The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, rese...The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, research data, personal data, proprietary knowledge, complex equipment designs and blueprints for yet to be patented products has all become extremely susceptible to Cybersecurity attacks. This research will investigate factors that affect that may have an influence on perceived ease of use of Cybersecurity, the influence of perceived ease of use on the attitude towards using Cybersecurity, the influence of attitude towards using Cybersecurity on the actual use of Cybersecurity and the influences of job positions on perceived ease of use of Cybersecurity and on the attitude towards using Cybersecurity and on the actual use of Cybersecurity. A model was constructed to investigate eight hypotheses that are related to the investigation. An online questionnaire was constructed to collect data and results showed that hypotheses 1 to 7 influence were significant. However, hypothesis 8 turned out to be insignificant and no influence was found between job positions and the actual use of Cybersecurity.展开更多
The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making...The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.展开更多
Time Sensitive Networking(TSN)will be an integral component of industrial networking.Time synchronization in TSN is provided by the IEEE-1588,Precision Time Protocol(PTP)protocol.The standard,dating back to 2008,margi...Time Sensitive Networking(TSN)will be an integral component of industrial networking.Time synchronization in TSN is provided by the IEEE-1588,Precision Time Protocol(PTP)protocol.The standard,dating back to 2008,marginally addresses security aspects,notably not encompassing the frames designed for management purposes(Type Length Values or TLVs).In this work we show that the TLVs can be abused by an attacker to reconfigure,manipulate,or shut down time synchronization.The effects of such an attack can be serious,ranging from interruption of operations to actual unintended behavior of industrial devices,possibly resulting in physical damages or even harm to operators.The paper analyzes the root causes of this vulnerability,and provides concrete examples of attacks leveraging it to de-synchronize the clocks,showing that they can succeed with limited resources,realistically available to a malicious actor.展开更多
文摘The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, research data, personal data, proprietary knowledge, complex equipment designs and blueprints for yet to be patented products has all become extremely susceptible to Cybersecurity attacks. This research will investigate factors that affect that may have an influence on perceived ease of use of Cybersecurity, the influence of perceived ease of use on the attitude towards using Cybersecurity, the influence of attitude towards using Cybersecurity on the actual use of Cybersecurity and the influences of job positions on perceived ease of use of Cybersecurity and on the attitude towards using Cybersecurity and on the actual use of Cybersecurity. A model was constructed to investigate eight hypotheses that are related to the investigation. An online questionnaire was constructed to collect data and results showed that hypotheses 1 to 7 influence were significant. However, hypothesis 8 turned out to be insignificant and no influence was found between job positions and the actual use of Cybersecurity.
文摘The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.
文摘Time Sensitive Networking(TSN)will be an integral component of industrial networking.Time synchronization in TSN is provided by the IEEE-1588,Precision Time Protocol(PTP)protocol.The standard,dating back to 2008,marginally addresses security aspects,notably not encompassing the frames designed for management purposes(Type Length Values or TLVs).In this work we show that the TLVs can be abused by an attacker to reconfigure,manipulate,or shut down time synchronization.The effects of such an attack can be serious,ranging from interruption of operations to actual unintended behavior of industrial devices,possibly resulting in physical damages or even harm to operators.The paper analyzes the root causes of this vulnerability,and provides concrete examples of attacks leveraging it to de-synchronize the clocks,showing that they can succeed with limited resources,realistically available to a malicious actor.
