Distributed Intrusion Detection System for Cloud Environments based on Data Mining techniques

Idhammad, Mohamed; Afdel, Karim; Belouch, Mustapha

doi:10.1016/j.procs.2018.01.095

Cited by 114 publications

(46 citation statements)

References 11 publications

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“…Distributed machine learning based IDS has been proposed by Idhammad et al for cloud environment [24]. The preprocessed data is used by anomaly detection module which uses NB classifier for separating the network traffic data into normal or abnormal traffic.…”

Section: Related Workmentioning

confidence: 99%

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

2020

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Cloud computing has very attractive features like elastic, on demand and fully managed computer system resources and services. However, due to its distributed and dynamic nature as well as vulnerabilities in virtualization implementation, the cloud environment is prone to various cyber-attacks and security issues related to cloud model. Some of them are inability to access data coming to and from cloud service, theft and misuse of data hosted, no control over sensitive data access, advance threats like malware injection attack, wrapping attacks, virtual machine escape, distributed denial of service attack (DDoS) etc. DDoS is one of the notorious attack. Despite a number of available potential solutions for the detection of DDoS attacks, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors, necessitate the development of improved detection approaches. This paper proposes a novel architecture that combines a well posed stacked sparse AutoEncoder (AE) for feature learning with a Deep Neural Network (DNN) for classification of network traffic into benign traffic and DDoS attack traffic. AE and DNN are optimized for detection of DDoS attacks by tuning the parameters using appropriately designed techniques. The improvements suggested in this paper lead to low reconstruction error, prevent exploding and vanishing gradients, and lead to smaller network which avoids overfitting. A comparative analysis of the proposed approach with ten state-of-the-art approaches using performance metrics-detection accuracy, precision, recall and F1-Score, has been conducted. Experiments have been performed on CICIDS2017 and NSL-KDD standard datasets for validation. Proposed approach outperforms existing approaches over the NSL-KDD dataset and yields competitive results over the CICIDS2017 dataset.

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Section: Related Workmentioning

confidence: 99%

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

2020

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“…Idhammad, Afdel and Belouch, [27], implemented a distributed intrusion detection system for Cloud environments. At first, they used the Naive Bayes model for anomaly detection and data preprocessing and then, for the multi-classification, they used a classifier based on Random Forest, that detects the type of each attack.…”

Section: Related Workmentioning

confidence: 99%

An Intrusion Detection System for Multi-class Classification Based on Deep Neural Networks

Toupas

Chamou

Giannoutakis

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Intrusion Detection Systems (IDSs) are considered as one of the fundamental elements in the network security of an organisation since they form the first line of defence against cyber threats, and they are responsible to effectively a potential intrusion in the network. Many IDS implementations use flowbased network traffic analysis to detect potential threats. Network security research is an ever-evolving field and IDSs in particular have been the focus of recent years with many innovative methods proposed and developed. In this paper, we propose a deep learning model, more specifically a neural network consisting of multiple stacked Fully-Connected layers, in order to implement a flow-based anomaly detection IDS for multi-class classification. We used the updated CICIDS2017 dataset for training and evaluation purposes. The experimental outcome using MLP for intrusion detection system, showed that the proposed model can achieve promising results on multi-class classification with respect to accuracy, recall (detection rate), and false positive rate (false alarm rate) on this specific dataset.

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“…Different techniques and strategies to resolve IDS shortcomings such as high false alert rates, poor reliability, and time consuming have been introduced in recent decades. A Hybrid Intrusion Identification (HII) method [19] based on DT and KNN is proposed in this paper. A functional selection method is used to extract structured information from NSL-KDD data set to improve the performance of the proposed approach.…”

Section: Literature Surveymentioning

confidence: 99%

Intrusion Detection System using Datamining Based Enhanced Framework

2019

IJITEE

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With the significant increase in the use of computers over the network and the development of applications on different platforms, the focus is on network security. The identification of multiple attacks is actually an important element of network security. The role of the IDS is to track and prevent unauthorized use or damage to network resources and systems. An intrusion detection system using Datamining Based Enhanced Framework (DEF) is presented in this paper. The model is assisted by the K-mean Clustering and Decision Tree (DT) classification techniques in which genetic algorithms (GA) for clusters, max runs and confidence can be used. The experimental results shows the promising outcome of the proposed Datamining Based Enhanced Framework (DEF).

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Distributed Intrusion Detection System for Cloud Environments based on Data Mining techniques

Cited by 114 publications

References 11 publications

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

An Intrusion Detection System for Multi-class Classification Based on Deep Neural Networks

Intrusion Detection System using Datamining Based Enhanced Framework

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