Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems (CPESs), but such systems have also become popular targets of cyberattacks. Currently, available methods for evaluating the impacts of cyberattacks suffer from limited resilience, efficacy, and practical value. To mitigate their potentially disastrous consequences, this study suggests a two-stage, discrepancy-based optimization approach that considers both preparatory actions and response measures, integrating concepts from social computing. The proposed Kullback-Leibler divergence-based, distributionally robust optimization (KDR) method has a hierarchical, two-stage objective function that incorporates the operating costs of both system infrastructures (e.g., energy resources, reserve capacity) and real-time response measures (e.g., load shedding, demand-side management, electric vehicle charging station management). By incorporating social computing principles, the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks. The preparatory stage entails day-ahead operational decisions, leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks. The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side, taking into account the social context and preferences of energy consumers, to ensure resilient, economically efficient CPES operations. Our method can determine optimal schemes in both stages, accounting for the social dimensions of the problem. An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR, incorporating social computing techniques to enhance the understanding and response to cyber threats. This approach can mitigate the impacts more effectively than several existing methods, even with limited data availability. To extend this risk-neutral model, we incorporate conditional value at risk as an essential risk measure, capturing the uncertainty and diverse impact scenarios arising from social computing factors. The empirical results affirm that the KDR method, which is enriched with social computing considerations, produces resilient, economically efficient solutions for managing the impacts of cyberattacks on a CPES. By integrating social computing principles into the optimization framework, it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs, ultimately improving the overall resilience and effectiveness of the system's response measures. 

A Social Computing Method for Energy Safety

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. System security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. 

Covert Channel Detection and Generation Techniques: A Survey



Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.



Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

Priority Selection of Road Traffic Net-works in Emergency Situations Based on Internet of Vehicles

The utilization of network technology in Industrial Control Systems (ICS) is becoming increasingly prevalent due to advancements in the Fifth Generation Networks (5G), the Internet of Things (IoT), cloud computing, and big data. These technologies have significantly enhanced intelligent mobile device data transmission speed and interaction levels. The connection of ICS devices to a network gives rise to supplementary security considerations. Hence, this paper must analyze and synthesize pertinent scholarly works on cyber security and safety within the ICS sector. This paper further categorizes the security risks that ICS may encounter into six distinct areas: attack, detect, risk estimation, incident response, protect, and incident prevention. Extensive literature evaluations and rigorous research were conducted to examine the subject matter thoroughly. Based on the findings, comprehensive suggestions and strategic approaches were produced for each specific area. This paper proposed the ICS-based Purdue Enterprise Reference Architecture (PERA) to evaluate the threat and solution for enhancing ICS security and safety. 

A Survey of Cyber Security and Safety in Industrial Control Systems

False data injection (FDI) attacks are serious threats to a cyber‐physical power system (CPPS), which may be launched by a malicious software or virus accessing only the measurements from one substation. This study proposes a novel attack method named the temporal FDI (TFDI) attack. Namely, the virus makes decisions based on temporal observations of the CPPS, and the attack is driven by a deep Q network (DQN) algorithm. As DQN takes vectors of continuous variables as input states, the proposed method is free of the state space explosion problem, which helps the virus to learn the optimal attack strategy efficiently. Moreover, for adopting time‐series measurements as quasi‐dynamic observations, long short‐term memory cells are employed as a layer of the Q network. The TFDI attack enables the virus to discern trends of load variations and enhance the attack’s effectiveness. Meanwhile, a countermeasure is also presented to detect the proposed FDI attack. Binary classifiers are trained for each bus to detect suspicious local measurements according to their deviations from system‐state manifolds. When suspicious measurements are spotted frequently, the corresponding bus is believed to be under FDI attacks. Test cases validate the efficacy of the proposed FDI attack method as well as its countermeasure.

Temporal false data injection attack and detection on cyber‐physical power system based on deep reinforcement learning

Aiming at the problems of low concealment of existing storage-type covert channels, high bit error rate, and low transmission rate of time-type covert channels, this paper proposes a method of constructing covert channels based on HTTP protocol combination. The method simulates browser application to send HTTP requests, dynamically distributes HTTP requests to different browsers, embeds hidden information by mathematical combination, and dynamically adjusts access objects, data packet time interval, and data packet length, thus improving the concealment of the channel. At the same time, the channel is based on the reliable transmission inside TCP protocol so that it is not affected by network jitter, thus ensuring the reliability of the channel. Experimental results show that this method can resist the detection method based on application signature, protocol fingerprint detection method, and combination model detection method, and has strong concealment. It can adjust the concealment and channel capacity according to the application scenario.

https://downloads.hindawi.com/journals/jece/2022/2257524.pdf

Covert Channel Construction Method Based on HTTP Composite Protocols

The early Poisson distribution and Markov autoregressive models can no longer reflect the characteristics of smart grid business flow. The long correlation characteristics of burst flow are simulated by the heavy tail ON/OFF model with multi-source convergence, while the Poisson model with short correlation and no memory is used for random flow. The simulation results show that the synthesized flow is still has a certain statistical self-similarity, and the coefficient H decreases after synthesis.That the long correlation and burst of self-similar business flows will not be smoothed by the convergence and synthesis of the self-similar business flows with short correlation services. In the actual operation process, the station-level power grid will result in load aggravation and significantly affect the network performance when it acts in response to faults. Therefore, it is necessary to consider the changes of network performance under different burst degrees.

/pdf/application-of-smart-grid-communication-service-flow-1zqyt1pq.pdf

Application of Smart Grid Communication Service Flow Modeling Based on Poisson Model in Grid Operation

False data injection (FDI) attacks have been regarded as serious cybersecurity threats to power systems. This paper proposed an attack detection method, which involves measuring uncertainties in both physical and cyber systems caused by FDI attacks. Using a new concept called Cyber-Physical System Entropy (CPSE), two indicators are developed: the grid CPSE (G-CPSE) and the bus CPSE (B-CPSE). An online detection method is coined for discovering long-term FDI attacks according to spatial and temporal increments of G-CPSE and B-CPSE. Test results on the IEEE 39-bus system show that both model-based and learning-based FDI attacks can be detected effectively and sensitively using G-CPSE and B-CPSE.

Towards Detecting Long-term False Data Injection Attacks: An Entropy-based Approach

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Papers

Temporal false data injection attack and detection on cyber‐physical power system based on deep reinforcement learning

Covert Channel Construction Method Based on HTTP Composite Protocols

Application of Smart Grid Communication Service Flow Modeling Based on Poisson Model in Grid Operation

Towards Detecting Long-term False Data Injection Attacks: An Entropy-based Approach