidly with the support of the Internet of things. Wireless sensor networks can deliver the information people need at any time, free from the constraints of time and space. Wireless sensor network is widely used, which lays a solid foundation for the development of Internet of things. As the node deployment environment of wireless sensor networks is usually very complex, it is necessary to study the security of wireless sensor networks so as to reduce security threats and network attacks. In this paper, the security and application of the wireless sensor network are emphasized.

1. Introduction

Wireless Sensor Network (WSN) is perception, and the main tool is sensor. The use of sensors can effectively sense the external environment, and then with the help of wireless network for information transmission, to meet user needs. The security of wireless sensor network must be paid attention to, because it is supported by high technical content and complex structure, once there is a problem, the consequences are unimaginable. The development of the Internet of things is inseparable from wireless sensor networks, and the ability of people to communicate quickly is also closely related to it [1].

WSN can be used to collect all kinds of data and information and deal with all kinds of complex environments, the application of WSN covers all aspects. In the military field, it can be used to detect the deployment of troops in enemy terrain and other situations, and it has the function of detecting biological and chemical pollution and nuclear radiation. In terms of environmental monitoring and protection, it can be used for data collection in the field environment, tracking animal footprints, analyzing pollution situation, and predicting the explosion of forest fire and debris flow. In the field of industry and agriculture, the monitoring of crop growth and product flow intelligent production. In the field of medical care and health, it can realize the function of obtaining physiological data of patients, analyzing their conditions, and timely receiving medical treatment. In addition, wireless sensor network plays a crucial role in the field of smart home, smart transportation, smart city, cultural relic protection and business, etc. Wireless sensor network is changing people’s life [2].

The information security of WSN is concerned in many contexts, such as military medical disaster prevention and other fields. Wireless sensor networks use open wireless communication channel technology to transmit data, but without security protection means, data is very vulnerable to internal and external attacks [3]. However, the amount of computation based on the cryptographic defence method is not suitable for WSN network. Therefore, it is also a big problem to choose an appropriate encryption method to ensure the security of WSN information [4] .

Abstract

Although mobile technologies keep evolving through years, Fault management and cyber-security management in mobile networks are still treated as separated notions with different blocks and different approaches whereas in practice, they are highly correlated. In this paper, we propose a framework that takes into account the correlation between these two management systems. The framework is based on several prediction agents where each agent is composed of a security predictor, a fault predictor and a generic anomaly detection model. A re-enforcement process allows to enhance the reliability of the machine learning training and prediction phases of the different predictors. Besides, each agent can collaborate with its neighborhood for a more resilient network. An application of this framework to 5G architecture is proposed by mapping the components of our framework with network slices. Finally, an experimentation is held over a testbed that we set up on openstack in order to forecast future anomalies related to proxy overload, latency violation in call session network functions and to excessive usage of memory. The training is achieved with ARIMA and deep learning models with promising results.

1 Introduction

As mobile network technologies evolve, new services are offered and more sophisticated networks are needed. The increasing number of Internet users leads to a redesign of network architecture, forcing designers to take into account new parameters such as the need of global coverage combined with low latency, as well as a high reliability and security level. Additionally, new networking experiences are added, such as Internetof-Things (IoT), which promise to offer new services and facilities to people’s daily lives. In this demanding environment, 5G technology is emerging, playing a decisive role in the implementation of new visions and promising to deliver solutions. A major innovation introduced by 5G technology [1] is the scalability. 5G architectures take into account the possible need of extending the capabilities of the network, both at the level of user traffic growth and at the level of new services input from providers. Slicing could be the ideal solution for such networks, offering scalability as well as flexibility in managing a giant network. Network Slicing is set to be a prominent feature of 5G to allow connectivity and data processing tailored to specific customers requirements. Mobile communications provided by smart networks will enhance the efficiency and productivity of business processes and will open up opportunities for network operators to address the Business-to-Business segment more effectively.

Internet of Things (IoT) ideates smart and inter-connected things capable of sharing their perceptions through the Internet. These devices are different from conventional Internetconnected devices in the sense that these are able to perform skill-full things on their own with minimal or no human interaction. Unfortunately, with the advent of amalgamate technologies, security has become a major concern for IoT networks. Recent efforts include re-inventing cryptographic solutions through the use of light-weight operations. However, after witnessing the growth of quantum computers, it can be inferred that the cryptographic techniques based on mathematical problems are not reliable enough. Therefore, there is need to develop solutions that can easily resist the adversarial effects and are suitable for the post-quantum world. In this paper, we perform in-depth analysis over the role of post-quantum cryptographic techniques for securing IoT networks and also explore ongoing research efforts in the field. In addition, we discuss the open research challenges and future research directions in the field.

Introduction

Internet of Things (IoT) is a cluster of technologies that requires different set of protocols, infrastructures, data storage mechanisms, and ways of communication associated with Information Technology (IT). Resource allocation in a smart manner is one of the key requirements while dealing with IoT devices as they are constrained in terms of energy, storage and computational resources. Communication technology for actuating and managing the data is also of great concern [1–3]. As a matter of fact, IoT ecosystem faces a lot of challenges associated with a number of aspects as shown in Fig. 1. This new paradigm of connected objects needs an assimilation of next generation technologies. In order to simultaneously provide ultra-reliability of communication, following technologies can be integrated – • Software Defined Networks (SDN) for managing haptic communications, • 5G technology for providing tactile Internet, and • The new era of distributed Cloud computing, i.e., edge computing for managing the resources. Significant increase in the use of IoT devices is bringing numerous business opportunities. However, manufacturers are still unable to ensure their customers that these devices are secure. Hence, despite all of the advantages, the security issues of these devices are acting as a huge rock in the way of letting this paradigm making a colossal impact on our lives.

As Software-Defined Networking (SDN) is getting popular, its security issue is being magnified as a new controversy, and this trend can be found from recent studies of presenting possible security vulnerabilities in SDN. Understanding the attack surface of SDN is necessary, and it is the starting point to make it more secure. However, most existing studies depend on empirical methods in different environments, and thus they have stopped short of converging on a systematic methodology or developing automated systems to rigorously test for security flaws in SDNs. Therefore, we need to disclose any possible attack scenarios in diverse SDN environments and examine how these attacks operate in those environments. Inspired by the necessity for disclosing the vulnerabilities in diverse SDN operating scenarios, we suggest an SDN penetration tool, DELTA, to regenerate known attack scenarios in diverse test cases. Furthermore, DELTA can even provide a chance of discovering unknown security problems in SDN by employing a fuzzing module. In our evaluation, DELTA successfully reproduced 26 known attack scenarios, across diverse SDN controller environments, and also discovered 9 novel SDN application mislead attacks.

Introduction

Security has been a subject of controversy in many newly emerged networked systems, such as peer-to-peer networks and cloud networks. After their appearance, researchers and practitioners have examined their security issues from various angles to verify their safeness, and this process makes them more secure so that they can be adapted in a real-world system. Software-Defined Networking (SDN), which manages a network in a centralized way, is a recently proposed networking technology, and now it is endorsed by both industry and academia. As SDN technology is getting popular, its security problem is being at issue, and thus researchers are investigating its security issues as they have conducted in other networked systems [26, 46, 39, 20, 25, 1]. Such security-critical reviews of SDNs offer a view into various breaches, but overall, the attack surfaces thus far explored have been quite limited to either highly targeted exploits, such as ARP spoofing or specific vulnerabilities that arise in various SDN components. Each previous result may not be applicable to other SDN environments (e.g., different control planes). Hence, operators seeking to assess security issues in their SDN environments need to survey existing SDN security-related studies and determine relevance on a case-by-case basis. Furthermore, an operator may have to adapt or redesign deployment-specific security test suites. This paper introduces a new SDN security evaluation framework, called DELTA, which can automatically instan- ∗Corresponding author ORCID(s): tiate attack cases against SDN elements across diverse environments, and which may assist in uncovering unknown security problems within an SDN deployment. Motivated by security testing tools in the traditional network security domain [45, 15], DELTA represents the first security assessment tool for SDN environments. Furthermore, we enhanced our tool with a specialized fuzzing module [30] to exploit opportunities for discovering unknown security flaws in SDNs.

The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network. In this paper, we demonstrate the successful use of the tool in two completely diverse operational cyber security environments, namely, (1) security operations center (SOC) for the SCinet network at the SuperComputing (SC) Conference in 2016 and 2017 and (2) Reservoir Labs’ Local Area Network (LAN). In each of these environments, we produce actionable results for cyber security specialists including (but not limited to) (1) finding malicious network traffic involving internal and external attackers using port scans, SSH brute forcing, and NTP amplification attacks, (2) uncovering obfuscated network threats such as data exfiltration using DNS port and using ICMP traffic, and (3) finding network misconfiguration and performance degradation patterns.

Introduction

Network analysis and network threat identification are notoriously difficult problems to solve. Traditional signature-based approaches are often thwarted by the ever-changing nature of modern cyber threats. It is nearly impossible to define signatures for what is or is not normal that generalize across many networks. Even on a given network, expected behaviors might change from day to day. Furthermore, it might not be possible to write coherent rules that capture all activities of concern. The application of cutting-edge data analytics to network traffic logs has struggled to surpass the shortcomings of classical signature-based systems. Supervised techniques run afoul of the same key problem – it is not realistic to specify normal versus abnormal behavior upfront. Other approaches that rely on training a model based on large volumes of historical data are hindered by another issue – because of the sensitive nature of network traffic there is very little publicly-available training data, and that data is not guaranteed to generalize in a meaningful way to the user’s own network. Tensor decompositions are a class of algorithms that provides a new approach for analyzing network traffic data that has been demonstrated to overcome these traditional shortcomings. A tensor is a multidimensional array of data – a suitable abstraction for structured network metadata collected in the form of network logs. A tensor decomposition breaks down a tensor, such as a log, into a finite set of patterns, called components. In this way, tensor decompositions perform a form of unsupervised learning on network traffic that does not require prior training data.