As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
Link flooding attack(LFA)is a type of covert distributed denial of service(DDoS)attack.The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet.Recently,t...Link flooding attack(LFA)is a type of covert distributed denial of service(DDoS)attack.The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet.Recently,the proliferation of Internet of Things(IoT)has increased the quantity of vulnerable devices connected to the network and has intensified the threat of LFAs.In LFAs,attackers typically utilize low-speed flows that do not reach the victims,making the attack difficult to detect.Traditional LFA defense methods mainly reroute the attack traffic around the congested link,which encounters high complexity and high computational overhead due to the aggregation of massive attack traffic.To address these challenges,we present an LFA defense framework which can mitigate the attack flows at the border switches when they are small in scale.This framework is lightweight and can be deployed at border switches of the network in a distributed manner,which ensures the scalability of our defense system.The performance of our framework is assessed in an experimental environment.The simulation results indicate that our method is effective in detecting and mitigating LFAs with low time complexity.展开更多
A data breach can seriously impact organizational intellectual property,resources,time,and product value.The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/I...A data breach can seriously impact organizational intellectual property,resources,time,and product value.The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols.As TCP relies on IP addresses,an attacker may easily trace the IP address of the organization.Given that many organizations run the risk of data breach and cyber-attacks at a certain point,a repeatable and well-developed incident response framework is critical to shield them.Enterprise cloud possesses the challenges of security,lack of transparency,trust and loss of controls.Technology eases quickens the processing of information but holds numerous risks including hacking and confidentiality problems.The risk increases when the organization outsources the cloud storage services through the vendor and suffers from security breaches and need to create security systems to prevent data networks from being compromised.The business model also leads to insecurity issues which derail its popularity.An attack mitigation system is the best solution to protect online services from emerging cyber-attacks.This research focuses on cloud computing security,cyber threats,machine learning-based attack detection,and mitigation system.The proposed SDN-based multilayer machine learning-based self-defense system effectively detects and mitigates the cyber-attack and protects cloud-based enterprise solutions.The results show the accuracy of the proposed machine learning techniques and the effectiveness of attack detection and the mitigation system.展开更多
The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave ind...The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave independently of others.IoT devices,however,are also vulnerable,mostly because they lack the essential built-in security to thwart attackers.It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment.As a result,the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT,such as Cloud computing,machine learning techniques,and lightweight encryption techniques.This paper presents a detailed analysis of the security requirements,attack surfaces,and security solutions available for IoT networks and suggests an innovative IoT architecture.The Seven-Layer Architecture in IoT provides decent attack detection accuracy.According to the level of risk they pose,the security threats in each of these layers have been properly categorized,and the essential evaluation criteria have been developed to evaluate the various threats.Also,Machine Learning algorithms like Random Forest and Support Vector Machines,etc.,and Deep Learning algorithms like Artificial Neural Networks,Q Learning models,etc.,are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.展开更多
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601in part by the National Natural Science Foundation of China(Grant No.62201605,62341110,U22A2002)in part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘Link flooding attack(LFA)is a type of covert distributed denial of service(DDoS)attack.The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet.Recently,the proliferation of Internet of Things(IoT)has increased the quantity of vulnerable devices connected to the network and has intensified the threat of LFAs.In LFAs,attackers typically utilize low-speed flows that do not reach the victims,making the attack difficult to detect.Traditional LFA defense methods mainly reroute the attack traffic around the congested link,which encounters high complexity and high computational overhead due to the aggregation of massive attack traffic.To address these challenges,we present an LFA defense framework which can mitigate the attack flows at the border switches when they are small in scale.This framework is lightweight and can be deployed at border switches of the network in a distributed manner,which ensures the scalability of our defense system.The performance of our framework is assessed in an experimental environment.The simulation results indicate that our method is effective in detecting and mitigating LFAs with low time complexity.
基金Deanship of Scientific Research at Majmaah University for supporting this work under Project No.RGP-2019-27.
文摘A data breach can seriously impact organizational intellectual property,resources,time,and product value.The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols.As TCP relies on IP addresses,an attacker may easily trace the IP address of the organization.Given that many organizations run the risk of data breach and cyber-attacks at a certain point,a repeatable and well-developed incident response framework is critical to shield them.Enterprise cloud possesses the challenges of security,lack of transparency,trust and loss of controls.Technology eases quickens the processing of information but holds numerous risks including hacking and confidentiality problems.The risk increases when the organization outsources the cloud storage services through the vendor and suffers from security breaches and need to create security systems to prevent data networks from being compromised.The business model also leads to insecurity issues which derail its popularity.An attack mitigation system is the best solution to protect online services from emerging cyber-attacks.This research focuses on cloud computing security,cyber threats,machine learning-based attack detection,and mitigation system.The proposed SDN-based multilayer machine learning-based self-defense system effectively detects and mitigates the cyber-attack and protects cloud-based enterprise solutions.The results show the accuracy of the proposed machine learning techniques and the effectiveness of attack detection and the mitigation system.
文摘The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave independently of others.IoT devices,however,are also vulnerable,mostly because they lack the essential built-in security to thwart attackers.It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment.As a result,the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT,such as Cloud computing,machine learning techniques,and lightweight encryption techniques.This paper presents a detailed analysis of the security requirements,attack surfaces,and security solutions available for IoT networks and suggests an innovative IoT architecture.The Seven-Layer Architecture in IoT provides decent attack detection accuracy.According to the level of risk they pose,the security threats in each of these layers have been properly categorized,and the essential evaluation criteria have been developed to evaluate the various threats.Also,Machine Learning algorithms like Random Forest and Support Vector Machines,etc.,and Deep Learning algorithms like Artificial Neural Networks,Q Learning models,etc.,are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.
