Biblio

The rapid improvement of computer and network technology not only promotes the improvement of productivity and facilitates people's life, but also brings new threats to production and life. Cyberspace security has attracted more and more attention. Different from traditional cyberspace security, APT attacks on key networks or infrastructure, with the main goal of stealing intellectual property, confidential information or sabotage, seriously threatening the interests and security of governments, enterprises and scientific research institutions. Timely detection and blocking is particularly important. The purpose of this paper is to study the security of software supply chain in power industry based on BAS technology. The experimental data shows that Type 1 projects account for the least amount and Type 2 projects account for the highest proportion. Type 1 projects have high unit price contracts and high profits, but the number is small and the time for signing orders is long.

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.

Facial emotion recognition helps feed the growth of the future artificial intelligence with the development of emotion recognition, learning, and analysis of different angles of a human face and head pose. The world's recent pandemic gave rise to the rapid installment of facial recognition for fewer applications, while emotion recognition is still within the experimental boundaries. The current challenges encountered with facial emotion recognition (FER) are the difference between background noises. Since today's world shows us that humans soon need robotics in the most significant role of human perception, attention, memory, decision-making, and human-robot interaction (HRI) needs employees. By merging the head pose as a combination towards the FER to boost the robustness in understanding emotions using the convolutional neural networks (CNN). The stochastic gradient descent with a comprehensive model is adopted by applying multi-task learning capable of implicit parallelism, inherent and better global optimizer in finding better network weights. After executing a multi-task learning model using two independent datasets, the experiment with the FER and head pose learning multi-views co-regularization frameworks were subsequently merged with validation accuracy.

Research on intelligent accounting system based on intelligent financial data sheet analysis system considering complex data mining is conducted in the paper. The expert audit system extracts business records from the business database according to the specified audit conditions, and the program automatically calculates the total amount of the amount data items, and then compares it with the standard or normal business, reflecting the necessary information such as differences and also possible audit trails. In order to find intrusion behaviors and traces, data collection is carried out from multiple points in the network system. The collection content includes system logs, network data packets, important files, and the status and the behavior of the user activities. Furthermore, complex data mining model is combined for the systematic analysis on the system performance. The simulation on the collected data is provided to the validate the performance.

In Production System Engineering (PSE), domain experts aim at effectively and efficiently analyzing and mitigating information security risks to product and process qualities for manufacturing. However, traditional security standards do not connect security analysis to the value stream of the production system nor to production quality requirements. This paper aims at facilitating security analysis for production quality already in the design phase of PSE. In this paper, we (i) identify the connection between security and production quality, and (ii) introduce the Production Security Network (PSN) to efficiently derive reusable security requirements and design patterns for PSE. We evaluate the PSN with threat scenarios in a feasibility study. The study results indicate that the PSN satisfies the requirements for systematic security analysis. The design patterns provide a good foundation for improving the communication of domain experts by connecting security and quality concerns.

The development of marine environment monitoring equipment has been improved by leaps and bounds in recent years. Numerous types of marine environment monitoring equipment have mushroomed with a wide range of high-performance capabilities. However, the existing data recording software cannot meet the demands of real-time and comprehensive data recording in view of the growing data types and the exponential data growth rate generated by various types of marine environment monitoring equipment. Based on the above-mentioned conundrum, this paper proposes a multi-source heterogeneous marine environmental data acquisition and storage method, which can record and replay multi-source heterogeneous data based upon the needs of real-time and accurate performance and also possess good compatibility and expandability.

Under the background of rapid development of campus informatization, the information management of college students' innovative activities is slightly outdated, and the operation of the traditional innovative activity record system has gradually become rigid. In response to this situation, this paper proposes a B/S architecture-based information management system for college students' innovative activities based on the current situation that the network and computers are widely used, which is designed for the roles of relevant managers of students on campus, such as class teachers, teachers and counselors, and has developed various functions to meet the needs of such users as class teachers, including user The system is designed to meet the needs of classroom teachers, classroom teachers and tutors. In order to meet the requirements of generality, expandability and ease of development, the overall architecture of the system is based on the javaEE platform, with JSP technology as the main development technology.

Cloud Computing revolutionize the usage of Internet of Things enabled devices integrated via Internet. Providing everything in an outsourced fashion, Cloud also lends infrastructures such as storage. Though cloud makes it easy for us to store and access the data faster and easier, yet there exist various security and privacy risks. Such issues if not handled may become more threatening as it could even disclose the privacy of an individual/ organization. Strengthening the security of data is need of the hour. The work proposes a novel architecture enhancing the security of Cloud data in an IoT integrated environment. In order to enhance the security, systematic use of a modified hybrid mechanism based on DNA code and Elliptic Curve Cryptography along with Third Party Audit is proposed. The performance of the proposed mechanism has been analysed. The results ensures that proposed IoT Cloud architecture performs better while providing strong security which is the major aspect of the work.

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

The Manufacturer Usage Description (MUD) standard aims to reduce the attack surface for IoT devices by locking down their behavior to a formally-specified set of network flows (access control entries). Formal network behaviors can also be systematically and rigorously verified in any operating environment. Enforcing MUD flows and monitoring their activity in real-time can be relatively effective in securing IoT devices; however, its scope is limited to endpoints (domain names and IP addresses) and transport-layer protocols and services. Therefore, misconfigured or compromised IoTs may conform to their MUD-specified behavior but exchange unintended (or even malicious) contents across those flows. This paper develops PicP-MUD with the aim to profile the information content of packet payloads (whether unencrypted, encoded, or encrypted) in each MUD flow of an IoT device. That way, certain tasks like cyber-risk analysis, change detection, or selective deep packet inspection can be performed in a more systematic manner. Our contributions are twofold: (1) We analyze over 123K network flows of 6 transparent (e.g., HTTP), 11 encrypted (e.g., TLS), and 7 encoded (e.g., RTP) protocols, collected in our lab and obtained from public datasets, to identify 17 statistical features of their application payload, helping us distinguish different content types; and (2) We develop and evaluate PicP-MUD using a machine learning model, and show how we achieve an average accuracy of 99% in predicting the content type of a flow.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.

In this work, we conduct a systematic study on data poisoning attacks to Matrix Factorisation (MF) based Recommender Systems (RS) where a determined attacker injects fake users with false user-item feedback, with an objective to promote a target item by increasing its rating. We explore the capability of a MF based approach to reduce the impact of attack on targeted item in the system. We develop and evaluate multiple techniques to update the user and item feature matrices when incorporating new ratings. We also study the effectiveness of attack under increasing filler items and choice of target item.Our experimental results based on two real-world datasets show that the observations from the study could be used to design a more robust MF based RS.

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.

National cultural security has existed since ancient times, but it has become a focal proposition in the context of the times and real needs. From the perspective of national security, national cultural security is an important part of national security, and it has become a strategic task that cannot be ignored in defending national security. Cultural diversity and imbalance are the fundamental prerequisites for the existence of national cultural security. Finally, the artificial intelligence algorithm is used as the theoretical basis for this article, the connotation and characteristics of China's national cultural security theory; Xi Jinping's "network view"; network ideological security view. The fourth part is the analysis of the current cultural security problems, hazards and their root causes in our country.

Over the past two decades, several forms of non-intrusive technology have been deployed in cooperation with medical specialists in order to aid patients diagnosed with some form of mental, cognitive or psychological condition. Along with the availability and accessibility to applications offered by mobile devices, as well as the advancements in the field of Artificial Intelligence applications and Natural Language Processing, Conversational Agents have been developed with the objective of aiding medical specialists detecting those conditions in their early stages and monitoring their symptoms and effects on the cognitive state of the patient, as well as supporting the patient in their effort of mitigating those symptoms. Coupled with the recent advances in the the scientific field of machine and deep learning, we aim to explore the grade of applicability of such technologies into cognitive health support Conversational Agents, and their impact on the acceptability of such applications bytheir end users. Therefore, we conduct a systematic literature review, following a transparent and thorough process in order to search and analyze the bibliography of the past five years, focused on the implementation of Conversational Agents, supported by Artificial Intelligence technologies and in service of patients diagnosed with Mild Cognitive Impairment and its variants.

For a long time, online attacks were regarded to pose a severe threat to web - based applications, websites, and clients. It can bypass authentication methods, steal sensitive information from datasets and clients, and also gain ultimate authority of servers. A variety of ways for safeguarding online apps have been developed and used to deal the website risks. Based on the studies about the intersection of cybersecurity and machine learning, countermeasures for identifying typical web assaults have recently been presented (ML). In order to establish a better understanding on this essential topic, it is necessary to study ML methodologies, feature extraction techniques, evaluate datasets, and performance metrics utilised in a systematic manner. In this paper, we go through web security flaws like SQLi, XSS, malicious URLs, phishing attacks, path traversal, and CMDi in detail. We also go through the existing security methods for detecting these threats using machine learning approaches for URL classification. Finally, we discuss potential research opportunities for ML and DL-based techniques in this category, based on a thorough examination of existing solutions in the literature.

As more and more information systems rely sen-sors for their critical decisions, there is a growing threat of injecting false signals to sensors in the analog domain. In particular, LightCommands showed that MEMS microphones are susceptible to light, through the photoacoustic and photoelectric effects, enabling an attacker to silently inject voice commands to smart speakers. Understanding such unexpected transduction mechanisms is essential for designing secure and reliable MEMS sensors. Is there any other transduction mechanism enabling laser-induced attacks? We positively answer the question by experimentally evaluating two commercial piezoresistive MEMS pressure sensors. By shining a laser light at the piezoresistors through an air hole on the sensor package, the pressure reading changes by ±1000 hPa with 0.5 mW laser power. This phenomenon can be explained by the photoelectric effect at the piezoresistors, which increases the number of carriers and decreases the resistance. We finally show that an attacker can induce the target signal at the sensor reading by shining an amplitude-modulated laser light.

Nowadays, improving the reliability and security of the transmitted data has gained more attention with the increase in emerging power-limited and lightweight communication devices. Also, the transmission needs to meet specific latency requirements. Combining data encryption and encoding in one physical layer block has been exploited to study the effect on security and latency over traditional sequential data transmission. Some of the current works target secure error-correcting codes that may be candidates for post-quantum computing. However, modifying the popularly used channel coding techniques to guarantee secrecy and maintain the same error performance and complexity at the decoder is challenging since the structure of the channel coding blocks is altered which results in less optimal decoding performance. Also, the redundancy nature of the error-correcting codes complicates the encryption method. In this paper, we briefly review the proposed security schemes on Turbo codes. Then, we propose a secure turbo code design and compare it with the relevant security schemes in the literature. We show that the proposed method is more secure without adding complexity.

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system’s security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security.

One of the fifth generation’s most promising solutions for addressing the network system capacity issue is the ultra-dense network. However, a new problem arises because the user equipment secure access is made up of access points that are independent, transitory, and dynamic. The APs are independent and equal in this. It is possible to think of it as a decentralized access network. The access point’s coverage is less than the standard base stations. The user equipment will interface with access points more frequently as it moves, which is a problem. The current 4G Authentication and Key Agreement method, however, is unable to meet this need for quick and frequent authentication. This study means to research how blockchain innovation is being utilized in production network the executives, as well as its forthcoming purposes and arising patterns. To more readily comprehend the direction of important exploration and illuminate the benefits, issues, and difficulties in the blockchain-production network worldview, a writing overview and a logical evaluation of the current examination on blockchain-based supply chains were finished. Multifaceted verification strategies have as of late been utilized as possible guards against blockchain attacks. To further develop execution, scatter administration, and mechanize processes, inventory network tasks might be upset utilizing blockchain innovation

A new framework is presented in this paper for proving coding theorems for linear codes, where the systematic bits and the corresponding parity-check bits play different roles. Precisely, the noisy systematic bits are used to limit the list size of typical codewords, while the noisy parity-check bits are used to select from the list the maximum likelihood codeword. This new framework for linear codes allows that the systematic bits and the parity-check bits are transmitted in different ways and over different channels. In particular, this new framework unifies the source coding theorems and the channel coding theorems. With this framework, we prove that the Bernoulli generator matrix codes (BGMCs) are capacity-achieving over binary-input output symmetric (BIOS) channels and also entropy-achieving for Bernoulli sources.

Explainable Artificial Intelligence (XAI) research focuses on effective explanation techniques to understand and build AI models with trust, reliability, safety, and fairness. Feature importance explanation summarizes feature contributions for end-users to make model decisions. However, XAI methods may produce varied summaries that lead to further analysis to evaluate the consistency across multiple XAI methods on the same model and data set. This paper defines metrics to measure the consistency of feature contribution explanation summaries under feature importance order and saliency map. Driven by these consistency metrics, we develop an XAI process oriented on the XAI criterion of feature importance, which performs a systematical selection of XAI techniques and evaluation of explanation consistency. We demonstrate the process development involving twelve XAI methods on three topics, including a search ranking system, code vulnerability detection and image classification. Our contribution is a practical and systematic process with defined consistency metrics to produce rigorous feature contribution explanations.