Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
There are different types of Cyber Security Attacks that are based on ICMP protocols. Many ICMP protocols are very similar, which may lead security managers to think they may have same impact on victim computer system...There are different types of Cyber Security Attacks that are based on ICMP protocols. Many ICMP protocols are very similar, which may lead security managers to think they may have same impact on victim computer systems or servers. In this paper, we investigate impact of different ICMP based security attacks on two popular server systems namely Microsoft’s Windows Server and Apple’s Mac Server OS running on same hardware platform, and compare their performance under different types of ICMP based security attacks.展开更多
Genetic studies have revealed that variants in genes that encode regulators of the complement system are major risk factors for the development of age-related macular degeneration(AMD).The biochemical consequences of ...Genetic studies have revealed that variants in genes that encode regulators of the complement system are major risk factors for the development of age-related macular degeneration(AMD).The biochemical consequences of the common polymorphism in complement factor H(Tyr402His)include increased formation of the membrane attack complex(MAC),which is deposited at the level of the inner choroid and choriocapillaris.Whereas the MAC is normally protective against foreign pathogens,it can also damage resident bystander cells when it is insufficiently regulated.Indeed,human maculas with early AMD show loss of endothelial cells in the choriocapillaris,the principal site of MAC activation.Modeling of MAC injury of choroidal endothelial cells in vitro reveals that these cells are susceptible to cell lysis by the MAC,and that unlysed cells alter their gene expression profile to undergo a pro-angiogenic phenotype that includes increased expression of matrix metalloproteinase-9.Strategies for protecting choriocapillaris endothelial cells from MAC-mediated lysis and for replacing lysed endothelial cells will be discussed.展开更多
Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anyt...Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.展开更多
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
文摘There are different types of Cyber Security Attacks that are based on ICMP protocols. Many ICMP protocols are very similar, which may lead security managers to think they may have same impact on victim computer systems or servers. In this paper, we investigate impact of different ICMP based security attacks on two popular server systems namely Microsoft’s Windows Server and Apple’s Mac Server OS running on same hardware platform, and compare their performance under different types of ICMP based security attacks.
文摘Genetic studies have revealed that variants in genes that encode regulators of the complement system are major risk factors for the development of age-related macular degeneration(AMD).The biochemical consequences of the common polymorphism in complement factor H(Tyr402His)include increased formation of the membrane attack complex(MAC),which is deposited at the level of the inner choroid and choriocapillaris.Whereas the MAC is normally protective against foreign pathogens,it can also damage resident bystander cells when it is insufficiently regulated.Indeed,human maculas with early AMD show loss of endothelial cells in the choriocapillaris,the principal site of MAC activation.Modeling of MAC injury of choroidal endothelial cells in vitro reveals that these cells are susceptible to cell lysis by the MAC,and that unlysed cells alter their gene expression profile to undergo a pro-angiogenic phenotype that includes increased expression of matrix metalloproteinase-9.Strategies for protecting choriocapillaris endothelial cells from MAC-mediated lysis and for replacing lysed endothelial cells will be discussed.
文摘Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.
