State of the Art Intrusion Detection System for Cloud Computing

The term Cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today Cloud computing not only provides innovative improvements in resource utilisation but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies  are blooming rapidly. From the perspective of security, Cloud computing also introduces concerns about data protection and intrusion detection mechanism. This paper surveys, explores and informs researchers about the latest developed Cloud Intrusion Detection Systems by providing a comprehensive taxonomy and investigating possible solutions to detect intrusions in cloud computing systems. As a result, we provide a comprehensive review of Cloud Intrusion Detection System research, while highlighting the specific properties of Cloud Intrusion Detection System. We also present taxonomy on the key issues in Cloud Intrusion Detection System area and discuss the different approaches taken to solve the issues. We conclude the paper with a critical analysis of challenges that have not fully solved

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation

TOWARDS A HOLISTIC EFFICIENT STACKING ENSEMBLE INTRUSION DETECTION SYSTEM USING NEWLY GENERATED HETEROGENEOUS DATASETS

With the exponential growth of network-based applications globally, there has been a transformation in organizations\u27 business models. Furthermore, cost reduction of both computational devices and the internet have led people to become more technology dependent. Consequently, due to inordinate use of computer networks, new risks have emerged. Therefore, the process of improving the speed and accuracy of security mechanisms has become crucial.Although abundant new security tools have been developed, the rapid-growth of malicious activities continues to be a pressing issue, as their ever-evolving attacks continue to create severe threats to network security. Classical security techniquesfor instance, firewallsare used as a first line of defense against security problems but remain unable to detect internal intrusions or adequately provide security countermeasures. Thus, network administrators tend to rely predominantly on Intrusion Detection Systems to detect such network intrusive activities. Machine Learning is one of the practical approaches to intrusion detection that learns from data to differentiate between normal and malicious traffic. Although Machine Learning approaches are used frequently, an in-depth analysis of Machine Learning algorithms in the context of intrusion detection has received less attention in the literature.Moreover, adequate datasets are necessary to train and evaluate anomaly-based network intrusion detection systems. There exist a number of such datasetsas DARPA, KDDCUP, and NSL-KDDthat have been widely adopted by researchers to train and evaluate the performance of their proposed intrusion detection approaches. Based on several studies, many such datasets are outworn and unreliable to use. Furthermore, some of these datasets suffer from a lack of traffic diversity and volumes, do not cover the variety of attacks, have anonymized packet information and payload that cannot reflect the current trends, or lack feature set and metadata.This thesis provides a comprehensive analysis of some of the existing Machine Learning approaches for identifying network intrusions. Specifically, it analyzes the algorithms along various dimensionsnamely, feature selection, sensitivity to the hyper-parameter selection, and class imbalance problemsthat are inherent to intrusion detection. It also produces a new reliable dataset labeled Game Theory and Cyber Security (GTCS) that matches real-world criteria, contains normal and different classes of attacks, and reflects the current network traffic trends. The GTCS dataset is used to evaluate the performance of the different approaches, and a detailed experimental evaluation to summarize the effectiveness of each approach is presented. Finally, the thesis proposes an ensemble classifier model composed of multiple classifiers with different learning paradigms to address the issue of detection accuracy and false alarm rate in intrusion detection systems

DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

Analysing web-based malware behaviour through client honeypots

With an increase in the use of the internet, there has been a rise in the number of attacks on servers. These attacks can be successfully defended against using security technologies such as firewalls, IDS and anti-virus software, so attackers have developed new methods to spread their malicious code by using web pages, which can affect many more victims than the traditional approach. The attackers now use these websites to threaten users without the user’s knowledge or permission. The defence against such websites is less effective than traditional security products meaning the attackers have the advantage of being able to target a greater number of users. Malicious web pages attack users through their web browsers and the attack can occur even if the user only visits the web page; this type of attack is called a drive-by download attack. This dissertation explores how web-based attacks work and how users can be protected from this type of attack based on the behaviour of a remote web server. We propose a system that is based on the use of client Honeypot technology. The client Honeypot is able to scan malicious web pages based on their behaviour and can therefore work as an anomaly detection system. The proposed system has three main models: state machine, clustering and prediction models. All these three models work together in order to protect users from known and unknown web-based attacks. This research demonstrates the challenges faced by end users and how the attacker can easily target systems using drive-by download attacks. In this dissertation we discuss how the proposed system works and the research challenges that we are trying to solve, such as how to group web-based attacks into behaviour groups, how to avoid attempts at obfuscation used by attackers and how to predict future malicious behaviour for a given web-based attack based on its behaviour in real time. Finally, we have demonstrate how the proposed system will work by implementing a prototype application and conducting a number of experiments to show how we were able to model, cluster and predict web-based attacks based on their behaviour. The experiment data was collected randomly from online blacklist websites.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysing web-based malware behaviour through client honeypots

With an increase in the use of the internet, there has been a rise in the number of attacks on servers. These attacks can be successfully defended against using security technologies such as firewalls, IDS and anti-virus software, so attackers have developed new methods to spread their malicious code by using web pages, which can affect many more victims than the traditional approach. The attackers now use these websites to threaten users without the user’s knowledge or permission. The defence against such websites is less effective than traditional security products meaning the attackers have the advantage of being able to target a greater number of users. Malicious web pages attack users through their web browsers and the attack can occur even if the user only visits the web page; this type of attack is called a drive-by download attack. This dissertation explores how web-based attacks work and how users can be protected from this type of attack based on the behaviour of a remote web server. We propose a system that is based on the use of client Honeypot technology. The client Honeypot is able to scan malicious web pages based on their behaviour and can therefore work as an anomaly detection system. The proposed system has three main models: state machine, clustering and prediction models. All these three models work together in order to protect users from known and unknown web-based attacks. This research demonstrates the challenges faced by end users and how the attacker can easily target systems using drive-by download attacks. In this dissertation we discuss how the proposed system works and the research challenges that we are trying to solve, such as how to group web-based attacks into behaviour groups, how to avoid attempts at obfuscation used by attackers and how to predict future malicious behaviour for a given web-based attack based on its behaviour in real time. Finally, we have demonstrate how the proposed system will work by implementing a prototype application and conducting a number of experiments to show how we were able to model, cluster and predict web-based attacks based on their behaviour. The experiment data was collected randomly from online blacklist websites.EThOS - Electronic Theses Online ServiceGBUnited Kingdo