Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

Deep Learning Enhanced Visulization Tool For Network Monitroing

In this era of web technology driven by social networks, cloud computing, big data, and E-business, technology is also rapidly evolving. Most of the information is stored and managed via the Internet. With an increase in these development tools and techniques, cyber-crime is constantly increasing. The level of damage these attacks cause to the system affects the organizations to the core. Contemporary Deep Learning and Machine Learning technologies have become the popular choice of intrusion detection systems for the detection and prediction of cyber-attack. Similarly, cyber-security visualization is also an integral and essential part of monitoring network traffic and optimization. Abundant work has already been done to detect attacks, but monitoring these attacks still appears as elusive as detection for cyber analysts. However, the current open-source visualization tool has not been integrated with Deep Learning models to gain intelligence on the network. While many researchers [3] are already working on cyber-attack defense mechanisms, this research also takes advantage of Deep Learning and Machine Learning technologies to contribute to the work against such crimes. A novel Deep Learning enhanced visualization tool is also proposed for malicious traffic node prediction and monitoring. The proposed method exploits the intriguing properties of Deep Learning models to gain intelligence for network monitoring. A real-world DARPA dataset has been used to validate the proposed method. Index Terms—Cyber-security, data analysis, data science, darpa-dataset, decision tree, deep learning, deep neural network, DL model, ML model, network analysis tool, network monitoring tool, supervised learning, support vector machine, visualization tool

Analyzing HTTP requests for web intrusion detection

Many web application security problems related to intrusion have resulted from the rapid development of web applications. To reduce the risk of web application problems, web application developers need to take measures to write secure applications to prevent known attacks. When such measures fail, it is important to detect such attacks and find the source of the attacks to reduce the estimated risks. Intrusion detection is one of the powerful techniques designed to identify and prevent harm to the system. Most defensive techniques in Web Intrusion Systems are not able to deal with the complexity of cyber-attacks in web applications. However, machine learning approaches could help to detect known and unknown web application attacks. In this paper, we present machine learning techniques to classify the HTTP requests in the well-known dataset CSIC 2010 HTTP (Giménez et al., 2012) as normal or abnormal traffic, and we compare our experimental results with the results reported by Pham et al. in 2016 and Nguyen et al. in 2011. These experiments produce results for overlapping sets of machine-learning techniques and different sets of features, allowing us to compare how good the various feature sets are for the various machine-learning techniques, at least on this dataset. Keywords: intrusion detection system; anomaly detection; web application attacks; machine learning

Towards Adversarial Resilience in Proactive Detection of Botnet Domain Names by using MTD

Artificial Intelligence is often part of state-of-the-art Intrusion Detection Systems. However, attackers use Artificial Intelligence to improve their attacks and circumvent IDS systems. Botnets use artificial intelligence to improve their Domain Name Generation Algorithms. Botnets pose a serious threat to networks that are connected to the Internet and are an enabler for many cyber-criminal activities (e.g., DDoS attacks, banking fraud and cyber-espionage) and cause substantial economic damage. To circumvent detection and prevent takedown actions, bot-masters use DGAs to create, maintain and hide C&C infrastructures. Furthermore, botmasters often release its source code to prevent detection, leading to numerous similar botnets that are created and maintained by different botmasters. As these botnets are based on nearly the same source code basis, they often share similar observable behavior. Current work on detection of DGAs is often based on applying machine learning techniques, as they are capable to generalize and to also detect yet unknown derivatives of a known botnets. However, these machine learning based classifiers can be circumvented by applying adversarial learning techniques. As a consequence, there is a need for resilience against adversarial learning in current Intrusion Detection Systems. In our work, we focus on adversarial learning in DNS based IDSs from the perspective of a network operator. Further, we present our concept to make existing and future machine learning based IDSs more resilient against adversarial learning attacks by applying multi-level Moving Target Defense strategies

Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods

Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.Comment: 9 pages. 7 figures. 7 tables. 46 references. Submitted to a special issue Journal of Information Security and Applications, Machine Learning Techniques for Cyber Security: Challenges and Future Trends, Elsevie

A Supervised Machine Learning Based Intrusion Detection Model for Detecting Cyber-Attacks Against Computer System

Internet usage has become essential for correspondence in almost every calling in our digital age. To protect a network, an effective intrusion detection system (IDS) is vital. Intrusion Detection System is a software application to detect network intrusion using various machine learning algorithms. The function of the expert has been lessened by machine learning approaches since knowledge is taken directly from the data. The fact that it makes use of all the features of an information packet spinning in the network for intrusion detection is weakened by the employment of various methods for detecting intrusions, such as statistical models, safe system approaches, etc. Machine learning has become a fundamental innovation for cyber security. Two of the key types of attacks that plague businesses, as proposed in this paper, are Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks. One of the most disastrous attacks on the Internet of Things (IOT) is a denial of service.  Two diverse Machine Learning techniques are proposed in this research work, mainly Supervised learning. To achieve this goal, the paper represents a regression algorithm, which is usually used in data science and machine learning to forecast the future. An innovative approach to detecting is by using the Machine Learning algorithm by mining application-specific logs. Cyber security is a way of providing their customers the peace of mind they need knowing that they have secured their information and money