Abstract

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Introduction

DDos attack is a distributed type of attack mode in which an attacker controls a large number of attack machines and sends out DoS attack instructions to the machine. In the latest Internet security report, DDoS attacks remain one of the major cybersecurity threats. The inexpensive pricing and “pay-as-you-go” focused accessibility to computational features and amenities on demand make cloud-based services a formidable competitor to the conventional IT solutions available in prior eras. The use of cloud computing is gaining popularity rapidly. Whether entirely or largely governments and companies have moved their IT infrastructures onto the cloud. Cloud-based Infrastructure offers various advantages compared to traditional, on-site conventional infrastructures. The removal of expenses associated with operation and impairment, as well as the accessibility of materials on request, are only a few of the advantages. However, there are many concerns that cloud consumers have, and the research addresses these issues. The majority of these inquiries centre on safeguarding operational concepts and information. Many security-related attacks can be prevented in conventional IT systems that do not use cloud computing. Focused cloud-based crimes are already using their innovations. Many security vulnerabilities in cloud computing are unique compared to their predecessors in non-cloud computing environments because data and business logic are stored on an external cloud server that lacks accessible oversight. The denial-of-service (DoS) assault is one technique that has been in the spotlight recently. Denial-of-service incidents are directed at the server rather than the people it supports. DoS attackers attempt to flood live servers by masquerading genuine users to overload the service’s capacity to handle incoming inquiries [1]. Cloud computing is an Internet-based service that enables users to access configurable computing resource sharing pools (including server, storage, application software, services, networks, etc.) to achieve online access to computing resources on demand. As a mixture of emerging technologies and business models, cloud computing has developed rapidly in recent years due to its advantages of super-large scale, virtualization, high reliability, good scalability and on-demand services. To overcome this issue, multiple inquiries are sent to the server simultaneously. The term “distributed denial of service,” or DDoS, refers to a variation on the classic “denial of service” that uses numerous computers to attack and impair one service at a time simultaneously.

Conclusion

The key goals of this study are to learn how to recognize and prevent attacks involving distributed denial-of-service. The first and most crucial step is determining which ports can be exploited. Nevertheless, this approach is not risk-free because susceptible ports are more likely to be exploited. Given ParrotSec’s track record for stability and performance, we decided it would be the ideal choice for our company’s computer system. Since a DDoS attack involves sending one million separate packets toward the target, starting with an on-the-internet website would be best. The targeted website was taken offline after it became clear that an assault had happened. Machine learning is constructive in this detecting process as well. Using this data, the most popular and accessible tool, “weka,” is being trained. Employing pre-processing techniques and the “discretize” filter to achieve the desired effect. Therefore, the following phase is not only quite intriguing but also rather useful for both forecasting and detecting. We employed both methods and compared the findings on the same platform, and we found that the naive Bayes method provides the most trustworthy conclusions. PCA selected 21 features from the possible 42 features, while LVQ selected only 20 features. The results suggest that LVQ based feature selection in the DT model may be more accurate than other methods in identifying attacks. As mentioned earlier, the model also outperformed the previous models in terms of accuracy, recall, specificity, and f-score. It was shown that the naive Bayes model had significantly better predictive power than the random forest model. There is a chance that a false positive rate warning will be triggered for packet transmissions within a network. Moreover, when compared to the random forest, naive Bayes produces considerably more accurate forecasts. It was demonstrated that the Naive Bayes algorithm outperformed the random forest technique to identify the false and actual rate of transmissions. The result detection is not carried out in real time. Although attacks can be detected, real-time alarm cannot be realized in the environment of high cluster security, so the feasibility of real-time monitoring under Hadoop platform should be studied continuously.