Jul 2026· International Journal of Computer Science and Engineering· 0 citations
TL;DR
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.
Abstract
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.
An Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest is presented, which effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks.
T Pushpalatha and RP Rajeshwari· International Journal of Adv...· 0 citations
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 results showed that embedding explainability in an IDS enhances the human-AI partnership, allowing security analysts to confirm the results of their IDS, mitigate false-positive ambiguity, optimize incident response, and meet regulatory and ethical obligations.
Christian Manna Guimma· Scriptora International Jour...· 0 citations
LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies, which shows its capacity in learning long-lasting dependencies.
Jawad Hussain Awan, Misbah Safdar, Muhammad Ayaz Shirazi et al.· Italian National Conference...· 0 citations
Phishing and social engineering rank among the most significant facilitators of cyber-crime, which encompasses data breaches, cyber-attacks, ransomware schemes, and denial of service incidents. A robust threat detection model is essential for reducing the risk of phishing attacks, as these types of attacks are frequently discussed in dark web forums and are thus widely adopted. It has been noted that these attacks can have severe direct or indirect effects on our assets, with the resulting financial damage closely linked to their occurrence. In this research, two methodologies are introduced: the first is an explainable AI (XAI) model specifically designed to assess cyber-risks associated with correlated phishing threats. The second is a hybrid approach referred to as a classifier ensemble, which employs a combination of three top-performing machine learning models through ensemble learning and categorizes them based on several high-impact parameters related to model performance. Ultimately, both proposed models have been compared and evaluated across multiple factors. The proposed model in addition carried out a precision of 97.5%, consider of 98.7%, and F1-score of 98.1%, with an average development of about 2–3% in comparison with baseline models. SHAP evaluation recognized Page Rank, URL Length, Number of Hyperlinks, Domain Age, Google Index, and Phishing Hints because the maximum influential capabilities affecting phishing prediction, while LIME supplied instance-stage reasons to enhance transparency and analyst confidence. The experimental findings display that integrating explainable artificial intelligence with ensemble getting to know appreciably improves phishing detection accuracy whilst simultaneously improving version interpretability, making the proposed framework appropriate for deployment in realistic cybersecurity environments.
Kalsoom Safdar, Huraira Irfan· International Journal of Inn...· 0 citations
An intelligent cyber threat detection framework based on a hybrid deep learning architecture that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) in order to get around restrictions on conventional security solutions.
Manjali Gupta, J. Kumar, Bharti· International Journal for Re...· 0 citations
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