Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the data...Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.展开更多
The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been...The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in Indonesia.The increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)attacks.An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous.This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks.The proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a classifier.This model can effectively solve the vanishing gradient problem caused by excessive positive samples.The experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing data.The rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’patterns.The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded.In addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory.展开更多
Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de...Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.展开更多
Structured Query Language(SQL)injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm.Traditional injection attack detection techniq...Structured Query Language(SQL)injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm.Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks.This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection.Features are extracted through content matching,effectively avoiding the loss of valid information,and an improved deep learning model is employed to enhance the detection effect of SQL injections.Considering that grammar parsing and word embedding may conceal key features and introduce noise,we propose training the transformed data vectors by preprocessing the data in the dataset and post-processing the word segmentation based on content matching.We optimized and adjusted the traditional Convolutional Neural Network(CNN)model,trained normal data,SQL injection data,and XSS data,and used these three deep learning models for attack detection.The experimental results show that the accuracy rate reaches 98.35%,achieving excellent detection results.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies...Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.展开更多
In this paper,we address a cross-layer resilient control issue for a kind of multi-spacecraft system(MSS)under attack.Attackers with bad intentions use the false data injection(FDI)attack to prevent the MSS from reach...In this paper,we address a cross-layer resilient control issue for a kind of multi-spacecraft system(MSS)under attack.Attackers with bad intentions use the false data injection(FDI)attack to prevent the MSS from reaching the goal of consensus.In order to ensure the effectiveness of the control,the embedded defender in MSS preliminarily allocates the defense resources among spacecrafts.Then,the attacker selects its target spacecrafts to mount FDI attack to achieve the maximum damage.In physical layer,a Nash equilibrium(NE)control strategy is proposed for MSS to quantify system performance under the effect of attacks by solving a game problem.In cyber layer,a fuzzy Stackelberg game framework is used to examine the rivalry process between the attacker and defender.The strategies of both attacker and defender are given based on the analysis of physical layer and cyber layer.Finally,a simulation example is used to test the viability of the proposed cross layer fuzzy game algorithm.展开更多
A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the meas...This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.展开更多
With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits ...With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.展开更多
This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control func...This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.展开更多
Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false d...Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals.For quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion perspective.Then,we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks.The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection.Moreover,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack observer.In addition,this secure control method is simple to implement because it avoids complicated mathematical operations.The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.展开更多
E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have va...E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have varying levels of understanding when it comes to securing an online application.Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws.Many published articles focused on these attacks’binary classification.This article described a novel deep-learning approach for detecting SQL injection and XSS attacks.The datasets for SQL injection and XSS payloads are combined into a single dataset.The dataset is labeledmanually into three labels,each representing a kind of attack.This work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a vector.Our model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload dataset.The payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and normal.The outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious payloads.BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.展开更多
文摘Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.
文摘The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in Indonesia.The increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)attacks.An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous.This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks.The proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a classifier.This model can effectively solve the vanishing gradient problem caused by excessive positive samples.The experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing data.The rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’patterns.The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded.In addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory.
基金supported in part by the National Science Foundation of China(62373240,62273224,U24A20259).
文摘Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.
基金supported by Jiangsu Higher Education“Qinglan Project”,an Open Project of Criminal Inspection Laboratory in Key Laboratories of Sichuan Provincial Universities(2023YB03)Major Project of Basic Science(Natural Science)Research in Higher Education Institutions in Jiangsu Province(23KJA520004)+5 种基金Jiangsu Higher Education Philosophy and Social Sciences Research General Project(2023SJYB0467)Action Plan of the National Engineering Research Center for Cybersecurity Level Protection and Security Technology(KJ-24-004)Jiangsu Province Degree and Postgraduate Education and Teaching Reform Project(JGKT24_B036)Digital Forensics Engineering Research Center of the Ministry of Education Open Project(DF20-010)Teaching Practice of Web Development and Security Testing under the Background of Industry University Cooperation(241205403122215)Research on Strategies for Combating and Preventing Virtual Currency Telecommunications Fraud(2024SJYB0344).
文摘Structured Query Language(SQL)injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm.Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks.This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection.Features are extracted through content matching,effectively avoiding the loss of valid information,and an improved deep learning model is employed to enhance the detection effect of SQL injections.Considering that grammar parsing and word embedding may conceal key features and introduce noise,we propose training the transformed data vectors by preprocessing the data in the dataset and post-processing the word segmentation based on content matching.We optimized and adjusted the traditional Convolutional Neural Network(CNN)model,trained normal data,SQL injection data,and XSS data,and used these three deep learning models for attack detection.The experimental results show that the accuracy rate reaches 98.35%,achieving excellent detection results.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
文摘Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.
基金supported by the Natural Science Foundation of China(62073268,62122063,62203360)the Young Star of Science and Technology in Shaanxi Province(2020KJXX-078).
文摘In this paper,we address a cross-layer resilient control issue for a kind of multi-spacecraft system(MSS)under attack.Attackers with bad intentions use the false data injection(FDI)attack to prevent the MSS from reaching the goal of consensus.In order to ensure the effectiveness of the control,the embedded defender in MSS preliminarily allocates the defense resources among spacecrafts.Then,the attacker selects its target spacecrafts to mount FDI attack to achieve the maximum damage.In physical layer,a Nash equilibrium(NE)control strategy is proposed for MSS to quantify system performance under the effect of attacks by solving a game problem.In cyber layer,a fuzzy Stackelberg game framework is used to examine the rivalry process between the attacker and defender.The strategies of both attacker and defender are given based on the analysis of physical layer and cyber layer.Finally,a simulation example is used to test the viability of the proposed cross layer fuzzy game algorithm.
文摘A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
基金supported by the National Natural Science Foundation of China(61925303,62173034,62088101,U20B2073,62173002)the National Key Research and Development Program of China(2021YFB1714800)Beijing Natural Science Foundation(4222045)。
文摘This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.
基金This work was supported in part by the National Natural Science Foundation of China(62206109)the Fundamental Research Funds for the Central Universities(21620346)。
文摘With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.
基金the National Natural Science Foundation of China(61873057)the Education Department of Jilin Province(JJKH20200118KJ).
文摘This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.
基金supported in part by the National Science Foundation of China(61873103,61433006)。
文摘Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals.For quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion perspective.Then,we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks.The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection.Moreover,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack observer.In addition,this secure control method is simple to implement because it avoids complicated mathematical operations.The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.
基金funded byResearchers Supporting Project Number(RSP2023R476)King Saud University,Riyadh,Saudi Arabia。
文摘E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have varying levels of understanding when it comes to securing an online application.Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws.Many published articles focused on these attacks’binary classification.This article described a novel deep-learning approach for detecting SQL injection and XSS attacks.The datasets for SQL injection and XSS payloads are combined into a single dataset.The dataset is labeledmanually into three labels,each representing a kind of attack.This work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a vector.Our model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload dataset.The payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and normal.The outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious payloads.BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.
