Jun 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 3312-3320· 0 citations
TL;DR
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.
Abstract
The rapid expansion of cloud computing infrastructures has fundamentally transformed how organizations manage
and deploy digital services, simultaneously introducing a complex and evolving attack surface that traditional security
mechanisms fail to adequately address. This survey examines the convergence of artificial intelligence (AI) and autonomous
cybersecurity with a focus on cloud environments. We systematically review thirteen recent papers spanning five core research
themes: AI-driven threat detection and classification, explainable AI (XAI) for cybersecurity transparency, autonomous response
and mitigation strategies, real-time cyber threat attribution, and AI-enhanced education for cybersecurity workforce
development. Our analysis highlights the state-of-the-art techniques including Graph Neural Networks (GNNs), transformerbased attention mechanisms, Federated Deep Learning (FDL), reinforcement learning, and multi-modal data fusion, all applied
to the challenge of building self-healing, autonomous cloud defense systems. We further discuss persistent challenges such as
dataset quality, model interpretability, adversarial robustness, and the gap between academic research and real-world
deployment. 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
Artificial intelligence (AI) has emerged as a transformative force in cybersecurity, offering capabilities that extend far beyond the static, rule-based defenses of the past. Machine learning, deep learning, and natural language processing techniques are increasingly embedded in intrusion detection systems, threat intelligence platforms, and automated incident response tools, enabling organizations to identify and neutralize threats with greater speed and precision. However, the same interconnectedness that drives digital transformation—spanning IoT ecosystems, cloud infrastructures, and 5G networks—has also expanded the attack surface available to malicious actors, giving rise to increasingly sophisticated, adaptive, and often AI-enabled threats such as adversarial machine learning attacks, deepfake-driven social engineering, and automated supply chain exploits. This paper examines the dual role of AI as both a defensive asset and a potential vector of risk within modern cybersecurity ecosystems. Drawing on a review of existing AI-driven security solutions, comparative analysis of AI-based versus traditional defense mechanisms, and case study evaluation, the study assesses the effectiveness, limitations, and ethical implications of AI integration in cyber defense. Findings indicate that while AI substantially improves threat detection accuracy and response times, challenges related to explainability, adversarial vulnerability, and regulatory oversight remain significant barriers to widespread adoption. The paper concludes with practical recommendations for organizations and policymakers seeking to harness AI's defensive potential while mitigating its associated risks, emphasizing the need for explainable AI frameworks, human-AI collaboration, and adaptive governance structures in an increasingly interconnected digital age.
Nicolas Guzman Camacho· Journal of Artificial Intell...· 0 citations
The rapid digital transformation of critical infrastructure has significantly increased its exposure to complex and continuously evolving cyber threats, creating an urgent need for intelligent and adaptive cybersecurity solutions. Conventional security mechanisms, such as signature-based and rule-based intrusion detection systems, often struggle to identify novel attack patterns and provide timely responses to emerging threats. To address these limitations, this study proposes an artificial intelligence (AI)-driven framework for cyber threat detection and automated response that strengthens the security, resilience, and operational reliability of critical infrastructure environments. The experimental evaluation demonstrates that AI-based techniques substantially outperform traditional cybersecurity methods in terms of detection performance. Conventional rule-based systems achieve an average detection accuracy of approximately 68%, whereas machine learning and deep learning models improve the accuracy to nearly 80% and 88%, respectively. The proposed AI-driven framework delivers the highest performance, achieving an overall detection accuracy of approximately 94%. This superior performance highlights its capability to accurately identify both previously known attacks and sophisticated zero-day threats. Beyond detection accuracy, the study evaluates response time, which plays a crucial role in limiting the impact of cyber incidents. The findings reveal that the proposed AI-enabled response mechanism reduces the average response time to approximately 35 seconds, compared with around 150 seconds for manual response processes and 90 seconds for conventional rule-based automation. Such improvements enable faster threat containment, minimize operational disruption, and enhance the resilience of critical infrastructure systems. The framework also demonstrates notable improvements in reducing false positive alerts. The AI-driven approach achieves a false positive rate of approximately 5%, significantly lower than the 20% observed in signature-based systems and the 12% reported for anomaly-based detection methods. By minimizing false alarms, the proposed framework improves the efficiency of security operations, reduces alert fatigue among cybersecurity analysts, and enables security teams to prioritize genuine threats more effectively.
Reily Kaium, Lizi Alasa, K. Robert et al.· The Eastasouth Journal of In...· 0 citations
The rapid evolution of intelligent cyber-attacks has challenged traditional cybersecurity mechanisms, necessitating the adoption of Artificial Intelligence (AI)-driven defense strategies. Advanced threats such as ransomware, zero-day exploits, Advanced Persistent Threats (APTs), and AI-powered phishing campaigns require adaptive and autonomous security solutions capable of real-time detection and response. This chapter explores innovative AI technologies, including Machine Learning, Deep Learning, Reinforcement Learning, Explainable AI, and Generative AI, for intelligent attack detection, prediction, and mitigation. A unified AI-driven cybersecurity framework is proposed that integrates threat intelligence, behavioural analytics, anomaly detection, explainable decision-making and autonomous incident response to enhance cyber resilience. A case study demonstrates the practical implementation of the framework in an enterprise environment. The chapter also discusses current challenges, implementation limitations, and future research directions, providing researchers and practitioners with insights into developing scalable, trustworthy, and next-generation AI-enabled cybersecurity systems.
S. Mohanarangan, G. Shoba, D. Karthika et al.· International Journal of Com...· 0 citations
The rapid expansion of digital technologies, cloud computing platforms, Internet of Things (IoT) devices, and interconnected communication infrastructures has significantly increased the frequency and complexity of cyberattacks across modern organizations. Conventional cybersecurity mechanisms such as firewalls, antivirus software, and signature-based intrusion detection systems have become increasingly inadequate against sophisticated threats, including advanced persistent threats, ransomware, zero-day attacks, phishing campaigns, and distributed denial-of-service attacks. The dynamic and adaptive nature of modern cyber threats necessitates the development of intelligent cybersecurity frameworks capable of real-time threat detection, predictive analysis, automated response, and adaptive defense mechanisms.
This study presents a multi-layer deep learning framework for intelligent cyberattack prevention. The proposed framework integrates multiple deep learning architectures, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Autoencoder models, to enhance threat detection accuracy, anomaly identification, behavioral analysis, and predictive cyber defense capabilities. The framework operates through layered analytical processes involving data acquisition, preprocessing, feature extraction, anomaly detection, threat classification, attack prediction, and automated response management.
The system was implemented using Python programming language, TensorFlow deep learning libraries, cloud-based datasets, and network traffic monitoring environments. Experimental evaluations were conducted using benchmark cybersecurity datasets containing various attack categories, including denial-of-service attacks, brute-force intrusions, malware activities, phishing attempts, and botnet traffic. Performance metrics, including detection accuracy, precision, recall, false-positive rate, and response time, were analyzed to evaluate system effectiveness.
The findings demonstrate that the proposed multi-layer deep learning framework significantly improves cyberattack detection accuracy, reduces false-positive alerts, enhances real-time response capabilities, and strengthens proactive cybersecurity defense mechanisms. The study concludes that deep learning-driven cybersecurity systems provide highly effective solutions for addressing evolving cyber threats within modern digital infrastructures.
A. Owolabi· International journal of re...· 0 citations
The rapid proliferation of sophisticated cyberattacks poses an unprecedented challenge to existing intrusion detection and threat intelligence systems. Conventional machine learning (ML)-based detection approaches, while effective in controlled environments, suffer from opacity, limited adaptability, and an inability to proactively anticipate novel attack vectors. This paper presents XAI-CTI, a novel Explainable Artificial Intelligence (XAI)-driven Cyber Threat Intelligence (CTI) framework designed to enable proactive and adaptive cyberattack detection. The proposed framework integrates a multi-layered threat intelligence pipeline comprising real-time data ingestion, federated feature engineering, ensemble-based anomaly detection, and post-hoc explainability modules grounded in SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). By coupling gradient-boosted ensemble models with attention-based deep neural architectures, XAI-CTI achieves state-of-the-art detection accuracy of 99.21% on the CIC-IDS2017 dataset, 98.87% on the UNSW-NB15 benchmark, and 97.94% on the NSL-KDD corpus, with average false positive rates below 0.31%. The adaptive learning module employs continual learning strategies to mitigate concept drift and maintain performance under evolving threat landscapes. Extensive evaluations demonstrate that the explainability layer reduces analyst investigation time by 43% compared to black-box baselines while maintaining detection fidelity.
R. Yadav, M.Kala Devi, Chodey et al.· Journal of Intelligent Decis...· 0 citations
The convergence of Cloud Computing and the Internet of Things (IoT) has created a hyper-connected ecosystem that drives modern industry but simultaneously expands the cyber-attack surface. Traditional security mechanisms, such as static firewalls and signature-based intrusion detection, are increasingly insufficient against sophisticated, polymorphic threats like Distributed Denial of Service (DDoS) and zero-day exploits. This review paper critically examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance information security within Cloud-IoT networks. We analyze the current state of AI-driven frameworks, focusing on hybrid Deep Learning models (CNN-LSTM), predictive analytics, and automated threat response mechanisms. A systematic literature survey of recent advancements identifies key trends, including the shift towards decentralized learning and real-time adaptive defense. Furthermore, we critically analyze the limitations of existing approaches, such as computational overhead and data privacy concerns. Finally, this paper identifies significant research gaps and provides strategic recommendations for developing robust, scalable, and intelligent security architectures for next-generation environments.
R. Saravanakumar, V.Anuratha, M.Elamparithi· International journal of com...· 0 citations