Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-6· 0 citations· 24 references
Abstract
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.
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
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
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
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
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
In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to decrease in resource-limited environments. The paper presents the enhanced intrusion detection system based on deep learning architecture, which can be flexible, adaptive and as well maintain the high detection capability with confidence under changing or to-be changed network settings. The objectives of this and aforementioned also address the issue of avoiding strong overtting behavior by models during the transfer learning and even rich feature representation through the first-stage operation: moving to address and ideally preventing attacks rather than supporting other attacks. The work is tailored to the deployment of the light-weight and adaptive IDS design which is supposed to be large enough to work in real time on low-powered devices such as IoTs and edge devices that are nondominated in energy and computationally less demanding. Real-time adaptability of the model will be examined through operational deployment simulations. It is also expected that such simulations would take into account latency, throughput, and energy consumption of the IDS model. On the one hand, In a stage nested within the very last period of this research, the IDS model has been merged with Explainable AI technologies; now LIME and SHAP are also preserved to improve the interpretability of the model decisions and the level of decision-making. What kind of feature attributions are made with the intrusion data? How is Interpretability of the model evaluated in terms of fidelity, comprehensibility, and expert belief? Therefore, all the above-mentioned events will be a perfect example of how the technologically ingrained tasks, particularly in the technical discipline of security studies, can be wrapped into the very cognitive resource of human beings.
Krishna Kumar Tiwari· 2026 International Conferenc...· 0 citations