Jun 2026· American Journal of AI Cyber Computing Management· Vol 6, pp. 476-484· 0 citations· 1 references
TL;DR
This paper presents a comprehensive framework for cyber threat forecasting by integrating traditional machine learning techniques with advanced Generative AI models and demonstrates that Generative AI significantly improves cyber threat prediction accuracy, reduces false-positive rates, enhances adaptive learning, and supports real-time security decision-making.
Abstract
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.
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 and concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems.
M. A. Gandhi, Dinesh Baban Kute, U. Hemavathi· International journal of com...· 0 citations
The rapid advancement of digital communication and networking technologies has resulted in a significant increase in the frequency and sophistication of cyber threats, creating new challenges for securing modern computer networks. Traditional intrusion detection approaches mainly depend on signature-based techniques and predefined security rules, making them less effective against newly emerging and continuously evolving cyber attacks. To address these limitations, this study presents an Explainable Artificial Intelligence (XAI)-based cyber threat detection framework that combines Long Short-Term Memory (LSTM) and Autoencoder models for accurate and transparent threat detection. The LSTM model captures sequential network traffic patterns to identify temporal attack behaviours, whereas the Autoencoder detects anomalous activities by learning the characteristics of normal network traffic. The integration of Explainable AI enables users to understand prediction outcomes by highlighting the factors influencing each detection decision. Furthermore, the framework performs quantitative risk assessment, categorizes threats into multiple severity levels, and generates suitable mitigation recommendations through an interactive web-based dashboard. Experimental results demonstrate that the proposed framework provides reliable, interpretable, and effective cyber threat detection, thereby supporting security professionals in making timely and well-informed decisions.
Indu Asitha, M. N.· International Journal of Com...· 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
APTs can be very advanced, able to hide within an organization for years, potentially compromising sensitive data and information. Old-fashioned signature-driven security tools don't keep up with the latest and most advanced attacks, and thus require proactive and intel-driven threats detection products. This research aims to design an early prediction and detection system of Advanced Persistent Threat activities with a machine learning system that works on Cyber Threat Intelligence. The architecture pulls together any and all threat intelligence gathered from network traffic logs, security alerts and external sources including Indicators of Compromise that include suspicious IP addresses, malicious website addresses, and other unusual communications. After data preprocessing and feature engineering, machine learning models such as Random Forest, Support Vector Machine, and Gradient Boosting are employed to learn the harmful user actions and foresee potentially high-risk actions. This suggested methodology is tested using the CICIDS2017 and UNSW-NB15 benchmark datasets of cyber security. It was observed from the experimental results that the best results has been obtained by Random Forest classifier with the highest accuracy as 97.8% after the differentiation of the legitimate and harmful activity. The results show that the integration of Cyber Threat Intelligence with machine learning has a significant impact on early threat detection, reduces the number of false-positive alerts and strengthens cyber security efforts to prevent Advanced Persistent Threat attacks.
Maheshwari S, S. Kirubakaran, R. Chitra et al.· 2026 6th International Confe...· 0 citations
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.
S. Tamilselvi· Journal of Intelligent Decis...· 0 citations
Modern cyber attacks increasingly involve adaptive adversaries, zero-day exploitation, polymorphic behavior, multistage campaigns, and weak signals distributed across heterogeneous security telemetry. Traditional signature-based intrusion
detection systems are effective for known threats but provide limited capability for predicting emerging attacks before they fully
materialize. This paper presents THREATPREDICT-AI, a machine learning framework for predicting emerging cyber threats
through weak-signal anomaly detection, time-series threat forecasting, graph-based threat correlation, ensemble risk scor-ing,
explainable prediction artifacts, audit logging, and Human-in-the-Loop analyst validation. The proposed framework ingests safe
cyber telemetry such as SIEM logs, IDS alerts, endpoint events, firewall records, authentication logs, and cyber threat
intelligence indicators. It converts raw events into event-level, window-level, temporal, and graph-level features. Anomaly risk,
forecast risk, and graph risk are fused into a calibrated threat score and mapped to low, medium, or high risk levels. A
Flask-based prototype was implemented with a SOC dashboard, role-based access control, synthetic event generation, prediction
management, HITL review, configurable model settings, and audit logs. Experimental demonstration using 220 synthetic cyber
events generated 59 prediction windows and detected a high-risk emerging threat window with a final score of 0.83. The
results show that combining anomaly, temporal, and graph sig-nals improves proactive threat identification while explainability,
auditability, and analyst governance improve operational trust.
V. Pandi, D. B. Yadav· International Journal for Re...· 0 citations