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

The Challenges in SDN/ML Based Network Security : A Survey

Machine Learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking (SDN) emerge. Sitting at the application layer and communicating with the control layer, machine learning based SDN security models exercise a huge influence on the routing/switching of the entire SDN. Compromising the models is consequently a very desirable goal. Previous surveys have been done on either adversarial machine learning or the general vulnerabilities of SDNs but not both. Through examination of the latest ML-based SDN security applications and a good look at ML/SDN specific vulnerabilities accompanied by common attack methods on ML, this paper serves as a unique survey, making a case for more secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with arXiv:1705.0056

Machine Learning Interpretability in Malware Detection

The ever increasing processing power of modern computers, as well as the increased availability of large and complex data sets, has led to an explosion in machine learning research. This has led to increasingly complex machine learning algorithms, such as Convolutional Neural Networks, with increasingly complex applications, such as malware detection. Recently, malware authors have become increasingly successful in bypassing traditional malware detection methods, partly due to advanced evasion techniques such as obfuscation and server-side polymorphism. Further, new programming paradigms such as fileless malware, that is malware that exist only in the main memory (RAM) of the infected host, add to the challenges faced with modern day malware detection. This has led security specialists to turn to machine learning to augment their malware detection systems. However, with this new technology comes new challenges. One of these challenges is the need for interpretability in machine learning. Machine learning interpretability is the process of giving explanations of a machine learning model\u27s predictions to humans. Rather than trying to understand everything that is learnt by the model, it is an attempt to find intuitive explanations which are simple enough and provide relevant information for downstream tasks. Cybersecurity analysts always prefer interpretable solutions because of the need to fine tune these solutions. If malware analysts can\u27t interpret the reason behind a misclassification, they will not accept the non-interpretable or black box detector. In this thesis, we provide an overview of machine learning and discuss its roll in cyber security, the challenges it faces, and potential improvements to current approaches in the literature. We showcase its necessity as a result of new computing paradigms by implementing a proof of concept fileless malware with JavaScript. We then present techniques for interpreting machine learning based detectors which leverage n-gram analysis and put forward a novel and fully interpretable approach for malware detection which uses convolutional neural networks. We also define a novel approach for evaluating the robustness of a machine learning based detector

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification