SDN Based DDoS Attack Detection System by Exploiting Ensemble Classification for Cloud Computing

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

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DoS and DDoS Attack Detection Using Deep Learning and IDS

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4a/10 ◽

2020 ◽

Vol 17 (4A) ◽

pp. 655-661

Author(s):

Mohammad Shurman ◽

Rami Khrais ◽

Abdulrahman Yateem

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Target System ◽

Online Systems ◽

Ddos Attack ◽

Long Short Term Memory ◽

Ddos Attack Detection

In the recent years, Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack has spread greatly and attackers make online systems unavailable to legitimate users by sending huge number of packets to the target system. In this paper, we proposed two methodologies to detect Distributed Reflection Denial of Service (DrDoS) attacks in IoT. The first methodology uses hybrid Intrusion Detection System (IDS) to detect IoT-DoS attack. The second methodology uses deep learning models, based on Long Short-Term Memory (LSTM) trained with latest dataset for such kinds of DrDoS. Our experimental results demonstrate that using the proposed methodologies can detect bad behaviour making the IoT network safe of Dos and DDoS attacks

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Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

10.26686/wgtn.17135222.v1 ◽

2021 ◽

Author(s):

◽

Abigail Koay

Keyword(s):

Information Sharing ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Traffic Classification ◽

Distributed Denial Of Service ◽

Ddos Attacks ◽

Low Intensity ◽

Ddos Attack ◽

Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

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