The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure ...The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.展开更多
This article presents an asset-based security system where security practitioners build their systems based on information they own and not solicited by observing attackers’behavior.Current security solutions rely on...This article presents an asset-based security system where security practitioners build their systems based on information they own and not solicited by observing attackers’behavior.Current security solutions rely on information coming from attackers.Examples are current monitoring and detection security solutions such as intrusion prevention/detection systems and firewalls.This article envisions creating an imbalance between attackers and defenders in favor of defenders.As such,we are proposing to flip the security game such that it will be led by defenders and not attackers.We are proposing a security system that does not observe the behavior of the attack.On the contrary,we draw,plan,and follow up our own protection strategy regardless of the attack behavior.The objective of our security system is to protect assets rather than protect against attacks.Virtual machine introspection is used to intercept,inspect,and analyze system calls.The system callbased approach is utilized to detect zero-day ransomware attacks.The core idea is to take advantage of Xen and DRAKVUF for system call interception,and leverage system calls to detect illegal operations towards identified critical assets.We utilize our vision by proposing an asset-based approach to mitigate zero-day ransomware attacks.The obtained results are promising and indicate that our prototype will achieve its goals.展开更多
This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data.Unlike traditional ...This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data.Unlike traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of sepsis.By integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions.Moreover,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis outcomes.This innovative integration significantly enhances the effectiveness of our approach.We leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing framework.Through techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock prediction.This transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical workflows.We validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling techniques.The findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.展开更多
基金support of the Interdisciplinary Research Center for Intelligent Secure Systems(IRC-ISS)Internal Fund Grant#INSS2202.
文摘The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.
基金This project is funded by King Abdulaziz City for Science and Technology(KACST)under the National Science,Technology,and Innovation Plan(Project Number 11-INF1657-04).
文摘This article presents an asset-based security system where security practitioners build their systems based on information they own and not solicited by observing attackers’behavior.Current security solutions rely on information coming from attackers.Examples are current monitoring and detection security solutions such as intrusion prevention/detection systems and firewalls.This article envisions creating an imbalance between attackers and defenders in favor of defenders.As such,we are proposing to flip the security game such that it will be led by defenders and not attackers.We are proposing a security system that does not observe the behavior of the attack.On the contrary,we draw,plan,and follow up our own protection strategy regardless of the attack behavior.The objective of our security system is to protect assets rather than protect against attacks.Virtual machine introspection is used to intercept,inspect,and analyze system calls.The system callbased approach is utilized to detect zero-day ransomware attacks.The core idea is to take advantage of Xen and DRAKVUF for system call interception,and leverage system calls to detect illegal operations towards identified critical assets.We utilize our vision by proposing an asset-based approach to mitigate zero-day ransomware attacks.The obtained results are promising and indicate that our prototype will achieve its goals.
基金funded by the Deanship of Research Oversight and Coordination (DROC),King Fahd University of Petroleum and Minerals,Dhahran 31261,Saudi ArabiaData and computing resources used to conduct the experiment were supported by Early Career grant (#EC-213004).
文摘This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data.Unlike traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of sepsis.By integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions.Moreover,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis outcomes.This innovative integration significantly enhances the effectiveness of our approach.We leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing framework.Through techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock prediction.This transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical workflows.We validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling techniques.The findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.
