Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats

This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest

The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date. A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast improvements in machine learning algorithms and their requirements. This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection accuracy using the K-fold cross validation algorithm on the random forest algorithm on approximately 126,000 samples of the NSL-KDD dataset