Machine learning techniques for accurate classification and detection of intrusions in computer network
Mutyalaiah Paricherla,Mahyudin Ritonga,Sandip R. Shinde,Smita M. Chaudhari,Rahmat Linur,Abhishek Raghuvanshi +5 more
7
TL;DR: In this paper , a combination of ant colony optimization (ACO) and the firefly approach for feature selection is proposed for intrusion detection, which is able to distinguish between normal and abnormal behaviors that are included within the dataset.
read more
Abstract: An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent the defences that are in place. Training and examination are the two basic components that make up the intrusion detection system (IDS) and each one may be analysed separately. During training, a number of distinct models are built, each of which is able to distinguish between normal and abnormal behaviours that are included within the dataset. This article proposes a combination of ant colony optimization (ACO) and the firefly approach for feature selection. The final outcome of giving careful thought to the selection of features will eventually result in greater accuracy of categorisation. When classifying various sorts of features, we make use of a wide variety of machine learning (ML) algorithms, including AdaBoost, gradient boost, and Bayesian network (BN), amongst others. The tests and assessments made use of data obtained from three distinct datasets, namely NSL-KDD, UNSW-NB15, and CICIDS 2017. The degree of performance of an individual may be broken down into its component parts, which include the F1 score, accuracy, precision, and recall. Gradient boost performs far better when it comes to recognising and classifying incursions.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks
Muhammad Muhammad Inuwa,Resul Das +1 more
TL;DR: This study compares machine learning methods (SVM, ANN, DT, LR, k-NN) for anomaly detection in IoT cyber attacks, finding that neural networks outperform other models, providing valuable insights for cybersecurity experts to develop robust protection strategies for IoT ecosystems.
44
Intrusion Detection System with Ensemble Machine Learning Approaches using VotingClassifier
Karuna Bagde,Atul D. Raut +1 more
TL;DR: This paper uses Intrusion Detection System with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions.
1
Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review
Akmal A. Younis,Adnan Mohsin Abdulazeez +1 more
TL;DR: The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.
Machine learning approach for intrusion detection system using dimensionality reduction
Deepa Manikandan,Jayaseelan Dhilipan +1 more
TL;DR: DR-DBMS is a machine learning-based intrusion detection system that improves accuracy and reduces the number of features using dimensionality reduction techniques.
Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network
Nor Paezah Abdullah,Murizah Kassim,Sayang Mohd Deni,Yusnani Mohd Yussoff +3 more
TL;DR: Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network reveals heavy-tailed distributions and skewed data on inbound and outbound packets and bytes. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB respectively. Protocol 6 TCP presents the highest amount of traffic and a weak positive correlation exists between the inbound and outbound packets and bytes on the network.
References
A survey of network anomaly detection techniques
TL;DR: This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering and evaluates effectiveness of different categories of techniques.
1.4K
A comparative analysis of gradient boosting algorithms
TL;DR: A comprehensive comparison between XGBoost, LightGBM, CatBoost, random forests and gradient boosting has been performed and indicates that CatBoost obtains the best results in generalization accuracy and AUC in the studied datasets although the differences are small.
A survey of deep learning-based network anomaly detection
TL;DR: An overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neuralnetwork, as well as the machine learning techniques relevant to network anomaly detection are presented.
784
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset
TL;DR: The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy and can well describe the network traffic behavior and improve the ability of anomaly detection effectively.
Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection
TL;DR: Experimental results show that the proposed hybrid dimensionality reduction method with the ensemble of the base learners contributes more critical features and significantly outperforms individual approaches, achieving high accuracy and low false alarm rates.
303