Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review
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TLDR
The comparative study of various ML algorithms used in IDS for several applications such as fog computing, Internet of Things (IoT), big data, smart city, and 5G network is explored and their efficiency was measured and also compared along with the latest researches.About:
This article is published in Procedia Computer Science.The article was published on 2020-01-01 and is currently open access. It has received 113 citations till now. The article focuses on the topics: Intrusion detection system & Information security.read more
Citations
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Journal ArticleDOI
Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems
TL;DR: This work proposes a sequential approach and evaluates the performance of a Random Forest, a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset to suggest that anomaly detection can be better addressed from a sequential perspective.
Journal ArticleDOI
A New Malware Classification Framework Based on Deep Learning Algorithms
Omer Aslan,Abdullah Asim Yilmaz +1 more
TL;DR: In this paper, a novel deep learning-based architecture is proposed which can classify malware variants based on a hybrid model, which integrates two wide-ranging pre-trained network models in an optimized manner.
Journal ArticleDOI
Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey
Shakila Zaman,Khaled Alhazmi,Mohammed Aseeri,Muhammad R. Ahmed,Risala Tasin Khan,M. Shamim Kaiser,Mufti Mahmud +6 more
TL;DR: In this paper, a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats is presented, and open challenges and future research directions are addressed for the safeguard of the IoT network.
Journal ArticleDOI
Evaluation of Classification Algorithms for Intrusion Detection System: A Review
TL;DR: This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity.
Journal ArticleDOI
A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning
Raj Kumar Batchu,Hari Seetha +1 more
TL;DR: A new automatic detection methodology is developed by reducing the feature space, which in turn reduces overfitting and computational time of the model and shows that the GB model performed well compared to the state-of-the-art methods with an accuracy of 99.97 %.
References
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Journal ArticleDOI
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
TL;DR: Performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately and other metrics prove that Random Forest performs comparatively better.
Journal ArticleDOI
Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
TL;DR: A multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks and a modified K-means algorithm is proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers.
Journal ArticleDOI
Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges
TL;DR: The relationship of IoT and SG, a huge dynamic global network infrastructure of Internet-enabled entities with web services, and some IoT architectures in SG are talked about.
Journal ArticleDOI
Intrusion detection systems for IoT-based smart environments: a survey
TL;DR: A comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms, and deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT Architecture is provided.
Journal ArticleDOI
Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection
TL;DR: Models for intrusion detection are built by using machine learning classification algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest, and Experimental results shows that Random Forest Classifier out performs the other methods in identifying whether the data traffic is normal or an attack.
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