The primary goal of cyber threat intelligence (CTI) is to give advanced and deep knowledge about threat landscape in order to maintain internal and external safeguards against advanced cyberattacks. The fundamental issue is inaccurate, incomplete, outdated, and invaluable threat intelligence. As a result, one of the emerging solutions to the threat information sharing is data analysis based on AI algorithms. In this paper, we introduced a supervised learning-based system that provides a sophisticated classification of cyber threats and risks to improve threat information sharing. Extensive simulations are performed to validate the proposed approach and compare it to numerous supervised learning techniques in terms of accuracy, precision, recall, f1-score, and total support.

The software supply chain is an unexpected revolutionary development in the software development and distribution field, paving the way for a new era of efficiency and creativity. Nonetheless, in this era of rapid technological progress, a significant absence of a complete study that encompasses the software supply chain. To fill this void, our paper introduced a survey about the important elements constituting this new paradigm. Within these pages, we unveil the nuances between the software supply chain with the distributed software paradigm, offering an unique and exhaustive exploration of its architecture, challenges, software lifecycle, security issues, and potential applications.

A software supply chain attack refers to an intrusion in the software development process that aims to inject malicious code into software components before they are deployed to end users. The objective of these attacks is to compromise the security of the end users or organizations using the software. This includes vulnerabilities introduced even during the critical design phase. Such attacks can occur at any stage of the software development process, from the initial design phase to the final delivery of the software to end-users. To address this problem, many security techniques and mechanisms were proposed to protect the software supply chain against the suspicious and malicious actors during the different stages. The scope of this survey is to give a comprehensive overview about the existing security solutions. Moreover, this paper introduces new and exhaustive criteria for attack classification to help identifying the features, capabilities, and limitations of software solutions, thereby enabling stakeholders to make informed decisions about the software products.

To enhance the security of spectrum sensing in centralized cognitive radio networks, we introduce a novel trust game-based model. We aim at encouraging the secondary users to send correctly their local sensing outcomes to the data fusion center. Our proposal ensures both the attacks detection and the punishment of malicious users. Indeed, our proposed punishment function intends to exempt the malicious secondary users with faulty spectrum sensing from participating in the spectrum sensing phase. Extensive simulations illustrate that the proposed game-based model outperforms the AND-rule and OR-rule models in terms of correct decision, missed-detection, false alarm, error probability and throughput.

Spectrum sensing security in cooperative cognitive radio networks with continuously mobile secondary users becomes a critical challenge. Thus, we propose a trust game-based model to ensure the spectrum detection while the mobility of the SUs is taken into account. Our proposal ensures both the attacks detection and the punishment of mobile malicious users launching the Spectrum Sensing Data Falsification (SSDF) attacks. Extensive simulations prove that the proposed model outperforms the AND-rule, OR-rule and Game-Based Secure Sensing (GSS) models in terms of correct decision probability, throughput and error probability with the random and linear mobility models and under four types of SSDF attacks.

In this paper, we introduce a new fault tolerant initial deployment schema. We aim at maximizing WSN lifetime by considering the energy consumption of nodes led by their communication activities. We defined a circular multi-layer model in which a number of nodes are placed in each layer with regards to the overall energy consumption. To that end, we define first a simple worst case model for nodes energy consumption taking into account their involvement in the routing function. From this model, we derive the optimal number of nodes that should be placed in every layer of the network to achieve energy consumption balancing among all layers. Extensive simulations show that the proposed deployment scheme outperforms random and uniform grid deployment models in terms of connectivity, network lifetime and energy consumption.