Proceedings ArticleDOI
Intrusion Detection Using Machine Learning and Deep Learning Techniques
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TLDR
This study handles the CIC DoS dataset to detect application based DoS attacks by using Random Forest, Extreme Gradient Boosting, Light Gradients Boosting Machine (LGBM), Gradientboosting, Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) algorithms.Abstract:
Unlike traditional Denial of Service (DoS) attacks, application layer DoS attacks are nearly undetectable at the network layer. CIC DoS is one of the intrusion detection dataset which includes application layer DoS attacks. Therefore in this study, we handle this dataset to detect application based DoS attacks by using Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting, Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) algorithms. The experimental results show that the performance of the LGBM based model is better than the other algorithms.read more
Citations
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Journal ArticleDOI
Security Threats, Defense Mechanisms, Challenges, and Future Directions in Cloud Computing
TL;DR: An architecture tutorial on cloud computing technology, including their essential characteristics, services models, deployment models, and cloud data center virtualization, and the various attacks in the cloud and privacy challenges is provided.
Journal ArticleDOI
Demystifying the role of public intrusion datasets: A replication study of DoS network traffic data
TL;DR: Assessing whether the attacks provided by public datasets are impactful on their targets shows a partial ineffectiveness, paving the way for the construction of more rigorous datasets, collected on documented and realistic server configurations and reflecting actual traffic conditions under normative operations and disruptive attacks.
Posted ContentDOI
Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques
TL;DR: The TP-IDS is designed in two phases for increasing accuracy, having Hadoop distributed file system underlying data storage & processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.
Book ChapterDOI
Performance Improvement of Intrusion Detection System for Detecting Attacks on Internet of Things and Edge of Things
Posted Content
Detecting Network Anomalies using Rule-based machine learning within SNMP-MIB dataset.
TL;DR: A network traffic system that relies on adopted dataset to differentiate the DOS attacks from normal traffic and the ICMP variables are implemented in the identification of ICMP attack, HTTP flood attack, and Slowloris at a high accuracy.
References
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Proceedings ArticleDOI
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
TL;DR: A reliable dataset is produced that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable and evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.
Journal ArticleDOI
Toward developing a systematic approach to generate benchmark datasets for intrusion detection
TL;DR: The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles.
Journal ArticleDOI
Deep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar,Mamoun Alazab,K. P. Soman,Prabaharan Poornachandran,Ameer Al-Nemrat,Sitalakshmi Venkatraman +5 more
TL;DR: A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.
Journal ArticleDOI
A Survey of Network-based Intrusion Detection Data Sets
TL;DR: In this article, the authors provide a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet-and flow-based network data in detail, identifying 15 different properties to assess the suitability of individual data sets.
Journal ArticleDOI
Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network
TL;DR: The experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the neural network structure.