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dc.contributor.authorVedula, Vasudha
dc.contributor.authorLama, Palden
dc.contributor.authorBoppana, Rajendra V.
dc.contributor.authorTrejo, Luis A.
dc.date.accessioned2021-09-09T13:39:00Z
dc.date.available2021-09-09T13:39:00Z
dc.date.issued2021-08-30
dc.identifierdoi: 10.3390/electronics10172105
dc.identifier.citationElectronics 10 (17): 2105 (2021)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/677
dc.description.abstractDistributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub.
dc.titleOn the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers
dc.date.updated2021-09-09T13:39:00Z
dc.description.departmentComputer Science


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