Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 25 references
Abstract
The rapid growth of interconnected digital infrastructures, cloud computing environments, Internet of Things devices, and enterprise networking systems has significantly increased the frequency, complexity, and sophistication of cyberattacks targeting organizational information assets. Traditional cybersecurity mechanisms based primarily on signature detection and static rule-based monitoring are becoming increasingly ineffective against modern attack strategies such as zero-day exploits, advanced persistent threats, insider attacks, ransomware campaigns, and polymorphic malware. In this context, adaptive threat intelligence frameworks integrated with behavior-based analytics have emerged as a promising approach for enhancing real-time cyberattack detection and proactive security response capabilities. This research investigates the design and implementation of an adaptive threat intelligence framework capable of identifying malicious activities through continuous behavioral analysis, anomaly detection, and dynamic threat assessment techniques. The study focuses on how behavioral analytics can improve cybersecurity resilience by monitoring user activities, network communication patterns, system interactions, application behavior, and endpoint activities to identify deviations from established normal operational baselines. Unlike traditional detection approaches that depend heavily on predefined signatures, behavior-based analytics enables the identification of previously unknown threats and evolving attack vectors through machine learning algorithms, predictive analytics, and intelligent pattern recognition models. The proposed framework integrates adaptive learning mechanisms that continuously update threat intelligence repositories based on real-time attack behaviors, thereby improving detection accuracy and minimizing response delays. The research further examines the role of artificial intelligence, big data analytics, and automated incident response systems in strengthening cyber defense infrastructures across enterprise environments. In addition to operational advantages, the study critically evaluates challenges associated with implementing adaptive threat intelligence systems, including false-positive generation, data privacy concerns, computational complexity, adversarial machine learning attacks, scalability limitations, and integration difficulties within heterogeneous network architectures. The research methodology incorporates quantitative analysis, simulated attack scenarios, case study evaluations, and expert assessments to measure the effectiveness of behavior-based threat detection techniques in identifying malicious activities across dynamic cybersecurity environments. Findings from the study indicate that adaptive threat intelligence frameworks significantly enhance threat visibility, accelerate incident response, reduce detection latency, and improve organizational preparedness against sophisticated cyber threats when compared to conventional security monitoring systems. The research also emphasizes the importance of continuous learning models, human oversight, ethical cybersecurity governance, and secure data management practices to ensure sustainable and reliable implementation of intelligent threat detection systems. The study concludes that behavior-based adaptive cybersecurity frameworks represent a critical advancement in modern cyber defense strategies by enabling organizations to detect, analyze, and respond to emerging cyber threats in real time while maintaining operational continuity, information security, and digital infrastructure resilience in increasingly hostile cyber environments.
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
The rapid digitization of critical infrastructure, businesses, and government services has expanded the cyber-attack surface, making traditional security mechanisms increasingly ineffective. AI-based cyber defense systems powered by real-time analytics provide a proactive and adaptive approach to cybersecurity by integrating machine learning, deep learning, and intelligent threat detection techniques. This study examines the architecture, analytical frameworks, and operational processes of AI-driven cyber defense solutions capable of detecting known and unknown threats, including zero-day attacks and advanced persistent threats (APTs). The proposed framework incorporates continuous monitoring, streaming analytics, anomaly detection, behavioral analysis, and automated response mechanisms. Performance is evaluated using metrics such as detection accuracy, false positive rate, response time, and scalability. 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 rapid expansion of interconnected digital infrastructures, cloud computing environments, Internet of Things ecosystems, and software-defined networks has significantly increased the complexity and sophistication of contemporary cyber threats, thereby challenging the effectiveness of conventional security mechanisms designed primarily for signature-based attack identification. Traditional intrusion detection systems often exhibit limitations in recognizing previously unseen attack patterns, adapting to rapidly evolving threat landscapes, and processing the enormous volume of network traffic generated by modern communication systems. Consequently, the integration of artificial intelligence techniques into cybersecurity frameworks has emerged as a promising approach for enhancing threat detection capabilities, improving response times, and reducing the operational burden imposed on security analysts. This study examines the application of artificial intelligence-powered intrusion detection systems that leverage deep learning architectures and anomaly detection methodologies to identify malicious activities within dynamic network environments. The proposed framework utilizes large-scale cybersecurity datasets comprising network flow characteristics, packet metadata, system logs, user behavior profiles, and communication patterns to develop intelligent detection models capable of distinguishing legitimate activities from suspicious or adversarial behaviors. Various deep learning algorithms, including convolutional neural networks, recurrent neural networks, long short-term memory networks, autoencoders, and hybrid architectures, are explored to capture complex temporal and spatial dependencies associated with cyberattacks. In parallel, anomaly detection mechanisms based on unsupervised and semi-supervised learning techniques are employed to identify deviations from established behavioral baselines, thereby enabling the recognition of zero-day exploits, advanced persistent threats, insider attacks, distributed denial-of-service campaigns, and stealthy intrusion attempts that may evade traditional security controls. Feature engineering, dimensionality reduction techniques, and model optimization procedures are incorporated to improve computational efficiency and enhance predictive accuracy while minimizing false alarm rates. Experimental evaluations indicate that deep learning-enabled intrusion detection systems achieve superior performance in terms of detection precision, adaptability, and scalability when compared with conventional rule-based approaches, particularly in environments characterized by high traffic volumes and continuously evolving attack vectors. Furthermore, real-time analytical capabilities supported by artificial intelligence facilitate proactive threat intelligence generation, automated incident prioritization, and adaptive cybersecurity defense strategies. Despite these advantages, challenges related to data imbalance, adversarial manipulation, model interpretability, privacy concerns, and computational resource requirements remain significant considerations for practical deployment. The study concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems capable of strengthening cyber resilience, reducing organizational exposure to emerging threats, and supporting the development of intelligent, autonomous, and context-aware security infrastructures suited to the demands of increasingly digitized societies.
Mohd. Asif I. Gandhi, Dinesh Baban Kute, U. Hemavathi· International journal of com...· 0 citations
This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems
R. K. Saidala, Amirkhan Pashayev, Tofig Hasanov· Journal of Computational Sci...· 0 citations
The increasing sophistication of cyber threats has created significant challenges for organizations in protecting digital infrastructures, sensitive information, and critical services. Traditional cybersecurity solutions based on signature matching and rule-based systems are often unable to detect emerging attack patterns, zero-day vulnerabilities, and advanced persistent threats in dynamic network environments. Machine Learning (ML) has improved cyber threat detection by enabling intelligent classification of malicious activities using historical security data. However, conventional ML models often require extensive feature engineering and exhibit limited adaptability to evolving attack behaviors. Recent advances in Generative Artificial Intelligence (Generative AI) have transformed cybersecurity by enabling intelligent threat forecasting, automated attack simulation, synthetic data generation, adaptive anomaly detection, and proactive security analysis. This paper presents a comprehensive framework for cyber threat forecasting by integrating traditional machine learning techniques with advanced Generative AI models. The proposed framework utilizes network traffic analysis, system logs, user behavior analytics, threat intelligence feeds, and security event data to predict future cyber threats. Comparative analysis is performed using conventional machine learning algorithms and Generative AI approaches to evaluate forecasting accuracy, prediction capability, and computational efficiency. Experimental results demonstrate that Generative AI significantly improves cyber threat prediction accuracy, reduces false-positive rates, enhances adaptive learning, and supports real-time security decision-making. The proposed framework contributes to the development of intelligent cybersecurity systems capable of proactively forecasting cyber threats and strengthening organizational resilience against rapidly evolving cyberattacks.
P. Paul, Bharath Bhushan, Bathini Revanth et al.· American Journal of AI Cyber...· 0 citations
It is concluded that AI has become an indispensable component of modern cybersecurity strategies and will play a critical role in safeguarding digital infrastructure against emerging cyber threats.
Shaurya Gupta· Innovative Research Thoughts· 0 citations