scholarly journals Privacy-Preserving Ddos Attack Detection using Cross-Domain: Challenges and Research

2019 ◽  
Vol 9 (1) ◽  
pp. 2373-2382
Keyword(s):  
Intrusion Detection ◽  
Privacy Preserving ◽  
Attack Detection ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Cross Domain ◽  
Ddos Attack Detection

In this paper, we present a review of on hand IDS (Intrusion Detection Techniques) for DDoS assaults. Interruption discovery framework is a well known and computationally costly task. We additionally clarify the essentials of interruption identification framework. We represent the present methodologies for Intrusion Detection framework. From the expansive assortment of proficient procedures that have been created we will look at the most significant ones. Their qualities and shortcomings are likewise researched. For reasons unknown, the conduct of the calculations is substantially more comparative as not out of the ordinary.

scholarly journals Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks

2018 ◽  
Vol 36 (3) ◽  
pp. 628-643 ◽  
Author(s):  
Liehuang Zhu ◽  
Xiangyun Tang ◽  
Meng Shen ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Privacy Preserving ◽  
Attack Detection ◽  
Software Defined Networks ◽  
Ddos Attack ◽  
Cross Domain ◽  
Ddos Attack Detection

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

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.

scholarly journals An Enhanced Deep Autoencoder-based Approach for DDoS Attack Detection

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Positive ◽  
Denial Of Service ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Ddos Attack ◽  
High Detection ◽  
Sparse Autoencoder ◽  
Ddos Attack Detection

Intrusion detection systems play a crucial role in preventing security threats and defending networks from attacks. Among the attacks, distributed Denial-of-Service (DDoS) attacks literally get into the network and, in addition, they are terribly troublesome to avoid. With the advent of unknown threats, traditional machine learning approaches are impacted by lower detection rates and higher false-positive rates. As a result, the DDoS detection system requires an over-performing machine learning classifier with minimal false-positive and high detection accuracy. In this context, we propose an Improved Deep Sparse Autoencoder-based Framework (EDSA) for DDoS Attack Detection with a cost minimization strategy. The sparse autoencoder is used for dataset extraction functionality, while the softmax layer is used for traffic classification as malicious or bengin. However, intrusion detection includes the risk elements of inaccurate prediction; hence, we have used research metrics such as accuracy, precision, detection rate and specificity for our model analysis. The proposed solution uses the CICDDoS 2019 datasets and demonstrates high detection accuracy with a much less false positives percentage.

scholarly journals DDOS Attack Detection And Handling Mechanism in WSN

2019 ◽  
Vol 8 (3) ◽  
pp. 4990-4993
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Sensor Nodes ◽  
Time To Failure ◽  
Detection Techniques ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Service Attack ◽  
Ddos Attack Detection ◽  
Mean Time

Wireless sensor network(WSN) uses in many distinct applications including real time event detection. Sensor nodes(SN) have limited energy associated with them that is required to be conserved. Once all the energy of the sensors is drained, then network dies. In addition sensor nodes are exposed to everyone hence SN is susceptible to attacks. Distributed denial of service attack is one of the common attacks caused by malicious attacker causing congestion and decay in lifetime of the network. DDOS attack floods network with the bogus requests. This causes the legitimate request to be avoided by the server due to lack of resources. Detection and prevention of such attacks thus becomes critical. This paper provides study of techniques used to detect DDOS attack along with suggest modification for improving classification accuracy in the detection techniques. in addition this paper also highlight other metrics such as mean time to failure and mean time between failure for improving the detection process.

scholarly journals Bit-and-Piece DDoS attack Detection based on the Statistical Metrics

2019 ◽  
Vol 9 (1S4) ◽  
pp. 48-55
Keyword(s):  
Service Providers ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Communication Service ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Novel Approach ◽  
Data Flows ◽  
Ddos Attack Detection ◽  
Statistical Metrics

on each successive day, the DDoS attacks are increasing, improving and becoming more critical than ever before. In 2018, CISCO predicted that DDoS attack traffics may reach to 3.1 billion during 2021. Bit and Piece DDoS attack is an emerging attacking technique was found and reported by Nexusguard. This attack mainly targets the communication service providers and it injects unwanted junk information in to the legitimate traffic and thus bypasses the detection techniques. This work is aimed to propose a novel approach for detecting bit and piece attack using statistical metrics. Here, the packet flow is monitored at every second and the variations in the data flows easily identified as an attack.

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