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Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

Computers, Materials & Continua 2021年第10期69卷 373-391页

作者： Mohammad Aladaileh Mohammed Anbar Iznan H.Hasbullah yousef k.sanjalawe Yung-Wey Chong National Advanced IPv6 Centre of Excellence Universiti Sains MalaysiaPenangMalaysia Department of Computer Sciences Northern Border UniversityAr’arKingdom of Saudi Arabia

The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network *** SDN provides a perfect platform for researchers to solve traditional network’s outstanding ***,despite the advantages of centralized control,concern about its security is *** more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire *** that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive *** paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or *** proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying *** two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.

关键词： Software-defined networking DDoS attack distributed denial of service Rényi joint entropy

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DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

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Computers, Materials & Continua 2023年第5期75卷 3571-3588页

作者： yousef sanjalawe Turke Althobaiti Cybersecurity Department Faculty of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan Computer Science Department PY CollageNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Computer Science Department Faculty of ScienceNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Remote Sensing Northern Border University(NBU) 9280 NBUAr’arSaudi Arabia

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few *** the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest *** the CC environment,Distributed Denial of Service(DDoS)attacks are *** cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial *** various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic ***,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack ***,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),krill Hird(kH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,*** a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

关键词： CIC IDS 2017 cloud computing distributed denial of service ensemble feature selection intrusion detection system

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Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

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Computer Systems Science & Engineering 2023年第10期47卷 453-469页

作者： Turke Althobaiti yousef sanjalawe Naeem Ramzan Department of Computer Science Faculty of ScienceNorthern Border University(NBU)Arar73222Saudi Arabia Remote Sensing Unit Northern Border University(NBU)Arar73222Saudi Arabia Deparment of Cybersecurity American University of Madaba(AUM)Amman11821Jordan School of Engineering and Computing University of West of ScotlandPaisleyPA12BEUK

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS *** advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to *** it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate *** is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or ***,identifying these types of attacks on web servers has become crucial,particularly in the *** this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life *** proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 *** crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

关键词： Cloud computing CNN flash crowd attack intrusion detection system LightGBM White Shark Optimizer

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