In recent years, with the globalization of semiconductor processing and manufacturing, integrated circuits have gradually become vulnerable to malicious attackers. In order to detect Hardware Trojans (HTs) hidden in integrated circuits, it has become one of the hottest issues in the field of hardware security. In this paper, we propose to apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to hardware Trojan detection, using PCA algorithm to extract features from small differences in side channel information, and then obtain the principal components. The SVM detection model is optimized by means of cross-validation and logarithmic interval. Finally, it is determined whether the original circuit contains a hardware Trojan. In the experiment, we use the SAKURA-G FPGA board, Agilent oscilloscope, and ISE simulation software to complete the experimental work. The test results of five different HTs show that the average True Positive Rate (TPR) of the proposed method for HTs can reach 99.48\%, along with an average True Negative Rate (TNR) of 99.2\%, and an average detection time of 9.66s.

In order to visually present all kinds of hardware Trojan horse detection methods and their relationship, a method is proposed to construct the knowledge graph of hardware Trojan horse detection technology. Firstly, the security-related knowledge of hardware Trojan horse is analyzed, then the entity recognition and relationship extraction are carried out by using BiLSTM-CRF model, and the construction of knowledge graph is completed. Finally, the knowledge is stored and displayed visually by using graph database neo4j. The combination of knowledge graph and hardware Trojan security field can summarize the existing detection technologies, provide a basis for the analysis of hardware Trojans, vigorously promote the energy Internet security construction, and steadily enhance the energy Internet active defense capability.

Outsourcing Integrated Circuits(ICs) pave the way for including malicious circuits commonly known as Hardware Trojans. Trojans can be divided into functional and parametric Trojans. Trojans of the first kind are made by adding or removing gates to or from the golden reference design. Trojans of the following type, the golden circuit is modified by decreasing connecting wire’s thickness, exposing the chip to radiation, etc. Hardware Trojan detection schemes can be broadly classified into dynamic and static detection schemes depending on whether or not the input stimulus is applied. The proposed method aims to detect functional Trojans using the static detection method. The work proposes a generic, scalable Trojan detection method. The defender does not have the luxury of knowing the type of Trojan the circuit is infected with, making it difficult for accurate detection. In addition, the proposed method does not require propagating the Trojan effect on the output, magnifying the Trojan effect, or any other voting or additional algorithms to accurately detect the Trojan as in previous literature. The proposed method analyses synthesis reports for Trojan detection. Game theory, in addition, aids the defender in optimal decisionmaking. The proposed method has been evaluated on ISCAS’85 and ISCAS’89 circuits. The proffered method detects various types of Trojans of varying complexities in less time and with 100\% accuracy.

With the development of streaming media, soft real-time system in today’s life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task.

Message-locked Encryption (MLE) is the most common approach used in encrypted deduplication systems. However, the systems based on MLE are vulnerable to frequency analysis attacks, because MLE encrypts the identical plaintexts into the identical ciphertexts, which is deterministic. The state-of-theart defense scheme, which named TED, lacks key verification and uses a single key server to record frequency information. Once the key server is compromised, TED will be vulnerable to brute-force attacks. In addition, TED’s key generation algorithm needs to be designed more exquisitely to strengthen protection, and its security indicator is not comprehensive. We propose SDAF, which supports key verification and enhanced protection against frequency analysis attacks. Based on chameleon hash, SDAF realizes key verification to prevent malicious key servers from generating fake encryption keys. In order to disturb the frequency information, SDAF introduces reservoir sample to generate uniformly distributed encryption keys, and uses multiple key servers, which interact with each other via multi-party PSI and rotate spontaneously to avoid the single point of failure. Moreover, a new indicator Kurtosis is pointed out to evaluate the security against frequency analysis attacks. We implement the prototypes of SDAF. The experiments of the real-world data sets show that, compared with the existing schemes, SDAF can better resist frequency analysis attacks with lower time overheads.

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.

Inertia plays a key role in power system resistance to active power disturbance. Under the background of large-scale renewable energy participating in power systems, the problem of weak inertia support brings challenges to power system security and stability operation. Based on the analysis of system equivalent inertia time constant, the inertia time constant of renewable energy access to the system in different scenarios are solved in this paper. According to the effects of inertia time constant change on the dynamic characteristics of system frequency, the assessment indexes of equivalent inertia time constant and the rate of change of frequency (RoCoF) is proposed. Then the inertia of high proportional renewable energy system and frequency stability is evaluated, combined with the assessment index of frequency deviation. Finally, the maximum renewable energy penetration of the system is analyzed with the proposed indexes. IEEE 30-bus system is used to verify the effectiveness of the proposed method by analyzing the RoCoF and equivalent inertia time constant assessment indexes.

Round-trip transmission scheme is one of key scheme for the high-precise fiber time synchronization system. Here an asymmetric channel attack against practical roundtrip time synchronization system is proposed and experimentally demonstrated. Using the achieved asymmetric channel attack module, the accuracy of the time synchronization system can be reduced from 90 ps to 538 ps as designed. It shows that channel symmetry assumption in practical applications could be broken by such attack method, and this attack could not be found without single-way-delay monitoring.

Mechanical vibration signals of GIS equipment are important information to reflect the operating status of equipment, but the vibration excitation of existing research is mostly based on a single power frequency current, and the detection effect has certain limitations. Therefore, in order to explore the influence of current frequency on GIS mechanical vibration characteristics, this paper carried out research on GIS mechanical vibration characteristics under variable frequency current excitation. Firstly, the mechanical vibration simulation platform of 110 kV GIS equipment under variable frequency current excitation was built in the laboratory. Then, the vibration signals generated by the equipment shell under normal operation state were collected based on the mechanical vibration detection system. Finally, the evolution laws of time domain and frequency domain vibration spectra of GIS equipment under different current frequencies and loads were studied. The results show that the overall time domain waveforms are smooth and the main vibration frequencies are twice the frequencies of excitation currents. Under the condition of the variable frequency current excitation with the same amplitude, the amplitudes of time domain and frequency domain vibration spectra of vibration signals are the largest when the GIS equipment is excited by 1200 A current at 40 Hz and 2400 A current at 80 Hz. Under the condition of the variable amplitude currents excitation with the same frequency, the amplitudes of vibration signals are positively correlated with the amplitudes of currents, and the distributions of frequency spectra are highly concentrated.

Large-scale renewable energy participates in the power grid through power electronic equipment, which cannot provide stable and effective inertia support for the power system. Based on the rate of change of frequency at the time of disturbance and the virtual inertia control of the energy storage system, the supporting effect of the energy storage on the inertia of a high-proportional renewable energy system is analyzed in this paper. Then an energy storage capacity configuration calculation method is proposed considering the equivalent inertia time constant and virtual inertia control parameters. Next, the quantitative analysis index is proposed based on the supporting effect of inertia, which provides analysis methods for renewable energy participating in the power grid and energy storage capacity configuration. Finally, the IEEE 30-bus system is used to analyze system frequency response characteristics under different energy storage capacity configuration scenarios. The effectiveness of the proposed method is verified.

The paper presents the stages of constructing a highly informative digital image of the time-frequency representation of information signals of cyber-physical systems. Signal visualization includes the stage of displaying the signal on the frequency-time plane, the stage of two-dimensional digital filtering and the stage of extracting highly informative components of the signal image. The use of two-dimensional digital filtering allows you to select the most informative component of the image of a complex analyzed information signal. The obtained digital image of the signal of the cyber-physical system is a highly informative initial information for solving a wide range of different problems of information security systems in cyberphysical systems with the subsequent use of machine learning technologies.

This paper studies a power conversion system supplying a High-Speed Permanent Magnet Motor (HSPMM). In opposite of classical approach, this study observes a dynamic trajectory modelling an electric drive chain with a constant acceleration of the machine to its nominal speed. This global approach allows to observe different phenomena at the same time (resonance, subharmonic, and harmonic distortion - THD) specific to the trajectory. The method reconciles electrical phenomena with a powerful mechanism of analysis from the Short-Time Fourier Transform (STFT) and the visual representation of the frequency spectrum (spectrogram tool). The Predictive Time-Frequency analysis applied on Electric Drive Systems (PreTiFEDS) offers a powerful tool for engineers and electric conversion system architects when designing the drive system chain.

Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-anonymity algorithm is utilized to prevent malicious users from accessing patients personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the Kanonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with kanonymity is 97.058\%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-anonymity has an accuracy score of 97.1429\%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions.

Data anonymization is one of the most important directions in privacy-preserving. However, research shows that simple anonymization of data does not protect privacy. To solve this problem, we present a novel and effective algorithm named tree-based K-degree anonymity (TKDA). We devise a new anonymity sequence generation method to reduce the information loss for social graphs. Then, the dynamic anonymization process is implemented by a depth-first search (DFS) traversal algorithm. Finally, the graph modification algorithm based on the anonymous sequence can keep the original graph structure stable. Average Path Length (APL), Average Clustering Coefficient (ACC), and Transitivity (T) are employed to evaluate the method. Experimental results on several datasets show that TKDA is closer to the values of the original graphs on the correlated three experimental metrics, which indicates that TKDA portrays the real data in more detail and improves the utility of the released data.

The Internet as a whole is a large network of interconnected computer networks and their supporting infrastructure which is divided into 3 parts. The web is a list of websites that can be accessed using search engines like Google, Firefox, and others, this is called as Surface Web. The Internet’s layers stretch well beyond the surface material that many people can quickly reach in their everyday searches. The Deep Web material, which cannot be indexed by regular search engines like Google, is a subset of the internet. The Dark Web, which extends to the deepest reaches of the Deep Web, contains data that has been purposefully hidden. Tor may be used to access the dark web. Tor employs a network of volunteer devices to route users web traffic via a succession of other users computers, making it impossible to track it back to the source. We will analyze and include results about the Dark Web’s presence in various spheres of society in this paper. Further we take dive into about the Tor metrics how the relay list is revised after users are determined based on client requests for directories (using TOR metrics). Other way we can estimate the number of users in anonymous networks. This analysis discusses the purposes for which it is frequently used, with a focus on cybercrime, as well as how law enforcement plays the adversary position. The analysis discusses these secret Dark Web markets, what services they provide, and the events that take place there such as cybercrime, illegal money transfers, sensitive communication etc. Before knowing anything about Dark Web, how a rookie can make mistake of letting any threat or malware into his system. This problem can be tackled by knowing whether to use Windows, or any other OS, or any other service like VPN to enter Dark world. The paper also goes into the agenda of how much of illegal community is involved from India in these markets and what impact does COVID-19 had on Dark Web markets. Our analysis is carried out by searching scholarly journal databases for current literature. By acting as a reference guide and presenting a research agenda, it contributes to the field of the dark web in an efficient way. This paper is totally built for study purposes and precautionary measures for accessing Dark Web.

E-voting plays a vital role in guaranteeing and promoting social fairness and democracy. However, traditional e-voting schemes rely on a centralized organization, leading to a crisis of trust in the vote-counting results. In response to this problem, researchers have introduced blockchain to realize decentralized e-voting, but the adoption of blockchain also brings new issues in terms of flexibility, anonymity, and usability. To this end, in this paper, we propose WeVoting, which provides weightbased flexibility with solid anonymity and enhances usability by designing a voter-independent on-chain counting mechanism. Specifically, we use distributed ElGamal homomorphic encryption and zero-knowledge proof to achieve voting anonymity with weight. Besides, WeVoting develops a counter-based counting mechanism to enhance usability compared with those self-tallying schemes. By critically designing an honesty-and-activity-based incentive algorithm, WeVoting can guarantee a correct counting result even in the presence of malicious counters. Our security and performance analyses elaborate that WeVoting achieves high anonymity in weighed voting under the premise of meeting the basic security requirements of e-voting. And meanwhile, its counting mechanism is sufficient for practical demands with reasonable overheads.

According to the idea of zero trust, this paper proposed an anonymous identity authentication scheme based on hash functions and pseudo-random number generators, which effectively increased the anonymity and confidentiality when users use the mobile networks, and ensure the security of the server. This scheme first used single-packet authentication technology to realize the application stealth. Secondly, hash functions and pseudo-random number generators were used to replace public key cryptosystems and time synchronization systems, which improved system performance. Thirdly, different methods were set to save encrypted information on the user s mobile device and the server, which realized different forms of anonymous authentication and negotiates a secure session key. Through security analysis, function and performance comparison, the results showed that the scheme had better security, flexibility and practicality, while maintained good communication efficiency.

The development of science and technology has led to the construction of smart cities, and in this scenario, there are many applications that need to provide their real-time location information, which is very likely to cause the leakage of personal location privacy. To address this situation, this paper designs a location privacy protection scheme based on graph anonymity, which is based on the privacy protection idea of K-anonymity, and represents the spatial distribution among APs in the form of a graph model, using the method of finding clustered noisy fingerprint information in the graph model to ensure a similar performance to the real location fingerprint in the localization process, and thus will not be distinguished by the location providers. Experiments show that this scheme can improve the effectiveness of virtual locations and reduce the time cost using greedy strategy, which can effectively protect location privacy.

The paper presents a Tbps-class anonymity router that supports both an anonymity protocol and IP by leveraging a programmable switch. The key design issue is to place both the compute-intensive header decryption function for anonymity protocol forwarding and the memory-intensive IP forwarding function on the processing pipes of a switch with satisfying its hardware requirements. A prototype router on a programmable switch achieves Tbps-scale forwarding.

Anonymity systems are widely used nowadays to protect user identity, but there are various threats currently in the anonymity network, such as virtual private networks, onion routing, and proxy servers. This paper looked at the different anonymity networks that are already out there and proposed a new model to stay anonymous on the internet by using open source tools and methods. This eliminates the current threats. It works by creating a virtual instance on the cloud server and configuring it using open source technologies such as OpenVPN. This model uses elastic cloud computing technology running over existing technologies such as virtual private networks and onion routing. The framework is a new way to stay anonymous on the internet. It is made up of only open source technologies.

The infrastructure required for data storage and processing has become increasingly feasible, and hence, there has been a massive growth in the field of data acquisition and analysis. This acquired data is published, empowering organizations to make informed data-driven decisions based on previous trends. However, data publishing has led to the compromise of privacy as a result of the release of entity-specific information. PrivacyPreserving Data Publishing [1] can be accomplished by methods such as Data S wapping, Differential Privacy, and the likes of k-Anonymity. k-Anonymity is a well-established method used to protect the privacy of the data published. We propose a clustering-based novel algorithm named SAC or the S core, Arrange, and Cluster Algorithm to pre serve privacy based on k-Anonymity. This method outperforms existing methods such as the Mondrian Algorithm by K. LeFevre and the One-pass K-means Algorithm by Jun-Lin Lin from a data quality perspective. S AC can be used to overcome temporal attack across subsequent releases of published data. To measure data quality post anonymization we present a metric that takes into account the relative loss in the information, that occurs while generalizing attribute values.

State-of-the-art template reconstruction attacks assume that an adversary has access to a part or whole of the functionality of a target model. However, in a practical scenario, rigid protection of the target system prevents them from gaining knowledge of the target model. In this paper, we propose a novel template reconstruction attack method utilizing a feature converter. The feature converter enables an adversary to reconstruct an image from a corresponding compromised template without knowledge about the target model. The proposed method was evaluated with qualitative and quantitative measures. We achieved the Successful Attack Rate(SAR) of 0.90 on Labeled Faces in the Wild Dataset(LFW) with compromised templates of only 1280 identities.

Satellite technologies are used for both civil and military purposes in the modern world, and typical applications include Communication, Navigation and Surveillance (CNS) services, which have a direct impact several economic, social and environmental protection activity. The increasing reliance on satellite services for safety-of-life and mission-critical applications (e.g., transport, defense and public safety services) creates a severe, although often overlooked, security problem, particularly when it comes to cyber threats. Like other increasingly digitized services, satellites and space platforms are vulnerable to cyberattacks. Thus, the existence of cybersecurity flaws may pose major threats to space-based assets and associated key infrastructure on the ground. These dangers could obstruct global economic progress and, by implication, international security if they are not properly addressed. Mega-constellations make protecting space infrastructure from cyberattacks much more difficult. This emphasizes the importance of defensive cyber countermeasures to minimize interruptions and ensure efficient and reliable contributions to critical infrastructure operations. Very importantly, space systems are inherently complex Cyber-Physical System (CPS) architectures, where communication, control and computing processes are tightly interleaved, and associated hardware/software components are seamlessly integrated. This represents a new challenge as many known physical threats (e.g., conventional electronic warfare measures) can now manifest their effects in cyberspace and, vice-versa, some cyber-threats can have detrimental effects in the physical domain. The concept of cyberspace underlies nearly every aspect of modern society s critical activities and relies heavily on critical infrastructure for economic advancement, public safety and national security. Many governments have expressed the desire to make a substantial contribution to secure cyberspace and are focusing on different aspects of the evolving industrial ecosystem, largely under the impulse of digital transformation and sustainable development goals. The level of cybersecurity attained in this framework is the sum of all national and international activities implemented to protect all actions in the cyber-physical ecosystem. This paper focuses on cybersecurity threats and vulnerabilities in various segments of space CPS architectures. More specifically, the paper identifies the applicable cyber threat mechanisms, conceivable threat actors and the associated space business implications. It also presents metrics and strategies for countering cyber threats and facilitating space mission assurance.

Recommender systems are powerful tools which touch on numerous aspects of everyday life, from shopping to consuming content, and beyond. However, as other machine learning models, recommender system models are vulnerable to adversarial attacks and their performance could drop significantly with a slight modification of the input data. Most of the studies in the area of adversarial machine learning are focused on the image and vision domain. There are very few work that study adversarial attacks on recommender systems and even fewer work that study ways to make the recommender systems robust and reliable. In this study, we explore two stateof-the-art adversarial attack methods proposed by Tang et al. [1] and Christakopoulou et al. [2] and we report our proposed defenses and experimental evaluations against these attacks. In particular, we observe that low-rank reconstructions and/or transformation of the attacked data has a significant alleviating effect on the attack, and we present extensive experimental evidence to demonstrate the effectiveness of this approach. We also show that a simple classifier is able to learn to detect fake users from real users and can successfully discard them from the dataset. This observation elaborates the fact that the threat model does not generate fake users that mimic the same behavior of real users and can be easily distinguished from real users’ behavior. We also examine how transforming latent factors of the matrix factorization model into a low-dimensional space impacts its performance. Furthermore, we combine fake users from both attacks to examine how our proposed defense is able to defend against multiple attacks at the same time. Local lowrank reconstruction was able to reduce the hit ratio of target items from 23.54\% to 15.69\% while the overall performance of the recommender system was preserved.

Probabilistic model checking is a useful technique for specifying and verifying properties of stochastic systems including randomized protocols and reinforcement learning models. However, these methods rely on the assumed structure and probabilities of certain system transitions. These assumptions may be incorrect, and may even be violated by an adversary who gains control of some system components.