Jul 2026· Journal of Information and Communication Technologies· Vol 25, pp. 1-30· 0 citations
TL;DR
A unified machine-learning framework for defence against malware-laden URLs, which simultaneously targets intrusion detection and intrusion prevention through module-aware, bio-inspired feature selection through module-aware, bio-inspired feature selection.
Abstract
Cybersecurity threats have become increasingly sophisticated and dynamic due to the rapid evolution of malicious software and attack techniques. However, the reliance of traditional IDS/IPS implementations on signature-based detection limits their ability to identify novel, rapidly evolving, and adaptive malware, despite their effectiveness against known cyber threats. This paper presents a unified machine-learning framework for defence against malware-laden URLs, which simultaneously targets intrusion detection (accuracy first, out of band) and intrusion prevention (latency first, inline) through module-aware, bio-inspired feature selection. The detection module employs random forest (RF) with the bat algorithm (BA), achieving 99.52% accuracy under stratified fivefold validation. This approach yields thousands of additional correct decisions per million URLs and reduces false negatives and false positives without adverse throughput effects. The prevention module utilizes a decision tree (DT) with the HHO ∩ BA consensus subset, achieving an inference time of 1 ms per URL with competitive accuracy (98.96%), enabling more than 1,000 URLs per second per core and meeting strict inline decision budgets. Standard metrics, such as accuracy, precision, recall, and F1-score, along with timing analyses, confirm that aligning feature selection with module objectives strikes a practical balance between detection quality and latency. The resulting guidance is direct: employ RF with BA for intrusion detection when optimal discriminative performance is essential, and adopt DT with the HHO ∩ BA subset for intrusion prevention when millisecond-scale latency dictates deployment.
The exponential rise in cyber threats has created a critical need for intelligent and adaptive intrusion detection systems
(IDS) capable of identifying both known and emerging attack patterns. Traditional rule-based IDS mechanisms, such as Snort,
rely heavily on predefined signatures and struggle against sophisticated attacks including port scanning, web-based exploits, and
distributed denial-of-service (DDoS) attacks. This paper presents an AI-based Intrusion Detection System that integrates
network simulation, machine learning, and real-time visualization into a unified three-layer framework. The NS-3 network
simulator generates realistic normal and malicious traffic between attacker, router, and victim nodes; the resulting packetcapture (PCAP) data is processed by a Python-based IDS engine that applies signature rules for port scanning, DoS flooding,
and web attacks (SQL Injection, XSS, LFI, command injection); and a Random Forest classifier, trained on the CIC-IDS2017
benchmark dataset, augments detection with machine-learning-based classification. A Flask-based web dashboard provides realtime visualization of alerts, packet statistics, and attack distribution. Experimental results show an average detection accuracy of
98.5%, an average F1-score of 97.7%, and a false-positive rate below 1.2%, outperforming rule-based and prior deep-learning
baselines on comparable attack categories. The proposed multi-layered architecture demonstrates that combining simulation,
machine learning, and visualization can produce a scalable and effective solution for modern network security challenges.
T. Senthil, V. Shanmuganeethi· International Journal for Re...· 0 citations
Random Forest achieves the highest performance with a test accuracy of 96.3%, F1-score of 0.947, and AUC of 0.993, establishing it as the optimal algorithm for static malware detection tasks and establishing it as the optimal algorithm for static malware detection tasks.
Umesh Balami, Ganesh Gautam, Gajendra Sharma· American Journal of Agricult...· 0 citations
The rapid growth of cyber landscapes and the development of a new cybersecurity model incorporating PET, deep learning, fuzzy systems, keystroke dynamic authentication, and encryption. It is used to prevent attacks by malware or unauthorized access to cloud systems. The proposed framework, which integrates an artificial intelligence (AI)-driven approach with identity and access management (IAM), enables the adaptive implementation of risk-based login authentication and real time anomaly detection. Unlike conventional security systems that depend on fixed rules and signatures, we provide more sophisticated solutions. A Floydel firewall is dynamically tailored through deep neural networks (DNNs) and automatically adjusts to fluctuating traffic patterns. It employs malware classification based on behavior, utilizes fuzzy logic to manage uncertainty during intrusions, and uses keystroke dynamics for user verification through typing patterns. The experiment demonstrates a 97.6% detection accuracy on benchmark data, while significantly reducing false positives and ensuring data confidentiality through encryption. The nature of cloud security can evolve based on the specific circumstances and threats we encounter. Looking ahead, we plan to delve into cryptography and distributed training to bolster decentralized infrastructures. This proposed framework aims to fortify data protection and ensure user privacy in essential areas like healthcare, financial services, and e-governance, thereby fostering increased trust.
Sai Kiranmai Dornala, S. p· International Journal of Int...· 0 citations
They originate from the rapid rise of cyber threats such as malware, phishing, ransomware,
denial of service, and unauthorised network intrusion, which have proven to be so difficult to
tackle that traditional security measures can hardly deal with the issue. Signature-based
intrusion detection system techniques in particular, which are commonly adopted by traditional
methods, usually lack the ability to detect novel and evolving attack vectors in addition to high
false positive rate and response time. In this regard, this paper proposes an AI threat detection
framework, employing data science methods to boost cybersecurity performances. The
researchers of this paper have tested the effectiveness of several models using a benchmark
dataset for cyber security, including CICIDS2017 or NSL-KDD and machine learning
techniques such as Random Forest, Support Vector Machine, Logistic Regression and
XGBoost for evaluating performance. Using measures of accuracy, precision, recall and F1-
score, the experiments show that the performance of ensemble learning models is higher than
shallow learning models in this research; XGBoost and Random Forest.
Praveen Kumar Reddy Gouni· International Journal of Soc...· 0 citations
The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
Esther J., Grace Phiri, Arockia Venice J.· International Journal of Dat...· 0 citations
Network infrastructure faces mounting pressure from increasingly sophisticated cyber attacks targeting systems reliant on digital connectivity. Activities such as Denial of Service, Probe, Remote-to-Local, and User-to-Root represent persistent threats that are challenging to intercept manually owing to high traffic volumes and rapidly evolving intrusion strategies. This work constructs a web-based Intrusion Detection System prototype by training an entropy-based Decision Tree classifier, conceptually grounded in the C4.5 framework, on the NSL-KDD benchmark. Development adheres to the six-phase CRISP-DM process encompassing business understanding, data preparation, model construction, performance evaluation, and system deployment. Preprocessing involves duplicate record elimination, categorical attribute encoding, and consolidation of multi-class attack labels into a binary Normal/Anomaly scheme. Ten-fold cross-validation yields 99.34% accuracy and 99.35% precision for the Decision Tree, representing a substantial margin over Naive Bayes which attains only 66.67% accuracy. A separate Gain Ratio analysis designates src_bytes as the most informative feature with a value of 0.5366, positioning it as the decision tree root node. The prototype is served through a Streamlit web application that accepts CSV dataset uploads, executes batch anomaly classification, presents detection outcomes through interactive visualizations, and produces exportable reports in both CSV and PDF formats. The system offers an interpretable and practically accessible batch-detection tool, continuous real-time monitoring and evaluation on contemporary intrusion benchmarks are identified as priorities for subsequent research.
Daniel Erick Witopo, Hartana Wijaya· bit-Tech· 0 citations
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