Securing Cyberspace: Innovations and Challenges in Computational Intelligence

Cybersecurity is of paramount importance in safeguarding computer networks, systems, and data from unauthorized access and malicious activities. As technology advances and digital infrastructure becomes more interconnected, the threat landscape evolves, leading to a surge in cyber-attacks. This surge has been fueled by accelerated digitization, increased reliance on digital infrastructure, and the proliferation of Internet-connected devices. Notably, the exponential growth in internet usage has led to a corresponding increase in cyber-attack incidents each year. To combat these threats, various counter-measures, including intrusion detection systems (IDSs), have been developed. In recent years, there has been a notable trend towards leveraging computational intelligence techniques, such as machine learning (ML), deep learning (DL), and data mining (DM), to bolster cybersecurity efforts. These techniques aim to enhance threat detection, understand attack patterns, and fortify defenses against evolving methodologies. However, challenges persist, including the mitigation of zero-day attacks and concerns about the security of ML techniques against adversarial attacks. Adversaries may exploit vulnerabilities in ML models, raising questions about their reliability and robustness in real-world cybersecurity scenarios. In this context, image encryption, neural network-based schemes, convolutional neural networks (CNNs), and deep learning-based adversarial learning schemes are widely deployed to ensure cybersecurity. These technologies play a crucial role in securing multimedia data and combating cyber threats in an increasingly interconnected digital landscape. This article presents a detailed literature review about existing methods to ensure the cybersecurity by utilizing, encryption neural cryptography and adversarial learning.