Biblio

The load margin due to voltage instability and small-signal instability can be a valuable measure for the operator of the power system to ensure a continuous and safe supply of electricity. However, if this load margin was calculated without considering system operating requirements, then this margin may not be adequate. This article proposes an algorithm capable of providing the power system load margin considering the requirements of voltage stability, small-signal stability, and operational requirements, as limits of reactive power generation of synchronous generators in dynamic security assessment. Case studies were conducted in the 107-bus reduced order Brazilian system considering a list of contingencies and directions of load growth.

The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57- and 118-bus test system, outperforming relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

Modern embedded IoT devices are an attractive target for cyber attacks. For example, they can be used to disable entire factories and ask for ransom. Recovery of compromised devices is not an easy task, because malware can subvert the original software and make itself persistent. In addition, many embedded devices do not implement remote recovery procedures and, therefore, require manual intervention.Recent proposals from NIST and TCG define concepts and building blocks for cyber resilience: protection, detection and recovery. In this paper, we describe a system which allows implementing cyber resilient IoT devices that can be recovered remotely and timely. The proposed architecture consists of trusted data monitoring, local and remote attack detection, and enforced connections to remote services as building blocks for attack detection and recovery. Further, hardware- and software-based implementations of such a system are presented.

Professional training is essential for organizations to successfully defend their assets against cyber-attacks. Successful detection and prevention of security incidents demands that personnel is not just aware about the potential threats, but its security expertise goes far beyond the necessary background knowledge. To fill-in the gap for competent security professionals, platforms offering realistic training environments and scenarios are designed that are referred to as cyber-ranges. Multiple cyber-ranges listed under a common platform can simulate more complex environments, referred as cyber-range federations. Security education approaches often implement gamification mechanics to increase trainees’ engagement and maximize the outcome of the training process. Scoring is an integral part of a gamification scheme, allowing both the trainee and the trainer to monitor the former’s performance and progress. In this article, a novel scoring model is presented that is designed to be agnostic with respect to the source of information: either a CR or a variety of different CRs being part of a federated environment.

The paper gives an overview of the ICS security and focuses on Control Systems. Use of internet had security challenges which led to the development of ICS which is designed to be dependable and safe. PCS, DCS and SCADA all are subsets of ICS. The paper gives a description of the developments in the ICS security and covers the most interesting work done by researchers. The paper also provides research information about the parameters on which a remotely executed cyber-attack depends.

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the most prominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.

The correctness of security control system strategy is very important to ensure the stability of power system. Aiming at the problem that the current security control strategy verification method is not enough to match the increasingly complex large power grid, this paper proposes a cyclic verification method of security control system strategy table based on constraints and whole process dynamic simulation. Firstly, the method is improved based on the traditional security control strategy model to make the strategy model meet certain generalization ability; And on the basis of this model, the cyclic dynamic verification of the strategy table is realized based on the constraint conditions and the whole process dynamic simulation, which not only ensures the high accuracy of strategy verification for the security control strategy of complex large power grid, but also ensures that the power system is stable and controllable. Finally, based on a certain regional power system, the optimal verification of strategy table verification experiment is realized. The experimental results show that the average processing time of the proposed method is 10.32s, and it can effectively guarantee the controllability and stability of power grid.

As a typical cyber-physical system, the power system utilizes advanced information and communication technologies to transmit crucial control signals in communication channels. However, many adversaries can construct false data injection attacks (FDIA) to circumvent traditional bad data detection and break the stability of the power grid. In this paper, we proposed a threat-maximizing FDIA model from the view of attackers. The proposed FDIA can not only circumvent bad data detection but can also cause a terrible fluctuation in the power system. Furthermore, in order to eliminate potential attack threats, the Spatio-temporal correlations of measurement matrices are considered. To extract the Spatio-temporal features, a data-driven detection method using a deep convolutional neural network was proposed. The effectiveness of the proposed FDIA model and detection are assessed by a simulation on the New England 39 bus system. The results show that the FDIA can cause a negative effect on the power system’s stable operation. Besides, the results reveal that the proposed FDIA detection method has an outstanding performance on Spatio-temporal features extraction and FDIA recognition.

Arbitrary style transfer refers to generate a new image based on any set of existing images. Meanwhile, the generated image retains the content structure of one and the style pattern of another. In terms of content retention and style transfer, the recent arbitrary style transfer algorithms normally perform well in one, but it is difficult to find a trade-off between the two. In this paper, we propose the Deep Content Guidance Network (DCGN) which is stacked by content guidance (CG) layers. And each CG layer involves one position self-attention (pSA) module, one channel self-attention (cSA) module and one content guidance attention (cGA) module. Specially, the pSA module extracts more effective content information on the spatial layout of content images and the cSA module makes the style representation of style images in the channel dimension richer. And in the non-local view, the cGA module utilizes content information to guide the distribution of style features, which obtains a more detailed style expression. Moreover, we introduce a new permutation loss to generalize feature expression, so as to obtain abundant feature expressions while maintaining content structure. Qualitative and quantitative experiments verify that our approach can transform into better stylized images than the state-of-the-art methods.

In smart girds, supervisory control and data acquisition (SCADA) systems have to protect data from advanced persistent threats (APTs), which exploit vulnerabilities of the power infrastructures to launch stealthy and targeted attacks. In this paper, we propose a reinforcement learning-based APT defense scheme for the control center to choose the detection interval and the number of Central Processing Units (CPUs) allocated to the data concentrators based on the data priority, the size of the collected meter data, the history detection delay, the previous number of allocated CPUs, and the size of the labeled compromised meter data without the knowledge of the attack interval and attack CPU allocation model. The proposed scheme combines deep learning and policy-gradient based actor-critic algorithm to accelerate the optimization speed at the control center, where an actor network uses the softmax distribution to choose the APT defense policy and the critic network updates the actor network weights to improve the computational performance. The advantage function is applied to reduce the variance of the policy gradient. Simulation results show that our proposed scheme has a performance gain over the benchmarks in terms of the detection delay, data protection level, and utility.

To provide web browsing and video streaming services with desirable quality, cache servers have been widely used to deliver digital data to users from locations close to users. For example, in the MEC (mobile edge computing), cache memories are provided at base stations of 5G cellular networks to reduce the traffic load in the backhaul networks. Cache servers are also connected to many edge routers in the CDN (content delivery network), and they are provided at routers in the ICN (information-centric networking). However, the cache pollution attack (CPA) which degrades the cache hit ratio by intentionally sending many requests to non-popular contents will be a serious threat in the cache networks. Quickly detecting the CPA hosts and protecting the cache servers is important to effectively utilize the cache resources. Therefore, in this paper, we propose a method of accurately detecting the CPA hosts using a limited amount of memory resources. The proposed method is based on a Bloom filter using the combination of identifiers of host and content as keys. We also propose to use two Bloom filters in parallel to continuously detect CPA hosts. Through numerical evaluations, we show that the proposed method suppresses the degradation of the cache hit ratio caused by the CPA while avoiding the false identification of legitimate hosts.

One of the many methods for identifying malware is to disassemble the malware files and obtain the opcodes from them. Since malware have predominantly been found to contain specific opcode sequences in them, the presence of the same sequences in any incoming file or network content can be taken up as a possible malware identification scheme. Malware detection systems help us to understand more about ways on how malware attack a system and how it can be prevented. The proposed method analyses malware executable files with the help of opcode information by converting the incoming executable files to assembly language thereby extracting opcode information (opcode count) from the same. The opcode count is then converted into opcode frequency which is stored in a CSV file format. The CSV file is passed to various machine learning algorithms like Decision Tree Classifier, Random Forest Classifier and Naive Bayes Classifier. Random Forest Classifier produced the highest accuracy and hence the same model was used to predict whether an incoming file contains a potential malware or not.

This paper presents some of our first experiences and findings in the ARPA-E project ReNew100, which is to develop an operator support system to enable stable operation of power system with 100% non-synchronous (NS) generation. The key to 100% NS system, as found in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. In this paper, we demonstrate in Electro-Magnetic-Transient (EMT) simulations, based on Hawai'i big island system with 100% NS capacity, that a system can be operated stably with the help of GFM inverters and appropriate controller parameters for the inverters. The dynamic security optimization (DSO) is introduced for optimizing the inverter control parameters to improve stability of the system towards N-1 contingencies. DSO is verified for five critical N-1 contingencies of big island system identified by Hawaiian Electric. The simulation results show significant stability improvement from DSO. The results in this paper share some insight, and provide a promising solution for operating grid in general with high penetration or 100% of NS generation.

In this work, we have proposed a state-of-the-art face logging system that detects and logs high quality cropped face images of the people in real-time for security applications. Multiple strategies based on resolution, velocity and symmetry of faces have been applied to obtain best quality face images. The proposed system handles the issue of motion blur in the face images by determining the velocities of the detections. The output of the system is the face database, where four faces for each detected person are stored along with the time stamp and ID number tagged to it. The facial features are extracted by our system, which are used to search the person-of-interest instantly. The proposed system has been implemented in a docker container environment on two edge devices: the powerful NVIDIA Jetson TX2 and the cheaper NVIDIA Jetson N ano. The light and fast face detector (LFFD) used for detection, and ResN et50 used for facial feature extraction are optimized using TensorRT over these edge devices. In our experiments, the proposed system achieves the True Acceptance Rate (TAR) of 0.94 at False Acceptance Rate (FAR) of 0.01 while detecting the faces at 20–30 FPS on NVIDIA Jetson TX2 and about 8–10 FPS on NVIDIA Jetson N ano device. The advantage of our system is that it is easily deployable at multiple locations and also scalable based on application requirement. Thus it provides a realistic solution to face logging application as the query or suspect can be searched instantly, which may not only help in investigation of incidents but also in prevention of untoward incidents.

With the advancement of portable head-mounted displays, interest in educational application of immersive spherical video-based virtual reality (SVVR) has been emerging. However, it remains unclear regarding the effects of immersive SVVR on cognitive and affective outcomes. In this study, we retrieved 58 learning outcomes from 16 studies. A meta-analysis was performed using the random effects model to calculate the effect size. Several important moderators were also examined such as control group treatment, learning outcome type, interaction functionality, content instruction, learning domain, and learner's stage. The results show that immersive SVVR is more effective than other instructional conditions with a medium effect size. The key findings of the moderator analysis are that immersive SVVR has a greater impact on affective outcomes, as well as under the conditions that learning system provides interaction functionality or integrates with content instruction before virtual exploratory learning.

Blockchain is opening up new avenues for the development of new sorts of digital services. In this article, we'll employ the transparent Blockchain method to propose a system for collecting data from many sources and databases for use in local and national elections. The Blockchain-based system will be safe, trustworthy, and private. It will assist to know the overall count of the candidates who participated and it functions in the same way as people's faith in their governments does. Blockchain technology is the one that handles the actual vote. We use the secure hash algorithm for resolving this problem and tried to bring a solution through the usage of this booming technology. A centralized database in a blockchain system keeps track of the secure electronic interactions of users in a peer-to-peer network.

Cyber threat information can be utilized to investigate incidents by leveraging threat-related knowledge from prior incidents with digital forensic techniques and tools. However, the actionability of cyber threat information in digital forensics has not yet been evaluated. Such evaluation is important to ascertain that cyber threat information is as actionable as it can be and to reveal areas of improvement. In this study, a dataset of cyber threat information products was created from well-known cyber threat information sources and its actionability in digital forensics was evaluated. The evaluation results showed a high level of cyber threat information actionability that still needs enhancements in supporting some widely present types of attacks. To further enhance the provision of actionable cyber threat information, the development of the new TREVItoSTIX Autopsy module is presented. TREVItoSTIX allows the expression of the findings of an incident investigation in the structured threat information expression format in order to be easily shared and reused in future digital forensics investigations.

This work describes a concept for extending the Network Time Security (NTS) protocol to enable implementation- independent communication between the NTS key establishment (NTS-KE) server and the connected time server(s). It Alls a specification gap left by RFC 8915 for securing the Network Time Protocol (NTP) and enables the centralized and public deployment of an NTS key management server that can support both secured NTP and secured PTP.

Within the ASIC development process, the phase of functional verification is a major bottleneck that affects the product time to market. A technique that decreases the time cost for reaching functional coverage closure is reducing the stimuli redundancy during the test regressions. This paper addresses such a solution and presents a novel, efficient, and scalable implementation that harnesses the power of artificial neural networks. This article outlines the concept strategy, highlights the framework structure, lists the experimental results, and underlines future research directions.

Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.

Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing their models to learn incorrect inputs to serve the attackers' malicious needs. In this paper, we show how to automatically generate fake CTI text descriptions using transformers. Given an initial prompt sentence, a public language model like GPT-2 with fine-tuning can generate plausible CTI text that can mislead cyber-defense systems. We use the generated fake CTI text to perform a data poisoning attack on a Cybersecurity Knowledge Graph (CKG) and a cybersecurity corpus. The attack introduced adverse impacts such as returning incorrect reasoning outputs, representation poisoning, and corruption of other dependent AI-based cyber defense systems. We evaluate with traditional approaches and conduct a human evaluation study with cyber-security professionals and threat hunters. Based on the study, professional threat hunters were equally likely to consider our fake generated CTI and authentic CTI as true.

Hardware Trojans are malicious modules causing vulnerabilities in designs. Secured hardware designs are desirable in almost all applications. So, it is important to make a trustworthy design that actually exposes malfunctions when a Trojan is present in it. Recently, ring oscillator based detection methods are gaining prominence as they help in detecting Trojans accurately. In this work, a non-destructive method of Trojan detection by modifying the circuit paths into oscillators is proposed. The change in frequencies of ring oscillators upon taking the process corners into account, indicate the presence of Trojans. Since Transient Effect Ring Oscillators (TERO) are also emerging as a good alternative to classical ring oscillators in Trojan detection, an effort is made to analyze the detection capability. Evaluation is done using ISCAS'85 benchmark circuits. Comparison is done in terms of frequency and findings indicate that TERO based Trojan detection is precise. Evaluation is carried out using Xilinx Vivado and ModelSim platforms.

Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.

Stress-related disorders are increasing because of work load, forces in teamwork, surroundings pressures and health related conditions. Thus, to avoid people living under heavy stress and develop more severe stress-related disorders, different internet and applications of stress management interventions are offered. Mobile applications with self-assessed health, burnout-scores and well-being are commonly used as outcome measures. Few studies have used sickleave to compare effects of stress interventions. A new approach is to use nature and garden in a multimodal stress management context. This study aimed to explore the effects of immersive and non-immersive games application by using nature theme virtual stress therapy in reducing stress level. Two weeks’ of experiments had involved 18 participants. Nine (9) of them were invited to join the first experiment which focused on immersive virtual reality (VR) experience. Their Blood Volume Pulse with Heart Rate (BVP+HR) and Skin Conductance (SC) were recorded using BioGraph Infiniti Biofeedback System that comes with three (3) sensors attached to the fingers. The second experiment were joined by another nine (9) participants. This experiment was testing on non-immersive desktop control experience. The same protocol measurements were taken which are BVP+HR and SC. Participants were given the experience to feel and get carried into the virtual nature as a therapy so that they will reduce stress. The result of this study points to whether immersive or non-immersive VR display using nature theme virtual therapy would reduce individuals stress level. After conducted series of experiments, results showed that both immersive and non-immersive VR display reduced stress level. However, participants were satisfied of using the immersive version as it provided a 360 degree of viewing, immersed experiences and feeling engaged. Thus, this showed and proved that applications developed with nature theme affect successfully reduce stress level no matter it is put in immersive or non-immersive display.