Biblio

Physical Layer Security (PLS) is used to accomplish perfect secure communication between intended network nodes, while the eavesdropper gets zero information. In this paper, a smart antenna technology i.e., Massive multiple-input-multiple-output (mMIMO) and Non-Orthogonal Multiple Access (NOMA) technology is being used to enhance the secrecy performance of a 5G communication network. Small scale Rayleigh fading channels, as well as large scale pathway loss, have to be taken into consideration. An eavesdropper with multiple antennas, an amplify-and-forward (AF) relay with multi antenna has been proposed. Spider Monkey Algorithm (SMO) is used in adding Artificial Noise (AN) for refining secrecy rate. The findings revealed that the suggested technique improves the security and the quality of Wireless communication.

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.

Security evaluation can be performed using a variety of analysis methods, such as attack trees, attack graphs, threat propagation models, stochastic Petri nets, and so on. These methods analyze the effect of attacks on the system, and estimate security attributes from different perspectives. However, they require information from experts in the application domain for properly capturing the key elements of an attack scenario: i) the attack paths a system could be subject to, and ii) the different characteristics of the possible adversaries. For this reason, some recent works focused on the generation of low-level security models from a high-level description of the system, hiding the technical details from the modeler.In this paper we build on an existing ontology framework for security analysis, available in the ADVISE Meta tool, and we extend it in two directions: i) to cover the attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and ii) to capture all the adversaries’ profiles as defined in the Threat Agent Library (TAL), a reference library for defining the characteristics of external and internal threat agents ranging from industrial spies to untrained employees. The proposed extension supports a richer combination of adversaries’ profiles and attack paths, and provides guidance on how to further enrich the ontology based on taxonomies of attacks and adversaries.

Static analysis tools generate a large number of alarms that require manual inspection. In prior work, repositioning of alarms is proposed to (1) merge multiple similar alarms together and replace them by a fewer alarms, and (2) report alarms as close as possible to the causes for their generation. The premise is that the proposed merging and repositioning of alarms will reduce the manual inspection effort. To evaluate the premise, this paper presents an empirical study with 249 developers on the proposed merging and repositioning of static alarms. The study is conducted using static analysis alarms generated on \$C\$ programs, where the alarms are representative of the merging vs. non-merging and repositioning vs. non-repositioning situations in real-life code. Developers were asked to manually inspect and determine whether assertions added corresponding to alarms in \$C\$ code hold. Additionally, two spatial cognitive tests are also done to determine relationship in performance. The empirical evaluation results indicate that, in contrast to expectations, there was no evidence that merging and repositioning of alarms reduces manual inspection effort or improves the inspection accuracy (at times a negative impact was found). Results on cognitive abilities correlated with comprehension and alarm inspection accuracy.

Static analysis tools help to detect common pro-gramming errors but generate a large number of false positives. Moreover, when applied to evolving software systems, around 95 % of alarms generated on a version are repeated, i.e., they have also been generated on the previous version. Version-aware static analysis techniques (VSATs) have been proposed to suppress the repeated alarms that are not impacted by the code changes between the two versions. The alarms reported by VSATs after the suppression, called delta alarms, still constitute 63% of the tool-generated alarms. We observe that delta alarms can be further postprocessed using their corresponding code changes: the code changes due to which VSATs identify them as delta alarms. However, none of the existing VSATs or alarms postprocessing techniques postprocesses delta alarms using the corresponding code changes. Based on this observation, we use the code changes to classify delta alarms into six classes that have different priorities assigned to them. The assignment of priorities is based on the type of code changes and their likelihood of actually impacting the delta alarms. The ranking of alarms, obtained by prioritizing the classes, can help suppress alarms that are ranked lower, when resources to inspect all the tool-generated alarms are limited. We performed an empirical evaluation using 9789 alarms generated on 59 versions of seven open source C applications. The evaluation results indicate that the proposed classification and ranking of delta alarms help to identify, on average, 53 % of delta alarms as more likely to be false positives than the others.

Static analyzers have become increasingly popular both as developer tools and as subjects of empirical studies. Whereas static analysis tools exist for disparate programming languages, the bulk of the empirical research has focused on the popular Java programming language. In this paper, we investigate to what extent some known results about using static analyzers for Java change when considering C\#-another popular object-oriented language. To this end, we combine two replications of previous Java studies. First, we study which static analysis tools are most widely used among C\# developers, and which warnings are more commonly reported by these tools on open-source C\# projects. Second, we develop and empirically evaluate EagleRepair: a technique to automatically fix code in response to static analysis warnings; this is a replication of our previous work for Java [20]. Our replication indicates, among other things, that 1) static code analysis is fairly popular among C\# developers too; 2) Re-Sharper is the most widely used static analyzer for C\#; 3) several static analysis rules are commonly violated in both Java and C\# projects; 4) automatically generating fixes to static code analysis warnings with good precision is feasible in C\#. The EagleRepair tool developed for this research is available as open source.

Software based scan diagnosis is the de facto method for debugging logic scan failures. Physical analysis success rate is high on dies diagnosed with maximum score, one symptom, one suspect and shorter net. This poses a limitation on maximum utilization of scan diagnosis data for PFA. There have been several attempts to combine dynamic fault isolation techniques with scan diagnosis results to enhance the utilization and success rate. However, it is not a feasible approach for foundry due to limited product design and test knowledge and hardware requirements such as probe card and tester. Suitable for a foundry, an enhanced diagnosis-driven analysis scheme was proposed in [1] that classifies the failures as frontend-of-line (FEOL) and backend-of-line (BEOL) improving the die selection process for PFA. In this paper, static NIR PEM and defect prediction approach are applied on dies that are already classified as FEOL and BEOL failures yet considered unsuitable for PFA due to low score, multiple symptoms, and suspects. Successful case studies are highlighted to showcase the effectiveness of using static NIR PEM as the next level screening process to further maximize the scan diagnosis data utilization.

The complexity and scale of modern software programs often lead to overlooked programming errors and security vulnerabilities. Developers often rely on automatic tools, like static analysis tools, to look for bugs and vulnerabilities. Static analysis tools are widely used because they can understand nontrivial program behaviors, scale to millions of lines of code, and detect subtle bugs. However, they are known to generate an excess of false alarms which hinder their utilization as it is counterproductive for developers to go through a long list of reported issues, only to find a few true positives. One of the ways proposed to suppress false positives is to use machine learning to identify them. However, training machine learning models requires good quality labeled datasets. For this purpose, we developed D2A [3], a differential analysis based approach that uses the commit history of a code repository to create a labeled dataset of Infer [2] static analysis output.

Smart grid (SG) is considered the next generation of the traditional power grid. It is mainly divided into three main infrastructures: power system, information and communication infrastructures. Cybersecurity is imperative for information infrastructure and the secure, reliable, and efficient operation of the smart grid. Cybersecurity or a lack of proper implementation thereof poses a considerable challenge to the deployment of SG. Therefore, in this paper, A comprehensive survey of cyber security is presented in the smart grid context. Cybersecurity-related information infrastructure is clarified. The impact of adopting cybersecurity on control and management systems has been discussed. Also, the paper highlights the cybersecurity issues and challenges associated with the control decisions in the smart grid.

In order to solve the problem of untargeted data security grading methods in the process of power grid data governance, this paper analyzes the mainstream data security grading standards at home and abroad, investigates and sorts out the characteristics of power grid data security grading requirements, and proposes a method that considers national, social, and A grid data security classification scheme for the security impact of four dimensions of individuals and enterprises. The plan determines the principle of power grid data security classification. Based on the basic idea of “who will be affected to what extent and to what extent when the power grid data security is damaged”, it defines three classification factors that need to be considered: the degree of impact, the scope of influence, and the objects of influence, and the power grid data is divided into five security levels. In the operation stage of power grid data security grading, this paper sorts out the experience and gives the recommended grading process. This scheme basically conforms to the status quo of power grid data classification, and lays the foundation for power grid data governance.

Smart grids are envisioned as the next-generation electricity grids. The data measured from the smart grid is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryption overhead on their users. To this end, we propose a lightweight attribute-based encryption scheme for data access control in smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental evaluation demonstrate that our scheme outperforms the existing schemes by achieving a very low decryption cost.

As a new generation of power grid system, smart grid and smart meter conduct two-way communication to realize the intelligent collection, monitoring and dispatching of user power data, so as to achieve a safer, stable, reliable and efficient power grid environment. With the vigorous development of power grid, there are also some security and privacy problems. This paper uses Paillier homomorphic encryption algorithm and role-based access control strategy to ensure the privacy security in the process of multi-dimensional aggregation, data transmission and sharing of power data. Applying the characteristics of blockchain technology such as decentralization, non tampering and traceability to the smart grid can effectively solve the privacy and security problems of power data transmission and sharing in the smart grid. This paper compares Paillier encryption algorithm with PPAR algorithm and SIAHE algorithm in terms of encryption mechanism, number of aggregators and computational complexity respectively. The results show that Paillier homomorphic encryption algorithm has higher data privacy and security.

Event detection and classification are crucial to power system stability. The Wide Area Measurement System (WAMS) technology helps in enhancing wide area situational awareness by providing useful synchronized information to the grid control center in order to accurately identify various power system events. This paper demonstrates the viability of using EWAMS (Egyptian Wide Area Measurement System) data as one of the evolving technologies of smart grid to identify extreme events within the Egyptian power grid. The proposed scheme is based on online synchronized measurements of wide-area monitoring devices known as Frequency Disturbance Recorders (FDRs) deployed at selected substations within the grid. The FDR measures the voltage, voltage angle, and frequency at the substation and streams the processed results to the Helwan University Host Server (HUHS). Each FDR is associated with a timestamp reference to the Global Positioning System (GPS) base. An EWAMS-based frequency disturbance detection algorithm based on the rate of frequency deviation is developed to identify varies types of events such as generator trip and load shedding. Based on proper thresholding on the frequency and rate of change of frequency of the Egyptian grid, different types of events have been captured in many locations during the supervision and monitoring the operation of the grid. EWAMS historical data is used to analyze a wide range of data pre-event, during and post-event for future enhancement of situational awareness as well as decision making.

While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

Smart grids integrate computing and communication infrastructure with conventional power grids to improve situational awareness, control, and safety. Several technologies such as automatic fault detection, automated reconfiguration, and outage management require close network monitoring. Therefore, utilities utilize sensing equipment such as PMUs (phasor measurement units), smart meters, and bellwether meters to obtain grid measurements. However, the expansion in sensing equipment results in an increased strain on existing communication infrastructure. Prior works overcome this problem by exploiting the sparsity of power consumption data in the Haar, Hankel, and Toeplitz transformation bases to achieve sub-Nyquist compression. However, data-driven dictionaries enable superior compression ratios and reconstruction accuracy by learning the sparsifying basis. Therefore, this work proposes using dictionary learning to learn the sparsifying basis of smart meter data. The smart meter data sent to the data centers are compressed using a random projection matrix prior to transmission. These measurements are aggregated to obtain the compressed measurements at the primary nodes. Compressive sensing-based estimators are then utilized to estimate the system states. This approach was validated on the IEEE 33-node distribution system and showed superior reconstruction accuracy over conventional transformation bases and over-complete dictionaries. Voltage magnitude and angle estimation error less than 0.3% mean absolute percentage error and 0.04 degree mean absolute error, respectively, were achieved at compression ratios as high as eight.

The features of socio-cyber-physical systems are presented, which dictate the need to revise traditional management methods and transform the management system in such a way that it takes into account the presence of a person both in the control object and in the control loop. The use of situational control mechanisms is proposed. The features of this approach and its comparison with existing methods of situational awareness are presented. The comparison has demonstrated wider possibilities and scope for managing socio-cyber-physical systems. It is recommended to consider a wider class of types of relations that exist in socio-cyber-physical systems. It is indicated that such consideration can be based on the use of pseudo-physical logics considered in situational control. It is pointed out that it is necessary to design a classifier of situations (primarily in cyberspace), instead of traditional classifiers of threats and intruders.

Control room video surveillance is an important source of information for ensuring public safety. To facilitate the process, a Decision-Support System (DSS) designed for the security task force is vital and necessary to take decisions rapidly using a sea of information. In case of mission critical operation, Situational Awareness (SA) which consists of knowing what is going on around you at any given time plays a crucial role across a variety of industries and should be placed at the center of our DSS. In our approach, SA system will take advantage of the human factor thanks to the reinforcement signal whereas previous work on this field focus on improving knowledge level of DSS at first and then, uses the human factor only for decision-making. In this paper, we propose a situational awareness-centric decision-support system framework for mission-critical operations driven by Quality of Experience (QoE). Our idea is inspired by the reinforcement learning feedback process which updates the environment understanding of our DSS. The feedback is injected by a QoE built on user perception. Our approach will allow our DSS to evolve according to the context with an up-to-date SA.

According to the characteristics of security threats and massive users in power mobile applications, a mobile application security situational awareness method based on big data architecture is proposed. The method uses open-source big data technology frameworks such as Kafka, Flink, Elasticsearch, etc. to complete the collection, analysis, storage and visual display of massive power mobile application data, and improve the throughput of data processing. The security situation awareness method of power mobile application takes the mobile terminal threat index as the core, divides the risk level for the mobile terminal, and predicts the terminal threat index through support vector machine regression algorithm (SVR), so as to construct the security profile of the mobile application operation terminal. Finally, through visualization services, various data such as power mobile applications and terminal assets, security operation statistics, security strategies, and alarm analysis are displayed to guide security operation and maintenance personnel to carry out power mobile application security monitoring and early warning, banning disposal and traceability analysis and other decision-making work. The experimental analysis results show that the method can meet the requirements of security situation awareness for threat assessment accuracy and response speed, and the related results have been well applied in a power company.

In defense and security applications, detection of moving target direction is as important as the target detection and/or target classification. In this study, a methodology for the detection of different mobile targets as approaching or receding was proposed for ground surveillance radar data, and convolutional neural networks (CNN) based on transfer learning were employed for this purpose. In order to improve the classification performance, the use of two key concepts, namely Deep Convolutional Generative Adversarial Network (DCGAN) and decision fusion, has been proposed. With DCGAN, the number of limited available data used for training was increased, thus creating a bigger training dataset with identical distribution to the original data for both moving directions. This generated synthetic data was then used along with the original training data to train three different pre-trained deep convolutional networks. Finally, the classification results obtained from these networks were combined with decision fusion approach. In order to evaluate the performance of the proposed method, publicly available RadEch dataset consisting of eight ground target classes was utilized. Based on the experimental results, it was observed that the combined use of the proposed DCGAN and decision fusion methods increased the detection accuracy of moving target for person, vehicle, group of person and all target groups, by 13.63%, 10.01%, 14.82% and 8.62%, respectively.

Smart cities deploy large numbers of sensors and collect a tremendous amount of data from them. For example, Advanced Metering Infrastructures (AMIs), which consist of physical meters that collect usage data about public utilities such as power and water, are an important building block in a smart city. In a typical sensor network, the measurement devices are connected through a computer network, which exposes them to cyber attacks. Furthermore, the data is centrally managed at the operator’s servers, making it vulnerable to insider threats.Our goal is to protect the integrity of data collected by large-scale sensor networks and the firmware in measurement devices from cyber attacks and insider threats. To this end, we first develop a comprehensive threat model for attacks against data and firmware integrity, which can target any of the stakeholders in the operation of the sensor network. Next, we use our threat model to analyze existing defense mechanisms, including signature checks, remote firmware attestation, anomaly detection, and blockchain-based secure logs. However, the large size of the Trusted Computing Base and a lack of scalability limit the applicability of these existing mechanisms. We propose the Feather-Light Blockchain Infrastructure (FLBI) framework to address these limitations. Our framework leverages a two-layer architecture and cryptographic threshold signature chains to support large networks of low-capacity devices such as meters and data aggregators. We have fully implemented the FLBI’s end-to-end functionality on the Hyperledger Fabric and private Ethereum blockchain platforms. Our experiments show that the FLBI is able to support millions of end devices.

FPGA bitstream protection schemes are often the first line of defense for secure hardware designs. In general, breaking the bitstream encryption would enable attackers to subvert the confidentiality and infringe on the IP. Or breaking the authenticity enables manipulating the design, e.g., inserting hardware Trojans. Since FPGAs see widespread use in our interconnected world, such attacks can lead to severe damages, including physical harm. Recently we [1] presented a surprising attack — Starbleed — on Xilinx 7-Series FPGAs, tricking an FPGA into acting as a decryption oracle. For their UltraScale(+) series, Xilinx independently upgraded the security features to AES-GCM, RSA signatures, and a periodic GHASH-based checksum to validate the bitstream during decryption. Hence, UltraScale(+) devices were considered not affected by Starbleed-like attacks [2], [1].We identified novel security weaknesses in Xilinx UltraScale(+) FPGAs if configured outside recommended settings. In particular, we present four attacks in this situation: two attacks on the AES encryption and novel GHASH-based checksum and two authentication downgrade attacks. As a major contribution, we show that the Starbleed attack is still possible within the UltraScale(+) series by developing an attack against the GHASH-based checksum. After describing and analyzing the attacks, we list the subtle configuration changes which can lead to security vulnerabilities and secure configurations not affected by our attacks. As Xilinx only recommends configurations not affected by our attacks, users should be largely secure. However, it is not unlikely that users employ settings outside the recommendations, given the rather large number of configuration options and the fact that Security Misconfiguration is among the leading top 10 OWASP security issues. We note that these security weaknesses shown in this paper had been unknown before.

When applied to short-term energy consumption forecasting, the federated learning framework allows for the creation of a predictive model without sharing raw data. There is a limit to the accuracy achieved by standard federated learning due to the heterogeneity of the individual clients' data, especially in the case of electricity data, where prediction of peak demand is a challenge. A set of clustering techniques has been explored in the literature to improve prediction quality while maintaining user privacy. These studies have mainly been conducted using sets of clients with similar attributes that may not reflect real-world consumer diversity. This paper explores, implements and compares these clustering techniques for privacy-preserving load forecasting on a representative electricity consumption dataset. The experimental results demonstrate the effects of electricity consumption heterogeneity on federated forecasting and a non-representative sample's impact on load forecasting.

The vehicle-to-grid (V2G) network has a clear advantage in terms of economic benefits, and it has grabbed the interest of powergrid and electric vehicle (EV) consumers. Many V2G techniques, at present, for example, use bilinear pairing to execute the authentication scheme, which results in significant computational costs. Furthermore, in the existing V2G techniques, the system master key is issued independently by the third parties, it is vulnerable to leaking if the third party is compromised by an attacker. This paper presents an efficient and secure anonymous authentication scheme for V2G networks to overcome this issue we use a lightweight authentication system for electric vehicles and smart grids. In the proposed technique, the keys are generated by the trusted authority after the successful registration of EVs in the trusted authority and the dispatching center. The suggested scheme not only enhances the verification performance of V2G networks and also protects against inbuilt hackers.