Aug 2026· International Journal of Data Mining Techniques and Applications· 0 citations· 9 references
TL;DR
The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
Abstract
Intrusion Detection Systems (IDSs) play a critical role in protecting modern networks against increasingly sophisticated cyberattacks. However, traditional IDS approaches often suffer from limitations such as low detection accuracy, high false alarm rates, and poor efficiency when processing large-scale network traffic. To address these challenges, this study proposes a hybrid intrusion detection model that combines Random Forest (RF) and Support Vector Machine (SVM) algorithms to improve detection performance and reliability. The motivation for integrating RF and SVM lies in their complementary strengths: Random Forest provides robustness against overfitting and effective handling of high-dimensional data, while SVM offers strong classification capability for complex and non-linear attack patterns. The proposed hybrid RF–SVM model was evaluated using the CIC-IDS2017 dataset, which contains realistic labeled network traffic representing both normal activities and multiple cyberattack categories, including DDoS, brute force, infiltration, botnets, web attacks, and port scanning. Experimental results demonstrate that the proposed hybrid model achieved an accuracy of 94%, precision of 93%, recall of 94%, and F1-score of 94%, outperforming standalone RF and SVM models in intrusion detection performance. Although the false positive rate slightly increased, the proposed approach significantly improved detection sensitivity and reduced the likelihood of missed attacks. The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
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
With the proliferation of internet-connected infrastructures and the complexity of cyberattacks, cybersecurity and intelligent intrusion detection systems have become more and more critical. Intrusion detection datasets, however, are now highly imbalanced, and conventional machine learning models have become biased towards the majority of benign traffic, misclassifying minority attack classes. This paper introduces a Cost-Sensitive Forest (CS-Forest) approach to enhance the detection of minority attacks in the CSE-CIC-IDS2018 dataset. The proposed framework combines cost-sensitive learning, ensemble-based Random Forest classification, feature selection, and SHAP explainability analysis to boost the performance of intrusion detection and interpretability. Various machine learning algorithms such as Decision Tree, Random Forest, AdaBoost, and XGBoost were tested and compared based on accuracy, precision, recall, F1-score, ROC-AUC, false positive rate, and false negative rate. Experimental results proved that the proposed CS-Forest has excellent performance, with 99.81% accuracy, 99.55% recall, 99.61% F1-score, and 0.998 ROC-AUC, significantly enhancing the performance of minority attack detection and reduced false negatives. The framework learned meaningful and interpretable network traffic behaviors, which was also confirmed using SHAP analysis. The research suggests that future IDS systems should incorporate cost-sensitive learning and explainable AI techniques to ensure improved reliability, transparency, and deployment in the cybersecurity landscape.
Intrusion Detection Systems (IDS) constitute a critical component of contemporary cybersecurity frameworks; however, their performance is frequently hindered by the high dimensionality of network traffic data, which contributes to increased false alarm rates and computational inefficiencies in conventional feature selection approaches. To address these limitations, this study proposes a novel hybrid feature selection framework that integrates the Ant Colony Optimization (ACO) and the Cuttlefish Algorithm (CFA). The hybrid approach leverages the global search exploration capability of ACO alongside the local search refinement strength of CFA to improve feature optimization for intrusion detection tasks. The proposed ACO-CFA algorithm was extensively evaluated using the KDD Cup 99 benchmark dataset, while the effectiveness of the optimized feature subsets was assessed through three machine learning classifiers: Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). Experimental findings revealed outstanding classification performance across multiple data partitioning ratios. Among the evaluated models, the Random Forest classifier consistently demonstrated superior effectiveness, achieving an accuracy of up to 99.97% while maintaining an optimal balance between precision and recall. Although the DT classifier produced comparable accuracy with faster computational performance, the SVM exhibited substantially higher computational costs despite its strong predictive capability. The study concludes that the hybrid ACO-CFA framework provides an efficient and scalable solution for feature selection, significantly improving IDS detection accuracy while minimizing computational complexity. Consequently, the proposed approach offers a robust foundation for the development of adaptive and high-performance intrusion detection systems capable of addressing increasingly sophisticated cyber threats.
Opeyemi Lateef USMAN· Journal of Basics and Applie...· 0 citations
Cyber attacks are growing in number and complexity. Modern networks faces various real cyber threats such as API, DDPS, ICMP,UDP, TCP, botnet, Bit LINK kind of attacks. Intrusion detection system depends on machine learning for detect these attacks, but they faces various challenges in present scenario like un wanted data , poisoned data , stale data and privacy risk. Machine unlearning MU provides reliable and trust full solution by allowing various kind of latest model to remove harmful, unwanted, outdated data. This paper presents comprehensive survey of recent studies on machine UN learning applied to intrusion detection system IDS. We analyzed various approaches for unlearning time optimize, model accuracy, attacks types, and computational efficiency. the study highlight bets practices , performance trends, research gaps, time optimization , model performance accuracy , providing a roadmap for future development of high-accuracy, adaptive IDS frameworks. This paper provides researchers and practitioners with: (1) a structured, critical appraisal of the MU-IDS landscape; (2) quantitative benchmarks for cross-method comparison; (3) identification of unresolved challenges and adversarial threat models; and (4) concrete future research directions toward practical, privacy-compliant, and adversarially robust intrusion detection systems.
Sarmistha Podder, Saptarshi Paul· International journal of com...· 0 citations
With the widespread adoption of cloud computing, securing enterprise networks against cyber threats has become increasingly important. Cloud environments are highly dynamic and constantly changing, making them susceptible to sophisticated cyberattacks that traditional Intrusion Detection Systems (IDS) often fail to detect. This study focuses on Intelligent Intrusion Detection Systems (IIDS) and their critical role in strengthening cloud security. Unlike conventional signature-based IDS that rely on fixed attack patterns, IIDS employ advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques including deep learning, decision trees, and ensemble models to identify both known and emerging threats with greater accuracy. The paper proposes an integrated framework that combines real-time anomaly detection with automated response capabilities for cloud networks. Key architectural elements of IIDS are examined, alongside major deployment challenges such as scalability, false-positive rates, and computational requirements. Additionally, practical case studies and performance evaluations illustrate how IIDS enhance threat detection by improving accuracy, adaptability, and efficiency. Finally, the paper outlines future research directions to further advance IIDS capabilities and address the evolving security needs of modern cloud infrastructures.
R. Velu· 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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