Using Machine Learning for Security Issues in Cognitive IoT

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by machine learning. Security and privacy of devices intrusion detection their success in achieving classification accuracy, machine deep learning with intrusion detection systems have greatly increased popularity. However, the need to store communication centralized server compromise privacy and security. Contrast, Federated Learning (FL) fits appropriately as a privacy-preserving decentralized learning technique that trains locally transfer the parameters the centralized instead of purpose current research provide thorough and application FL intrusion detection systems. Machine Learning (ML) and Deep Learning (DL) approaches, which may embed intelligence in IoT devices and networks, can help to overcome a variety of security challenges. The research includes a detailed overview of the application of FL in several anomaly detection domains. In addition, it increases understanding of ML and its application to the field of the Cognitive Internet of Things (CIoT). This endeavour also includes something crucial . The relevant FL implementation issues are also noted, revealing potential areas for further research. The researcher emphasised the flaws in current security remedies, which call for ML and DL methods. The report goes into great detail on how ML and DL are now being utilised to help handle various security issues that IoT networks are facing. Random Neural Networks that have been trained using data retrieved by Cognitive Packets make the routing decisions. A number of potential future directions for ML and DL-based IoT security research are also included in the study. The report concludes by outlining workable responses to the problem. The paper closes by offering a beginning point for future study, describing workable answers to the problem of FL-based intrusion detection system implementation

Adaptive Traffic Fingerprinting for Darknet Threat Intelligence

Darknet technology such as Tor has been used by various threat actors for organising illegal activities and data exfiltration. As such, there is a case for organisations to block such traffic, or to try and identify when it is used and for what purposes. However, anonymity in cyberspace has always been a domain of conflicting interests. While it gives enough power to nefarious actors to masquerade their illegal activities, it is also the cornerstone to facilitate freedom of speech and privacy. We present a proof of concept for a novel algorithm that could form the fundamental pillar of a darknet-capable Cyber Threat Intelligence platform. The solution can reduce anonymity of users of Tor, and considers the existing visibility of network traffic before optionally initiating targeted or widespread BGP interception. In combination with server HTTP response manipulation, the algorithm attempts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by shadow, we show that the detection scheme is effective with false positive rate of 0.001, while sensitivity detecting non-targets was 0.016+-0.127. Our algorithm could assist collaborating organisations willing to share their threat intelligence or cooperate during investigations.Comment: 26 page

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