Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.

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AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

1Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, Ohio, 2Department of Computer Science and Engineering, B V Raju Institute of Technology, Medak, India 3School of Computer Science and IT, Jain deemed to be University, Bengaluru, India 4Department of Mathematics, Chaudhary Charan Singh University, Meerut, India 5Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India 6Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia

A survey on the blockchain techniques for the Internet of Vehicles security

A kind of cyber-attack that severely paralyzes the victim server by injecting illegitimate packets of data is a DDoS attack, which is progressive in nature. Therefore its detection is a highly tedious task. Hence, IDS models are developed to detect this attack efficiently, based on machine learning algorithms such as C4.5, SVM, and KNN classifier algorithms and 10-fold cross validation techniques. The NSL-KDD bench mark dataset is employed to validate the models experimentally. A 10-fold cross validation technique is used to select the trend features, and ten trial runs are made to avoid biased output. The classic SVM classifier model reported better accuracy, but the precision and sensitivity of the C4.5 classifier algorithm are better than that of SVM and KNN models. In order to improve the performance of the machine learning based intrusion detection models, an attempt is made to feed the SVM and KNN based IDS model with the features selected by C4.5 classifier algorithm, and the obtained performance metric values are reported. It is evident from the results obtained that the hybrid combination of C4.5 with SVM out performed all other models discussed in this research with an accuracy of 0.9604.

DDoS Attack Detection Using Hybrid Machine Learning Based IDS Models

Improve the Security of Industrial Control System: A Fine-Grained Classification Method for DoS Attacks on Modbus/TCP

. The present era of technology improves the health care services and Wireless Body Area Network (WBAN) is one of them. It is a technology which uses wireless sensors to gather the vital signs from human body for monitoring. TheWBANisoftenconnectedwithcloudtoovercomeprocessingandstoragelim-itations.However,usingcloudwithWBANopensupthedoorforvariousattacks. DDoSisoneofthemajorthreatwhichdirectlyaffectstheavailabilityofpatient data,andharnesstheadoptionofcloud-assistedWBANtechnology.Therefore,in thisworkweproposeaapproachtodetectDDoSattackincloud-assistedWBAN andensurestheavailabilityofpatientsdata.Theproposedapproachisbasedon AdaptiveandSupremeAttributeSelectionwithStackedEnsembleClassiﬁcation (ASAS-SEC).Alltherequestsintendedtousethepatientdatastoredoncloudmust havetopassthroughASAS-SECmechanism.Therequestclassiﬁedasbenignare onlyallowedtoaccessthepatientdataandDDoSrequestsarepassedtoIntensive CareUnit(ICU),wherethesourceoftheattackisidentiﬁedandblocked.Publi-callyavailableNSL-KDDdatasetisutilizedtoevaluatetheproposedASAS-SEC approachandresultsshowsthatproposedapproachoutperformsotherstate-of-the-artapproachesandachievesclassiﬁcationaccuracyof98.86%,F1-Scoreof 98.3%, and false alarm of 0.017.

A Stacked Ensemble Method with Adaptive Attribute Selection to Detect DDoS Attack in Cloud-Assisted WBAN

Distributed Denial of Service (DDoS) attacks aims exhausting the target network with malicious traffic, which is a threat to the availability of the service. Many detection systems, specifically Intrusion Detection System (IDS) have been proposed throughout the last two decades as the Internet evolved, although users and organizations find it continuously challenging and defeated while dealing with DDoS. Though, IDS is the first point of defense for protecting critical networks against ever evolving issues of intrusive activities, however it should be up to date all the time to detect any anomalous behavior so that integrity, confidentiality and availability of the service can be preserved. But, the accuracy of new detection methods, techniques, algorithms heavily rely on the existence of well-designed datasets for training purposes and evaluation by creating the classifier model. In this work, experimentation has been carried out using major supervised classification algorithms to classify the DDoS attack accurately from the legitimate flows. Among all the classifier, tree-based classifiers and distance-based classifiers performed the best.

Maulik Gohil

Papers

Evaluation of Classification algorithms for Distributed Denial of Service Attack Detection