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The core of cognitive radio paradigm is to introduce cognitive devices able to opportunistically access the licensed radio bands. The coexistence of licensed and unlicensed users prescribes an effective spectrum hole-detection and a noninterfering sharing of those frequencies. Collaborative resource allocation and spectrum information exchange are required but often costly in terms of energy and delay. In this paper, each secondary user (SU) can achieve spectrum sensing and data transmission through a coalitional game-based mechanism. SUs are called upon to report their sensing results to the elected coalition head, which properly decides on the channel state and the transmitter in each time slot according to a proposed algorithm. The goal of this paper is to provide a more holistic view on the spectrum and enhance the cognitive system performance through SUs behavior analysis. We formulate the problem as a coalitional game in partition form with non-transferable utility, and we investigate on the impact of both coalition formation and the combining reports costs. We discuss the Nash Equilibrium solution for our coalitional game and propose a distributed strategic learning algorithm to illustrate a concrete case of coalition formation and the SUs competitive and cooperative behaviors inter-coalitions and intra-coalitions. We show through simulations that cognitive network performances, the energy consumption and transmission delay, improve evidently with the proposed scheme.

The objective of this work is to present a framework to be followed to model, test, validate and implement a DL model for anomaly, abuse, malware or botnet detection, with the aim of implementing or improving an Intrusion Detection System (IDS) within the NTMA framework, by means of new machine learning and deep learning techniques, which addresses reliability and processing speed considerations. The said process will be used to perform studies on ML and DL models used for cybersecurity in isolation and in combination to extract conclusions, which can help in the improvement of intrusion detection systems using massive data collection techniques used in Big-Data. The example discussed in this work implemented part of our framework by applying the CNN algorithm on the CSE-CIC-IDS2018 dataset. The results are encouraging for the use of ML in IDS, with an efficiency that exceeds 92% after 30 iterations. Thus, this model remains to be improved and tested on real networks.

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