Jul 2026· International journal of Computer Networks & Communications· 0 citations· 30 references
TL;DR
Experimental findings show that the recommended prediction model has outperformed conventional approaches in IDS to secure network data.
Abstract
Network intrusion is the unauthorized access, manipulation, or interruption of computer networks and systems. It comprises a variation of illegal behaviors, such as hacking, virus deployment and data theft. Detecting and blocking intrusions is vital for protecting sensitive data and credentials to ensure the integrity and safety of digital infrastructure. To develop a Machine Learning technique for a cybersecurity Network Intrusion Detection System, this research proposes a novel Cheetah Optimization-driven Intelligent Adaptive Boosting (CO-IAB) technique to protect systems and ensure data identification against threats and breaches by detecting and responding to unauthorized or illegitimate access to computer networks. Initially, the research obtained a dataset that contains a wide range of network intrusion techniques for training the suggested detection model. The collected raw data is pre-processed using Zscore Normalization to improve the quality of the data obtained. The Kernel Principal Component Analysis (Kernal-PCA) algorithm is employed to extract significant features from the processed data. The CO is used to optimize hyperparameters and feature selection for the subsequent IAB network architecture, improving its performance in network intrusions detection by purposely developed Python code. The analysis of the findings is evaluated with metrics parameters such as Recall, Precision, Accuracy and F1- score. The results are cross validated to authenticate the contribution of the proposed prediction model. Experimental findings show that the recommended prediction model has outperformed conventional approaches in IDS to secure network data
A thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles, foreseeing an all-encompassing solution to modern network security issues.
Gaurav Kishor Saxena, Shambhu Dayal Sahu· International Journal of Cre...· 0 citations
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Ranobir Hasan, H. Jamal, Kamal Kamal et al.· The Eastasouth Journal of In...· 0 citations
Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS. This covers several detection methods, including signature-based, anomaly-based, specification-based, and behavior-based approaches, as well as their advantages and disadvantages in recognizing both existing and new cyber threats. The review also covers the architecture of NIDS which consists of network sensors, preprocessors, network traffic analysis, alert generation and security analysis. A variety of ML techniques, including supervised, unsupervised, semi-supervised, ensemble, and deep learning (DL) approaches, are being explored to improve the accuracy and adaptability of intrusion detection systems (IDS). Other applications such as DoS/DDoS attack detection, Malware detection, Botnets, Brute force attacks, Insider compromise, IoT compromise and Critical infrastructure threats are also shown. Despite all the challenges in terms of false positives, scalability, computational complexity, data quality, and novel attack styles, the features that ML can provide for intelligent, adaptive, and accurate intrusion detection systems are appealing.
Madhav Sharma· International Journal of Cyb...· 0 citations
New attacks are getting smarter and more sophisticated, so the old signature-based intrusion detection and prevention systems can't find them. This work proposes a machine learning approach to build a cybersecurity threat intelligence framework for effective multiclass intrusion detection, in which the Decision Tree classifier is used. The CICIDS2017 benchmark dataset, which contains both benign network traffic and several types of cyberattacks, is used to build and test the suggested model. The goal of the preparation process is to enhance classification performance by cleaning and separating data, utilizing Standard Scaler to scale features, and SMOTE to balance classes. Metrics like as recall, accuracy, precision, F1-score, confusion matrix, and ROC curve are used to test the Decision Tree model. An impressive 99.91% accuracy (ACC) rate, 97.79% precision (PRE), 97.08% recall (REC), 97.41% F1-score (F1), and 0.99 AUC were revealed by the experiment's outcomes. The suggested method beats state-of-the-art deep learning and ML approaches in terms of performance, execution time, and computational complexity. The results show that the suggested design is a reliable, efficient, and lightweight way to find cyber security threats and IDR apps with intelligence.
Madhav Sharma· International Journal of Cyb...· 0 citations
Highly accurate systems for detecting threats in real time are needed urgently owing to the exponential growth in cloud-network systems and increasingly sophisticated attacks. The conventional security systems using rules and signatures are inadequate in the changing environment of cloud computing because of evolving attacks.The suggested framework represents an intelligent solution for detecting and classifying threats in cloud computing by using smart machine learning algorithms. An intelligent system will collect data related to cloud network traffic and extract the features, and then it will use the supervisory learning algorithm to classify the threats. The experimental assessment has been performed based on a cloud intrusion detection dataset that consists of various types of attacks including network intrusion, malware, phishing, and data exfiltration. The implemented model had a total classification accuracy of 99.98% that proved to be very reliable with regard to detection of threats in which there are few false positives as well as false negatives. The findings confirm the assertion that the proposed framework offers real-time, scalable and effective security protection that is applicable in contemporary cloud-networks.
Pallapati Solmon, Shaik Khuran Bi, Yerram Lokeshreddy et al.· 2026 4th International Confe...· 0 citations
As interconnected devices increasingly transmit personal and sensitive data, security attacks are becoming more sophisticated and prevalent, highlighting the critical need for effective security solutions in Internet of Things (IoT) environments. An automated Network Intrusion Detection (NID) system plays a vital role in notifying system administrators of security breaches, acting as an efficient tool for protecting IoT networks from various threats. This study utilizes the UNSW-NB 15 dataset to enhance intrusion detection accuracy by addressing performance challenges and class imbalances within the data. We employ a combination of feature selection techniques, including Filter Method, Wrapper Method, and an Embedded approach using Lasso and Random Forest with Recursive Feature Elimination (RFE), alongside Pearson Correlation Coefficient (PCC). To tackle class imbalance, we apply the Synthetic Minority Over-sampling Technique (SOMTE). Various algorithms are implemented, including Random Forest, Decision Tree, AdaBoost, Bernoulli Naive Bayes, K-Nearest Neighbors, and Logistic Regression. Notably, the Stacking Classifier, which combines Boosted Decision Trees, Bagging with Random Forest, and LightGBM, demonstrates high performance in accurately detecting intrusions, significantly improving detection rates and reducing false alarms.
Rangu Shashidhar, M. Raju· International Journal of Eng...· 1 citation
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