Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence (XAI) has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms.

/pdf/explainable-artificial-intelligence-xai-to-enhance-trust-4wssaf3qkz.pdf

Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

https://www.mdpi.com/1424-8220/21/2/446/pdf

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

E-government is an indispensable part of a Smart City. Information and communication technologies transform the relationship between citizens, businesses, and government departments, which enables the implementation of e-government, making operational processes efficient and speedy. This chapter investigates the current deployment strategies and the technological solutions of e-government in terms of security and privacy in a Smart City environment; it also identifies the challenges of adoption. In addition, this chapter proposes a decentralized framework based upon blockchain and artificial intelligence to provide a secure and privacy-preserving infrastructure. The proposed framework integrates technologies to provide mutual trust between individuals, businesses, and governments, leading to a greater transparency of activity and less operational overhead. The reduction in process overhead results in lower running costs (therefore increasing revenue) and improves the speed of cross-boundary transactions.

Privacy and Security Aspects of E-government in Smart Cities

An effective genetic algorithm-based feature selection method for intrusion detection systems

Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in the cyberspace The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques These systems can identify and disconnect malicious network traffic, thereby helping to protect networks This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset Based on the findings, the key challenges and opportunities in addressing cyberattacks using artificial intelligence techniques are summarized and future work suggested

/pdf/machine-learning-algorithms-for-network-intrusion-detection-45t3jicb6u.pdf

Machine Learning Algorithms for Network Intrusion Detection

In past, detection of network attacks has been almost solely done by human operators. They anticipated network anomalies in front of consoles, where based on their expert knowledge applied necessary security measures. With the exponential growth of network bandwidth, this task slowly demanded substantial improvements in both speed and accuracy. One proposed way how to achieve this is the usage of artificial intelligence (AI), progressive and promising computer science branch, particularly one of its sub-fields - machine learning (ML) - where main idea is learning from data. In this paper authors will try to give a general overview of AI algorithms, with main focus on their usage for network intrusion detection.

M. Stampar

Papers

Artificial intelligence in network intrusion detection