Aug 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
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.
Abstract
Networks are rapidly and inevitably becoming complex, also the number of cyber threats is rising. Technology that uses intrusion quote elimination methods is essential to most networked forms for intentional analysis. The paper provides 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. The method uses both abnormality detection, which is extremely useful for identifying novel and 0-day attacks, and misuse detection, which is more effective in the detection of known attack patterns. Support Vector Machines is applied in building the system that achieves excellent accuracy in classification of network traffic, and at the same time reducing false negative and false positive rates. The hybrid model is the approach through which data-driven analytics is performed on raw network traffic data to extract relevant features, which are then used at run-time for classification by use of SVM. Anomaly detection aims at discovering deviations originating from standard network behavior, whereas misuse detection employs aspects of known signatures attack. This anomaly or misuse based systems outperform state-of-the-art models with respect to accuracy 97.1 in all other parameters such as precision, recall, as well as the f1 score. Among the other positive aspects of this work is the possible use of SVM in hybrid intrusion detection frameworks foreseeing an all-encompassing solution to modern network security issues. The research will further consider utilization of adaptive learning in order to improve persistency of the system against learning to new threats.
Keywords:- Behavioral Anomaly Detection, Explainable Artificial Intelligence, Ensemble Learning, Anomaly Detection, Attack Classification
The rapid expansion and spread of networked systems and digital services has tremendously expanded the complexity and frequency of cyberattacks, and conventional security tools are no longer relevant to contemporary cyber threats. Intrusion Detection Systems (IDS) are very important in detection of malicious activities, but the traditional signature based and rule-based IDS are limited in that they have high false-positive, cannot be able to detect the attacks of the zeroday, and fail to be adapted to changing patterns of threats. The recent developments in machine learning (ML) have brought intelligent and adaptive methods that can learn the complicated patterns based on large volumes of network traffic data. This paper provides an in-depth analysis of effective machine learning methods to intrusion detection system in cybersecurity. The paper compares the efficacy of supervised, unsupervised and ensemble-based ML algorithms that conduct intrusion detection with enhanced accuracy, lowered computation load, and improved scalability. It focuses on the feature selection, dimensionality reduction, and model optimization to enhance the detecting performance and retain the capability of running it in real-time. In the results, the hybrid and ensemble models of machine learning prove to be much more efficient than the conventional IDS methods and provide a strong protection against the current cyber threats. This research contributes toward developing intelligent, adaptive, and efficient IDS frameworks suitable for contemporary and future cybersecurity infrastructures.
K. Ashwini, M. Supriya· 2026 5th International Confe...· 0 citations
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
Moulay Ibrahim El-Khalil Ghembaza· International journal of Com...· 0 citations
Modern computer networks face a persistent and growing range of cyber threats, including unauthorized access, malware infiltration, denial-of-service attacks, and data exfiltration. Conventional signature- and rule-based intrusion detection systems (IDS) struggle to identify zero-day attacks and evolving intrusion patterns as networks expand in scale and complexity, and they typically require frequent manual updates to remain effective. This paper presents a machine learning–based intrusion detection system capable of automatically analyzing network traffic and distinguishing malicious activity from legitimate behavior. By learning from historical traffic data, the proposed system adapts to emerging threats with greater accuracy than static, rule-driven approaches. The system is implemented in Python using established data-analytics and machine-learning libraries, and it evaluates several supervised classifiers—Random Forest, Support Vector Machine, Naïve Bayes, and Logistic Regression—to categorize network traffic as normal or intrusive. Experimental results show that the Random Forest classifier consistently achieves the strongest overall performance, and the integration of real-time packet capture with a visualization dashboard enables continuous, interpretable monitoring. The findings demonstrate that machine learning offers a scalable, adaptive, and largely automated foundation for securing contemporary network infrastructures.
Mohit kumar saini Mohit kumar saini, Dhanna Ram Dhanna Ram, Pooja Verma Pooja Verma· 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
The increasing complexity and frequency of cyberattacks have heightened the need for intrusion detection systems capable of recognizing threats that extend beyond predefined signatures. Traditional signature-based IDS solutions are often insufficient for detecting newly emerging or evolving attack patterns. This work presents a supervised machine learning approach for network intrusion detection, employing the NSL-KDD dataset to classify network traffic as either normal or malicious. The study investigates the performance of several classifiers, comprising Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The proposed framework incorporates key preprocessing procedures such as feature normalization, dimensionality reduction, and attribute filtering to enhance model robustness. The proposed models are evaluated using accuracy, precision, recall, and F1-score, with special consideration given to minimizing false positives to enhance real-time detection effectiveness. Experimental findings demonstrate that supervised learning models exhibit improved detection capability and better adaptability to unfamiliar attack behaviors. The results highlight the potential of machine learning techniques as scalable and effective components of modern network security architectures.
S. Sachin, Shifana Begum· 2026 5th International Confe...· 0 citations
In Cybersecurity, Intrusion detection system is a tool that is used to identify the abnormal activity on the network in a timely manner. This is done by inspecting and analyzing the features of network packets to find any anomaly in them. In high dimensional traffic, the intrusion detection system needs to inspect and analyze all the features of a network packet to make the decision, which is an overhead. Feature reduction techniques are used to map the existing feature set to a new but less feature space to make the intrusion detection system more efficient. In this work we compared the performance of three commonly used feature reduction techniques, viz. Principle Component Analysis, Independent Component Analysis and Linear Discriminant Analysis. We used k-Nearest Neighbor and Naïve Bayes algorithm as classifiers. It was observed that Linear Discriminant Analysis resulted in improved accuracy of 95-100% and reduced false alarms between 0-0.5%.