This paper introduces an innovative tool equipped with a multi-perspective, user-friendly dashboard designed to enhance the explainability of AI models, particularly in cybersecurity. By enabling users to select data samples and apply various xAI methods, the tool provides insightful views into the decision-making processes of AI systems. These methods offer diverse perspectives and deepen the understanding of how models derive their conclusions, thus demystifying the "black box" of AI. The tool’s architecture facilitates easy integration with existing ML models, making it accessible to users regardless of their technical expertise. This approach promotes transparency and fosters trust in AI applications by aligning decision-making with domain knowledge and mitigating potential biases.

In recent years, the increasing sophistication and proliferation of cyberthreats have underscored the necessity for robust network security measures, as well as a comprehensive approach to cyberprotection at large. As cyberthreats are continuously more and more complex, and their detection, response and mitigation often involve dealing with big data, the need for novel solutions is present also in cyber-criminal law enforcement (LEA) and network forensics contexts. Traditional, anomaly-based or signature-based intrusion detection systems (IDS) often face challenges in adapting to the evolving cyberattack landscape. On the other hand, Machine Learning (ML) has emerged as a promising approach, proving its ability to detect complex patterns in big data, including applications such as intrusion detection and classification of threats in the network environment, with high accuracy and precision (reduced rate of false positives). In this paper we present the Trustworthy Cyberattack Detector tool (TCAD), benefiting from the machine learning algorithms for the detection and classification of cyberattacks. TCAD can be used for monitoring the network in real-time and for offline analysis of collected network data. We believe that the TCAD can be successfully applied for the task of detecting and classifying evidence during criminal investigations related to network cyber attacks, but also can be helpful for the correlation of discovered network-based events over time with other collected non-network evidence.

The emergence and consecutive entrance of Generative AI (particularly ChatGPT) into the mainstream has provoked all kinds of reactions, from excitement to apprehension, but it has not been definitely decided whether it is a boon or a bane yet. We wish to voice the still unmentioned relation between AI accessibility and social injustice. So far, the initial access to tools such as ChatGPT has been free or low-cost. This is predicated on the availability of open-source or inexpensively sourced data. As the value of models hinges upon high quality, diverse data, the demand for it will increase, resulting in the rising costs of its procuration. We worry that the free models will then turn into expensive commodities, limiting their use only to the privileged entities. This potential shift causes major concerns about ethics and social equity, with the concept of unbundling being one of the potential solutions.

The growing diffusion of malware is causing non-negligible economic and social costs. Unfortunately, modern attacks evolve and adapt to defensive mechanisms, and many threats are designed for the optimal exploitation of the traits of the victims. Thus, phenomena such as mobile malware, fileless malware or stegomalware are becoming widespread and represent the next variations of malicious attacks that have to be faced. In particular, the massive amount of digital content shared on the Internet is increasingly more often being used by attackers for the injection of malicious code to bypass security tools or prevent detection.