The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing hardware.However,exi...The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing hardware.However,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource limitations.Considering the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)circuits.Some studies have pointed out that the failure of key modules tends to cause greater damage to the network.Therefore,under limited conditions,priority protection designs need to be made on key modules to improve protection efficiency.We have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA systems.For the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)method.Then,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important modules.Finally,we conduct empirical research using actual FPGA designs as examples.The results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.展开更多
Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious ...Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.展开更多
Dear Editor, In order to accommodate the effects of false data injection attacks(FDIAs), the moving target defense(MTD) strategy is recently proposed to enhance the security of the smart grid by perturbing branch susc...Dear Editor, In order to accommodate the effects of false data injection attacks(FDIAs), the moving target defense(MTD) strategy is recently proposed to enhance the security of the smart grid by perturbing branch susceptances. However, most pioneer work only focus on the defending performance of MTD in terms of detecting FDIAs and the impact of MTD on the static factors such as the power and economic losses.展开更多
Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this lett...Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is launched.Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.展开更多
With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WT...With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.展开更多
基金supported by the Natural Science Foundation of China under Grant Nos.62362008,61973163,61972345,U1911401.
文摘The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing hardware.However,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource limitations.Considering the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)circuits.Some studies have pointed out that the failure of key modules tends to cause greater damage to the network.Therefore,under limited conditions,priority protection designs need to be made on key modules to improve protection efficiency.We have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA systems.For the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)method.Then,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important modules.Finally,we conduct empirical research using actual FPGA designs as examples.The results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.
基金supported in part by the Guizhou Provincial Science and Technology Projects(ZK[2022]149)the Special Foundation of Guizhou University(GZU)([2021]47)+2 种基金the Guizhou Provincial Research Project for Universities([2022]104)the GZU cultivation project of the National Natural Science Foundation of China([2020]80)Shanghai Engineering Research Center of Big Data Management。
文摘Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.
基金supported in part by the Guizhou Provincial Science and Technology Projects(ZK[2022]149)the Natural Science Foundation of Guizhou University([2021]47)+4 种基金the GZU Cultivation project of the National Natural Science Foundation of China(NSFC)([2020]80)NSFC(62073285,62061130220)the Natural Science Foundation of Zhejiang Province(LZ21F 020006)the Fundamental Research Funds for the Central Universities(226-2022-00120)the Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province。
文摘Dear Editor, In order to accommodate the effects of false data injection attacks(FDIAs), the moving target defense(MTD) strategy is recently proposed to enhance the security of the smart grid by perturbing branch susceptances. However, most pioneer work only focus on the defending performance of MTD in terms of detecting FDIAs and the impact of MTD on the static factors such as the power and economic losses.
基金supported in part by the Guizhou Provincial Science and Technology Projects(ZK[2022]149)the Guizhou Provincial Research Project for Universities([2022]104)+2 种基金the Special Foundation of Guizhou University([2021]47)the GZU cultivation project of National Natural Science Foundation of China([2020]80)Shanghai Engineering Research Center of Big Data Management,and the National Natural Science Foundation of China(62073285,62061130220)。
文摘Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is launched.Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.
基金Project supported by the National Natural Science Foundation of China(No.61833015)。
文摘With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.