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...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 attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This 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 resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In 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 situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash 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.展开更多
Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based cach...Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.展开更多
基金The authors gratefully acknowledge the approval and the support of this research study by grant no.SCIA-2022-11-1551 from the Deanship of Scientific Research at Northern Border University,Arar,K.S.A.
文摘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 attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This 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 resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In 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 situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash 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.
文摘Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.
