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As a new area of technology, the Internet of Things (IoT) is a flagship and promising paradigm for innovating society. However, IoT-based critical infrastructures are an appealing target for cybercriminals. Such distinctive infrastructures are increasingly sensitive to cyber vulnerabilities and subject to many cyberattacks. Thus, protecting these infrastructures is a significant issue for organizations and nations. In this context, raising the cybersecurity posture of critical cyber infrastructures is an extremely urgent international issue. In addition, with the rapid development of adversarial techniques, current cyber threats have become more sophisticated, complicated, advanced and persistent. Thus, given these factors, prior to implementing efficient and resilient cybersecurity countermeasures, identification and in-depth mapping of cyber threats is an important step that is generally overlooked. Therefore, to solve cybersecurity challenges, this study presents a critical analysis of the most recent cybersecurity issues for IoT-based critical infrastructures. We then discuss potential cyber threats and cyber vulnerabilities and the main exploitation strategies adopted by cybercriminals. Further, we provide a taxonomy of cyberattacks that may affect critical cyber infrastructures. Finally, we present security requirements and some realistic recommendations to enhance cybersecurity solutions.

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Malware, a lethal weapon of cyber attackers, is becoming increasingly sophisticated, with rapid deployment and self-propagation. In addition, modern malware is one of the most devastating forms of cybercrime, as it can avoid detection, make digital forensics investigation in near real-time impossible, and the impact of advanced evasion strategies can be severe and far-reaching. This makes it necessary to detect it in a timely and autonomous manner for effective analysis. This work proposes a new systematic approach to identifying modern malware using dynamic deep learning-based methods combined with heuristic approaches to classify and detect five modern malware families: adware, Radware, rootkit, SMS malware, and ransomware. Our symmetry investigation in artificial intelligence and cybersecurity analytics will enhance malware detection, analysis, and mitigation abilities to provide resilient cyber systems against cyber threats. We validated our approach using a dataset that specifically contains recent malicious software to demonstrate that the model achieves its goals and responds to real-world requirements in terms of effectiveness and efficiency. The experimental results indicate that the combination of behavior-based deep learning and heuristic-based approaches for malware detection and classification outperforms the use of static deep learning methods.