Performance evaluation of Machine learning algorithms for Intrusion Detection System

• URL: http://arxiv.org/abs/2310.00594v1
• Date: Sun, 1 Oct 2023 06:35:37 GMT
• Title: Performance evaluation of Machine learning algorithms for Intrusion Detection System
• Authors: Sudhanshu Sekhar Tripathy, Bichitrananda Behera,
• Abstract summary: This paper focuses on intrusion detection systems (IDSs) analysis using Machine Learning (ML) techniques. We analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models.
• Score: 0.40964539027092917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The escalation of hazards to safety and hijacking of digital networks are among the strongest perilous difficulties that must be addressed in the present day. Numerous safety procedures were set up to track and recognize any illicit activity on the network's infrastructure. IDS are the best way to resist and recognize intrusions on internet connections and digital technologies. To classify network traffic as normal or anomalous, Machine Learning (ML) classifiers are increasingly utilized. An IDS with machine learning increases the accuracy with which security attacks are detected. This paper focuses on intrusion detection systems (IDSs) analysis using ML techniques. IDSs utilizing ML techniques are efficient and precise at identifying network assaults. In data with large dimensional spaces, however, the efficacy of these systems degrades. correspondingly, the case is essential to execute a feasible feature removal technique capable of getting rid of characteristics that have little effect on the classification process. In this paper, we analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models. Then, we implement ML classifiers such as Logistic Regression, Decision Tree, K-Nearest Neighbour, Naive Bayes, Bernoulli Naive Bayes, Multinomial Naive Bayes, XG-Boost Classifier, Ada-Boost, Random Forest, SVM, Rocchio classifier, Ridge, Passive-Aggressive classifier, ANN besides Perceptron (PPN), the optimal classifiers are determined by comparing the results of Stochastic Gradient Descent and back-propagation neural networks for IDS, Conventional categorization indicators, such as "accuracy, precision, recall, and the f1-measure, have been used to evaluate the performance of the ML classification algorithms.

Related papers

• Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers [1.6001193161043425]
  Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. This research addresses the sophisticated adversarial manipulations posed by attackers, aiming to undermine machine learning-based botnet detection systems. We introduce a flow-based detection approach, leveraging machine learning and deep learning algorithms trained on the ISCX and ISOT datasets.
  arXiv  Detail & Related papers  (2024-09-01T08:53:21Z)
• Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
  Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
  arXiv  Detail & Related papers  (2024-07-08T09:18:59Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction [6.374540518226326]
  Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. This paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding (PCA) for dimension reduction. Using the CIC-IDS 2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS 2018 dataset.
  arXiv  Detail & Related papers  (2024-01-22T05:49:41Z)
• Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification [0.0]
  Intrusion Detection Systems (IDSs) are security devices designed to identify and mitigate attacks to modern networks. Data-driven approaches based on Machine Learning (ML) have gained more and more popularity for executing the classification tasks. However, typical ML models adopted for this purpose do not properly take into account the uncertainty associated with their prediction.
  arXiv  Detail & Related papers  (2023-09-05T13:52:41Z)
• OMINACS: Online ML-Based IoT Network Attack Detection and Classification System [0.0]
  This paper proposes an online attack detection and network traffic classification system. It combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. It can detect the presence of malicious traffic flows and classify them according to the type of attack they represent.
  arXiv  Detail & Related papers  (2023-02-18T04:06:24Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• GIM: Gaussian Isolation Machines [40.7916016364212]
  In many cases, neural network classifiers are exposed to input data that is outside of their training distribution data. We present a novel hybrid (generative-discriminative) classifier aimed at solving the problem arising when OOD data is encountered. The proposed GIM's novelty lies in its discriminative performance and generative capabilities, a combination of characteristics not usually seen in a single classifier.
  arXiv  Detail & Related papers  (2020-02-06T09:51:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.