Biblio

Demand response has emerged as one of the most promising methods for the deployment of sustainable energy systems. Attempts to democratize demand response and establish programs for residential consumers have run into scalability issues and risks of leaking sensitive consumer data. In this work, we propose a privacy-friendly, incentive-based demand response market, where consumers offer their flexibility to utilities in exchange for a financial compensation. Consumers submit encrypted offer which are aggregated using Computation Over Encrypted Data to ensure consumer privacy and the scalability of the approach. The optimal allocation of flexibility is then determined via double-auctions, along with the optimal consumption schedule for the users with respect to the day-ahead electricity prices, thus also shielding participants from high electricity prices. A case study is presented to show the effectiveness of the proposed approach.

Web browsers are among the most important but also complex software solutions to access the web. It is therefore not surprising that web browsers are an attractive target for attackers. Especially in the last decade, security researchers and browser vendors have developed sandboxing mechanisms like security-relevant HTTP headers to tackle the problem of getting a more secure browser. Although the security community is aware of the importance of security-relevant HTTP headers, legacy applications and individual requests from different parties have led to possible insecure configurations of these headers. Even if specific security headers are configured correctly, conflicts in their functionalities may lead to unforeseen browser behaviors and vulnerabilities. Recently, the first work which analyzed duplicated headers and conflicts in headers was published by Calzavara et al. at USENIX Security [1]. The authors focused on inconsistent protections by using both, the HTTP header X-Frame-Options and the framing protection of the Content-Security-Policy.We extend their work by analyzing browser behaviors when parsing duplicated headers, conflicting directives, and values that do not conform to the defined ABNF metalanguage specification. We created an open-source testbed running over 19,800 test cases, at which nearly 300 test cases are executed in the set of 66 different browsers. Our work shows that browsers conform to the specification and behave securely. However, all tested browsers behave differently when it comes, for example, to parsing the Strict-Transport-Security header. Moreover, Chrome, Safari, and Firefox behave differently if the header contains a character, which is not allowed by the defined ABNF. This results in the protection mechanism being fully enforced, partially enforced, or not enforced and thus completely bypassable.

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.

With the development of the information age, the process of global networking continues to deepen, and the cyberspace security has become an important support for today’s social functions and social activities. Web applications which have many security risks are the most direct interactive way in the process of the Internet activities. That is why the web applications face a large number of network attacks. Interpretive dynamic programming languages are easy to lean and convenient to use, they are widely used in the development of cross-platform web systems. As well as benefit from these advantages, the web system based on those languages is hard to detect errors and maintain the complex system logic, increasing the risk of system vulnerability and cyber threats. The attack defense of systems based on interpretive dynamic programming languages is widely concerned by researchers. Since the advance of endogenous security technologies, there are breakthroughs on the research of web system security. Compared with traditional security defense technologies, these technologies protect the system with their uncertainty, randomness and dynamism. Based on several common network attacks, the traditional system security defense technology and endogenous security technology of web application based on interpretive dynamic languages are surveyed and compared in this paper. Furthermore, the possible research directions of those technologies are discussed.

This article discusses a threat and vulnerability analysis model that allows you to fully analyze the requirements related to information security in an organization and document the results of the analysis. The use of this method allows avoiding and preventing unnecessary costs for security measures arising from subjective risk assessment, planning and implementing protection at all stages of the information systems lifecycle, minimizing the time spent by an information security specialist during information system risk assessment procedures by automating this process and reducing the level of errors and professional skills of information security experts. In the initial sections, the common methods of risk analysis and risk assessment software are analyzed and conclusions are drawn based on the results of comparative analysis, calculations are carried out in accordance with the proposed model.

This study explores the Critical Success Factors (CSFs) for Security Education, Training and Awareness (SETA) programmes. Data is gathered from 20 key informants (using semi-structured interviews) from various geographic locations including the Gulf nations, Middle East, USA, UK, and Ireland. The analysis of these key informant interviews produces eleven CSFs for SETA programmes. These CSFs are mapped along the phases of a SETA programme lifecycle (design, development, implementation, and evaluation).

Nowadays, the messaging system is one of the most popular mobile applications, and therefore the authentication between clients is essential. Various kinds of such mobile applications are using encryption-based security protocols, but they are facing many security threat issues. It clearly defines the necessity for a trustful security procedure. Therefore, a blockchain-based messaging system could be an alternative to this problem. That is why, we have developed a secured peer-to-peer messaging system supported by blockchain. This proposed mechanism provides data security among the users. In a blockchain-based framework, all the information can be verified and controlled automatically and all the transactions are recorded that have been created already. In our paper, we have explained how the users can communicate through a blockchain-based messaging system that can maintain a secured network. We explored why blockchain would improve communication security in this post, and we proposed a model architecture for blockchain-based messaging that retains the performance and security of data stored on the blockchain. Our proposed architecture is completely decentralized and enables users to send and receive messages in an acceptable and secure manner.

We propose versatile hardware framework for ECC. The framework supports arithmetic operations over P-256, Ed25519 and Curve25519 curves, enabling easy implementation of various ECC algorithms. Framework finds its application area e.g. in FIDO2 attestation or in nowadays rapidly expanding field of hardware wallets. As the design is intended to be ASIC-ready, we designed it to be area efficient. Hardware units are reused for calculations in several finite fields, and some of them are superior to previously designed circuits in terms of time-area product. The framework implements several attack countermeasures. It enables implementation of certain countermeasures even in later stages of design. The design was validated on SoC FPGA.

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.

We have several issues in most current supply chain management systems. Consumers want to spend money on environmentally friendly products, but they are seldomly informed of the environmental contributions of the suppliers. Meanwhile, each supplier seeks to recover the costs for the environmental contributions to re-invest them into further contributions. Instead, in most current supply chains, the reward for each supplier is not clearly defined and fairly distributed. To address these issues, we propose a supply-chain contribution management platform for fair reward distribution called ‘Advanced Ledger.’ This platform records suppliers' environ-mental contribution trails, receives rewards from consumers in exchange for trail-backed fungible tokens, and fairly distributes the rewards to each supplier based on the contribution trails. In this paper, we overview the architecture of Advanced Ledger and 11 technical features, including decentralized autonomous organization (DAO) based contribution verification, contribution concealment, negative-valued tokens, fair reward distribution, atomic rewarding, and layer-2 rewarding. We then study the requirements and candidates of the smart contract platforms for implementing Advanced Ledger. Finally, we introduce a use case called ‘ESG token’ built on the Advanced Ledger architecture.

This study purpose was to examine the determinant factors that affect the Micro, Small, and Medium Enterprise (MSME) merchants who had the intention to use Quick Response Code Indonesian Standard (QRIS) as a payment system. QRIS was expected to be applied by merchants to diminish the virus spread and keep the circulation of money safe; but there were not many merchants using the QRIS as a payment method. The factors MSME merchant might not use the QRIS were related to perceived usefulness, perceived security, perceived ease of use, and trust. The population was MSMEs in South Tangerang City who did not use QRIS yet and the population was unknown. Using the Lemeshow formula, obtained a sample of 115 people, and the sampling technique used purposive sampling. Then data were analyzed using multi-regression analysis and processed by SPSS. The results indicated that perceived usefulness and perceived security had a significant affect on trust, whereas trust and ease of use significant affect the intention to use QRIS. Moreover, trust was able to mediate the perceived usefulness to intention to use. Since ease of use had no significant affect on trust, then the mediation given by trust to perceived ease of use had no significant affect on intention to use.

The development of IoT has penetrated various sectors. The development of IoT devices continues to increase and is predicted to reach 75 billion by 2025. However, the development of IoT devices is not followed by security developments. Therefore, IoT devices can become gateways for cyber attacks, including brute force and sniffing attacks. Authentication mechanisms can be used to ward off attacks. However, the implementation of authentication mechanisms on IoT devices is challenging. IoT devices are dominated by constraint devices that have limited computing. Thus, conventional authentication mechanisms are not suitable for use. Two-factor authentication using RFID and fingerprint can be a solution in providing an authentication mechanism. Previous studies have proposed a two-factor authentication mechanism using RFID and fingerprint. However, previous research did not pay attention to message exchange security issues and did not provide mutual authentication. This research proposes a secure mutual authentication protocol using two-factor RFID and fingerprint using MQTT protocol. Two processes support the authentication process: the registration process and authentication. The proposed protocol is tested based on biometric security by measuring the false acceptance rate (FAR) and false rejection rate (FRR) on the fingerprint, measuring brute force attacks, and measuring sniffing attacks. The test results obtained the most optimal FAR and FRR at the 80% threshold. Then the equal error rate (ERR) on FAR and FRR is around 59.5%. Then, testing brute force and sniffing attacks found that the proposed protocol is resistant to both attacks.

Among the greatest obstacles in cybersecurity is insider threat, which is a well-known massive issue. This anomaly shows that the vulnerability calls for specialized detection techniques, and resources that can help with the accurate and quick detection of an insider who is harmful. Numerous studies on identifying insider threats and related topics were also conducted to tackle this problem are proposed. Various researches sought to improve the conceptual perception of insider risks. Furthermore, there are numerous drawbacks, including a dearth of actual cases, unfairness in drawing decisions, a lack of self-optimization in learning, which would be a huge concern and is still vague, and the absence of an investigation that focuses on the conceptual, technological, and numerical facets concerning insider threats and identifying insider threats from a wide range of perspectives. The intention of the paper is to afford a thorough exploration of the categories, levels, and methodologies of modern insiders based on machine learning techniques. Further, the approach and evaluation metrics for predictive models based on machine learning are discussed. The paper concludes by outlining the difficulties encountered and offering some suggestions for efficient threat identification using machine learning.

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

In the traditional Internet of Vehicles, communication data is easily tampered with and easily leaked. In order to improve the trust evaluation mechanism of the Internet of Vehicles and establish a trust relationship between vehicles, a blockchain-based Internet of Vehicles trust evaluation (BBTE) scheme is proposed. First, the scheme uses the roadside unit RSU to calculate the trust value of vehicle nodes and maintain the generation, verification and storage of blocks, so as to realize distributed data storage and ensure that data cannot be tampered with. Secondly, an efficient trust evaluation method is designed. The method integrates four trust decision factors: initial trust, historical experience trust, recommendation trust and RSU observation trust to obtain the overall trust value of vehicle nodes. In addition, in the process of constructing the recommendation trust method, the recommendation trust is divided into three categories according to the interaction between the recommended vehicle node and the communicator, use CRITIC to obtain the optimal weights of three recommended trusts, and use CRITIC to obtain the optimal weights of four trust decision-making factors to obtain the final trust value. Finally, the NS3 simulation platform is used to verify the security and accuracy of the trust evaluation method, and to improve the identification accuracy and detection rate of malicious vehicle nodes. The experimental analysis shows that the scheme can effectively deal with the gray hole attack, slander attack and collusion attack of other vehicle nodes, improve the security of vehicle node communication interaction, and provide technical support for the basic application of Internet of Vehicles security.

Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively.

This paper proposes a magnetic actuator using a partially magnetized FePt thick film as a permanent magnet and membrane material for bi-directional micropumps. The magnetized areas act as flux sources, while the magnetized and unmagnetized areas play a role of the membrane part. The mechanical and magnetic characterization results show FePt has a large tensile strength and a lower Young’s modulus than Si crystal, and a comparable remanence to NdFeB. A magnetic pattern transfer technique with a post thermal demagnetization is proposed and experimentally verified to magnetize the FePt partially. Using the proposed magnetic actuator with partially magnetized FePt film is beneficial to simplify the complicated structure and fabrication process of the bi-directional magnetic micropump besides other magnetic MEMS devices.

Side Channel Attacks (SCAs), an attack that exploits the physical information generated when an encryption algorithm is executed on a device to recover the key, has become one of the key threats to the security of encrypted devices. Recently, with the development of deep learning, deep learning techniques have been applied to SCAs with good results on publicly available dataset experiences. In this paper, we propose a power traces decomposition method that divides the original power traces into two parts, where the data-influenced part is defined as data power traces (Tdata) and the other part is defined as device constant power traces, and use the Tdata for training the network model, which has more obvious advantages than using the original power traces for training the network model. To verify the effectiveness of the approach, we evaluated the ATXmega128D4 microcontroller by capturing the power traces generated when implementing AES-128. Experimental results show that network models trained using Tdata outperform network models trained using raw power traces (Traw ) in terms of classification accuracy, training time, cross-subkey recovery key, and cross-device recovery key.

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.

Facial expression intensity, which quantifies the degree of facial expression, has been proposed. It is calculated based on how much facial feature values change compared to an expressionless face. The estimation has two aspects. One is to classify facial expressions, and the other is to estimate their intensity. However, it is difficult to do them at the same time. There- fore, in this work, the estimation of intensity and the classification of expression are separated. We suggest an explicit method and an implicit method. In the explicit one, a classifier determines which types of expression the inputs are, and each regressor determines its intensity. On the other hand, in the implicit one, we give zero values or non-zero values to regressors for each type of facial expression as ground truth, depending on whether or not an input image is the correct facial expression. We evaluated the two methods and, as a result, found that they are effective for facial expression recognition.

Software vulnerabilities threaten the security of computer system, and recently more and more loopholes have been discovered and disclosed. For the detected vulnerabilities, the relevant personnel will analyze the vulnerability characteristics, and combine the vulnerability scoring system to determine their severity level, so as to determine which vulnerabilities need to be dealt with first. In recent years, some characteristic description-based methods have been used to predict the severity level of vulnerability. However, the traditional text processing methods only grasp the superficial meaning of the text and ignore the important contextual information in the text. Therefore, this paper proposes an innovative method, called BERT-CNN, which combines the specific task layer of Bert with CNN to capture important contextual information in the text. First, we use Bert to process the vulnerability description and other information, including Access Gained, Attack Origin and Authentication Required, to generate the feature vectors. Then these feature vectors of vulnerabilities and their severity levels are input into a CNN network, and the parameters of the CNN are gotten. Next, the fine-tuned Bert and the trained CNN are used to predict the severity level of a vulnerability. The results show that our method outperforms the state-of-the-art method with 91.31% on F1-score.

This paper investigates the physical layer security of a cognitive radio (CR) non-orthogonal multiple-access (NOMA) network supported by an intelligent reflecting surface (IRS). In a CR network, a secondary base station (BS) serves a couple of users, i.e., near and far users, via NOMA transmission under eavesdropping from a malicious attacker. It is assumed that the direct transmission link from the BS and far user is absent due to obstacles. Thus, an IRS is utilized to support far user communication, however, the communication links between the IRS and near/primary users are neglected because of heavy attenuation. The exact secrecy outage probability (SOP) for the near user and approximate SOP for the far user are then derived in closed-form by using the Gauss-Chebyshev approach. The accuracy of the derived analytical SOP is then verified through Monte Carlo simulations. The simulation results also provide useful insights on the impacts of the number of IRS reflecting elements and limited interference temperature on the system SOP.

Imbalanced class distribution can cause information loss and missed/false alarms for deep learning and machine-learning algorithms. The detection performance of traditional intrusion detection systems tend to degenerate due to skewed class distribution caused by the uneven allocation of observations in different kinds of attacks. To combat class imbalance and improve network intrusion detection performance, we adopt the conditional generative adversarial network (CTGAN) that enables the generation of samples of specific classes of interest. CTGAN builds on the generative adversarial networks (GAN) architecture to model tabular data and generate high quality synthetic data by conditionally sampling rows from the generated model. Oversampling using CTGAN adds instances to the minority class such that both data in the majority and the minority class are of equal distribution. The generated security alerts are used for training classifiers that realize critical alert detection. The proposed scheme is evaluated on a real-world dataset collected from security operation center of a large enterprise. The experiment results show that detection accuracy can be substantially improved when CTGAN is adopted to produce a balanced security-alert dataset. We believe the proposed CTGAN-based approach can cast new light on building effective systems for critical alert detection with reduced missed/false alarms.

The vertiginous technological advance related to globalization and the new digital era has led to the design of new techniques and tools that deal with the risks of technology and information. Terms such as "cybersecurity" stand out, which corresponds to that area of computer science that is responsible for the development and implementation of information protection mechanisms and technological infrastructure, in order to deal with cyberattacks. Phishing is a crime that uses social engineering and technical subterfuge to steal personal identity data and financial account credentials from users, representing a high economic and financial risk worldwide, both for individuals and for large organizations. The objective of this research is to determine the ways to prevent phishing, by analyzing the characteristics of this computer fraud, the various existing modalities and the main prevention strategies, in order to increase the knowledge of users about this. subject, highlighting the importance of adequate training that allows establishing efficient mechanisms to detect and block phishing.

Operating systems have various components that produce artifacts. These artifacts are the outcome of a user’s interaction with an application or program and the operating system’s logging capabilities. Thus, these artifacts have great importance in digital forensics investigations. For example, these artifacts can be utilized in a court of law to prove the existence of compromising computer system behaviors. One such component of the Microsoft Windows operating system is Shellbag, which is an enticing source of digital evidence of high forensics interest. The presence of a Shellbag entry means a specific user has visited a particular folder and done some customizations such as accessing, sorting, resizing the window, etc. In this work, we forensically analyze Shellbag as we talk about its purpose, types, and specificity with the latest version of the Windows 11 operating system and uncover the registry hives that contain Shellbag customization information. We also conduct in-depth forensics examinations on Shellbag entries using three tools of three different types, i.e., open-source, freeware, and proprietary tools. Lastly, we compared the capabilities of tools utilized in Shellbag forensics investigations.