Metaheuristic Optimization for Enhancing Cybersecurity Frameworks: An Overview of Methods and Impacts

Shahid Mahmood¹^,*

¹School of Finance and Economics, Jiangsu University, Zhenjiang, People's Republic of China

Abstract

The increasing number of cyber security threats, notably ransomware and malware, make traditional methods ineffective, hence the need for intelligent methods. This literature review delves into the latest advancements in cyber security technologies that leverage artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance system defenses. Key focus areas include improving ransomware detection, developing more effective intrusion detection systems (IDS), securing Internet of Things (IoT) networks, and strengthening cryptographic methods. The reviewed studies highlight how AI-driven techniques—such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and adversarial training—automate the detection of threats, optimize cyber security measures, and offer real-time responses to evolving risks. Innovative frameworks like Zero Trust Architecture (ZTA) and AI further bolster security by offering automated threat mitigation and anomaly detection. Furthermore, new metaheuristic algorithms are integrated into IDS systems to enhance the detection rate and minimize false positives. The advanced approaches show how AI could solve the constantly emerging challenges in cyber security and focus on a continuous development approach to make cyber security scalable, robust, and transparent when considering complex attacks.

Keywords: Cybersecurity; Artificial intelligence; Machine learning; Ransomware detection; Intrusion detection systems; Internet of Things; Cryptographic systems