Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 22 references
Abstract
The increasing sophistication of cyber threats poses serious challenges to national security (NS) and critical infrastructure (CI), requiring adaptive and intelligence-driven cyber defense mechanisms. While recent artificial intelligence (AI)-based methods have improved detection capabilities, many existing solutions focus on isolated threat categories or rely on single-layer detection models, limiting their robustness and deployment feasibility. This work presents a unified and adaptive artificial intelligence (AI)-enabled cyber threats detection framework that simultaneously addresses intrusion detection, malware detection and phishing detection within a cyber warfare context. The proposed framework integrates hybrid detection strategies with a threshold-based decision mechanism to balance detection effectiveness, false positive control and computational efficiency. A formal mathematical formulation supports feature representation, classification and evaluation. The framework is evaluated using multiple publicly available benchmark datasets under a consistent experimental setup. The experimental results demonstrate strong performance across threat categories, achieving detection accuracy above 96%, F1-scores exceeding 95% and false positive rates below 2%, highlighting the framework's effectiveness and deployment suitability for mission-critical cyber defense applications.
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
XAI-CTI is presented, a novel Explainable Artificial Intelligence (XAI)-driven Cyber Threat Intelligence (CTI) framework designed to enable proactive and adaptive cyberattack detection that achieves state-of-the-art detection accuracy and reduces analyst investigation time.
R. Yadav, M.Kala Devi, Chodey et al.· Journal of Intelligent Decis...· 0 citations
The increasing complexity and frequency of cyber-attacks have exposed the critical limitations of traditional signature-based security systems.Artificial Intelligence (AI) encompassing machine learning, deep learning, large language models federated learning, and reinforcement learning has emerged as a transformative paradigm for modern cyber defense. This survey critically examines significant research contributions highlighting AI applications across intrusion detection, malware analysis, phishing detection, ransomware mitigation, distributed denial-of-service prevention, and network security. A structured taxonomy of AI-driven cyber security techniques is presented, supported by analysis of widely adopted evaluation datasets including NSL-KDD, UNSW-NB15, CIC-IDS2017, and CICIoMT2024 which are assessed in terms of their scope, attack coverage and applicability to real-world deployment scenarios. Selected Pakistani criminal investigations are examined to demonstrate the operational role of AI-assisted digital forensics in judicial proceedings. The survey further identifies unresolved research challenges, including adversarial robustness, dataset generalizability, and the absence of explainability standards for court-admissible AI evidence, and outlines prospective directions to advance the development of intelligent, resilient and legally cyber security systems.
Muhammad Zahid Mehmood, Irshad Ahmed Sumra, Sobia Yaqoob· International journal for el...· 0 citations
The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.
Chinedu Eze· International Journal of App...· 0 citations
The increasing sophistication, frequency, and scale of cyberattacks have created significant challenges for conventional cybersecurity systems. Traditional security solutions such as firewalls, signature-based intrusion detection systems, and antivirus software are largely reactive and depend on predefined rules and known attack patterns. Consequently, these systems often struggle to detect and respond effectively to emerging threats such as Advanced Persistent Threats (APTs), zero-day attacks, ransomware, botnets, and insider attacks. Recent advancements in Artificial Intelligence (AI), particularly Reinforcement Learning (RL), have demonstrated the potential to create autonomous systems capable of learning and adapting to dynamic environments. Simultaneously, Cyber Threat Intelligence (CTI) provides valuable contextual information regarding threat actors, attack techniques, vulnerabilities, and indicators of compromise. This study proposes an Autonomous Cyber Defense Framework that integrates Reinforcement Learning and Threat Intelligence to enhance threat detection, decision-making, and automated response capabilities. The framework employs a Deep Q-Network (DQN) agent that continuously learns optimal defense actions through interaction with network environments while utilizing threat intelligence feeds to improve situational awareness. Experimental evaluation was conducted using benchmark cybersecurity datasets, including CICIDS2017 for Intrusion Detection, UNSW-NB15 for attack classification, CTU-13 for botnet detection and Custom Threat Feeds for threat intelligence. The results indicate that the proposed framework achieved a precision rate of 98.4%, a recall rate of 98.2%, an F1-score of 98.3%, and a threat mitigation rate of 96.8%. False positive rate of 1.9, False negative rate of 1.5 and Response rate of 41%, significantly outperforming traditional machine learning and signature-based security approaches. The findings demonstrate that integrating reinforcement learning with threat intelligence can provide a highly adaptive and proactive cyber defense mechanism suitable for modern network environments.
Abimbola B. Owolabi, F. Osang· Direct Research Journal of E...· 0 citations
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
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