Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the kno...Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.展开更多
A space called Unmanned Aerial Vehicle(UAV)cyber is a new environment where UAV,Ground Control Station(GCS)and business processes are integrated.Denial of service(DoS)attack is a standard network attack method,especia...A space called Unmanned Aerial Vehicle(UAV)cyber is a new environment where UAV,Ground Control Station(GCS)and business processes are integrated.Denial of service(DoS)attack is a standard network attack method,especially suitable for attacking the UAV cyber.It is a robust security risk for UAV cyber and has recently become an active research area.Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network.In addition,the honeypot,an effective security vulnerability defense mechanism,has not been widely adopted or modeled for defense against DoS attack UAV cyber.With this motivation,the current research paper presents a honeypot game theorymodel that considersGCS andDoS attacks,which is used to study the interaction between attack and defense to optimize defense strategies.The GCS and honeypot act as defenses against DoS attacks in this model,and both players select their appropriate methods and build their benefit function models.On this basis,a hierarchical honeypot and G2A network delay reward strategy are introduced so that the defender and the attacker can adjust their respective strategies dynamically.Finally,by adjusting the degree of camouflage of the honeypot for UAV network services,the overall revenue of the defender can be effectively improved.The proposed method proves the existence of a mixed strategy Nash equilibrium and compares it with the existing research on no delay rewards and no honeypot defense scheme.In addition,this method realizes that the UAV cyber still guarantees a network delay of about ten milliseconds in the presence of a DoS attack.The results demonstrate that our methodology is superior to that of previous studies.展开更多
文摘Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.
基金Basic Scientific Research program of China JCKY2020203C025 funding is involved in this study.
文摘A space called Unmanned Aerial Vehicle(UAV)cyber is a new environment where UAV,Ground Control Station(GCS)and business processes are integrated.Denial of service(DoS)attack is a standard network attack method,especially suitable for attacking the UAV cyber.It is a robust security risk for UAV cyber and has recently become an active research area.Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network.In addition,the honeypot,an effective security vulnerability defense mechanism,has not been widely adopted or modeled for defense against DoS attack UAV cyber.With this motivation,the current research paper presents a honeypot game theorymodel that considersGCS andDoS attacks,which is used to study the interaction between attack and defense to optimize defense strategies.The GCS and honeypot act as defenses against DoS attacks in this model,and both players select their appropriate methods and build their benefit function models.On this basis,a hierarchical honeypot and G2A network delay reward strategy are introduced so that the defender and the attacker can adjust their respective strategies dynamically.Finally,by adjusting the degree of camouflage of the honeypot for UAV network services,the overall revenue of the defender can be effectively improved.The proposed method proves the existence of a mixed strategy Nash equilibrium and compares it with the existing research on no delay rewards and no honeypot defense scheme.In addition,this method realizes that the UAV cyber still guarantees a network delay of about ten milliseconds in the presence of a DoS attack.The results demonstrate that our methodology is superior to that of previous studies.
