The increasing complexity and scale of cyber threats demand intelligent and adaptive defense mechanisms that extend beyond traditional approaches. Artificial Intelligence (AI) has emerged as a key enabler for enhancing cyber security through automated detection, analysis, and response. This paper presents a comprehensive survey of AI applications in cyber security across five major domains: malware detection, intrusion detection, phishing and spam detection, botnet detection, and cyber forensics. A systematic methodology based on data and methodological triangulation is employed to analyze 75 studies published between 2021 and 2025. The paper introduces a multi-layer taxonomy that maps cyber threats to application domains, analysis methods, AI approaches, and their associated capabilities and limitations. In addition, a cross-domain meta-analysis is conducted to identify recurring trends and assess the adoption of AI across different cyber security scenarios. The analysis reveals that deep learning and transformer-based models dominate data-intensive domains such as intrusion detection and malware analysis, whereas traditional machine learning techniques remain effective in structured and resource-constrained settings, particularly for phishing detection. Key challenges include dataset limitations, limited explainability, adversarial vulnerabilities, and computational constraints. Unlike existing surveys that focus on specific techniques or individual cyber security domains, this work provides a unified, application-oriented perspective on AI-driven cyber security. It further highlights emerging trends, open challenges, and future research directions toward more robust, scalable, and trustworthy cyber security systems.
The reviewed literature indicates that AI-based methodologies often demonstrate superior detection capabilities for intricate and previously unseen attack patterns compared to traditional methods; however, direct performance comparisons are complicated due to discrepancies in datasets, experimental designs, and evaluation protocols.
Jaswanth Garugu· International Journal for Re...· 0 citations
This survey provides a structured synthesis of the current state of the art, identifying key research directions for the next generation of intelligent, autonomous cloud security systems.
Mohammed Adeen Khurshid, Mulla, Mohammed Zuber Mulla, Mohammed Aziz A Khazi et al.· International Journal for Re...· 0 citations
An in-depth comprehensive Systematic Mapping Study (SMS) of 110 relevant articles published between 2015 and 2025 related to AI/GenAI-based IDS, offering a novel and integrated, comprehensive mapping of both defensive and offensive dimensions of AI/GenAI-enabled cybersecurity.
With the widespread adoption of cloud computing, securing enterprise networks against cyber threats has become increasingly important. Cloud environments are highly dynamic and constantly changing, making them susceptible to sophisticated cyberattacks that traditional Intrusion Detection Systems (IDS) often fail to detect. This study focuses on Intelligent Intrusion Detection Systems (IIDS) and their critical role in strengthening cloud security. Unlike conventional signature-based IDS that rely on fixed attack patterns, IIDS employ advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques including deep learning, decision trees, and ensemble models to identify both known and emerging threats with greater accuracy. The paper proposes an integrated framework that combines real-time anomaly detection with automated response capabilities for cloud networks. Key architectural elements of IIDS are examined, alongside major deployment challenges such as scalability, false-positive rates, and computational requirements. Additionally, practical case studies and performance evaluations illustrate how IIDS enhance threat detection by improving accuracy, adaptability, and efficiency. Finally, the paper outlines future research directions to further advance IIDS capabilities and address the evolving security needs of modern cloud infrastructures.
R. Velu· 2026 4th International Confe...· 0 citations
The review outlines future research directions emphasizing lightweight and explainable AI models, graph neural networks, federated and continual learning, adaptive hybrid intelligence, and standardized real-world evaluation frameworks to support the development of accurate, scalable, robust, and deployable malware detection systems for next-generation Software-Defined Networks.
Sudhakar Yerme, Prabhakar L. Ramteke· International journal of adv...· 0 citations
The rapid expansion of digital infrastructures, cloud ecosystems, Internet of Things (IoT) environments, and intelligent enterprise platforms has significantly increased the complexity and frequency of cybersecurity threats. Traditional security mechanisms based on static rules and signature-based detection approaches are increasingly insufficient against sophisticated attacks involving zero-day exploits, advanced persistent threats, automated malware, and coordinated intrusion campaigns. This research paper presents a comprehensive analysis of AI-driven cybersecurity frameworks designed for real-time intrusion detection and threat intelligence generation. The study explores the integration of artificial intelligence (AI), machine learning (ML), deep learning, behavioral analytics, automation, and intelligent decision-making mechanisms for developing adaptive cybersecurity architectures. A research-oriented review methodology is adopted by synthesizing existing contributions from the provided literature, focusing on AI-enabled fraud detection, secure DevOps, zero-trust security, digital twin environments, distributed computing, privacy-preserving models, and intelligent risk assessment frameworks. The proposed analytical framework examines key components including real-time data acquisition, AI-based anomaly detection, threat intelligence processing, automated response orchestration, and continuous security optimization. Findings indicate that AI-driven cybersecurity architectures enhance detection accuracy, reduce response latency, and improve resilience against evolving cyber threats. However, challenges related to explainability, adversarial AI attacks, data privacy, computational requirements, and regulatory compliance remain significant barriers to large-scale adoption. The study contributes a structured understanding of how AI technologies can transform cybersecurity operations from reactive defense mechanisms into proactive, predictive, and autonomous security ecosystems.
Dilshan Jayawardena, Dr. Kavindi Perera· International Journal of Com...· 0 citations