Biblio

The current study aims to discern the impact of expert systems and neural network on the Jordanian commercial banks. In achieving the objective, the study employed descriptive analytical approach and the population consisted of the 13 Jordanian commercial banks listed at Amman Stock Exchange-ASE. The primary data were obtained by using a questionnaire with 188 samples distributed to a group of accountants, internal auditors, and programmers, who constitute the study sample. The results unveiled that there is an impact of the application of expert systems and neural networks on the relevance of financial information in Jordanian commercial banks. It also revealed that there is a high level of relevance of financial information in Jordanian commercial banks. Accordingly, the study recommended the need for banks to keep pace with the progress and development taking place in connection to the process and environment of expertise systems by providing modern and developed devices to run various programs and expert systems. It also recommended that, Jordanian commercial banks need to rely more on advanced systems to operate neural network technology more efficiently.

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.

The paper considers the important problem of information protection in complex energy systems. The expert assessment of information protection in complex energy systems method has been developed. Based on the conducted research and data processing, a method of forming the analytical basis for decision-making aimed at ensuring the competitiveness of complex information protection systems has been developed.

This article proposes a method for determining the intruder type in the systems of physical protection of objects. An intruder trying to enter the territory, buildings or premises of the facility has to overcome typical engineering reinforcement elements of building structures. Elements of building structures are equipped with addressable alarm sensors. The intruder type is proposed to be determined according to its equipment by comparing the time of actually overcoming the building structure elements with the expert estimates. The time to overcome the elements of building structures is estimated by the time between successive responses of the security alarm address sensors. The intruder's awareness of the protection object is proposed to be assessed by tracking the route of its movement on the object using address sensors. Determining the intruder type according to the data of the security alarm systems can be used for the in-process tactics control of the security group actions.

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.

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.

The smooth operation of metering equipment is inseparable from the monitoring and analysis of equipment alarm events by automated metering systems. With the generation of big data in power metering and the increasing demand for information security of metering systems in the power industry, how to use big data and protect data security at the same time has become a hot research field. In this paper, we propose a hybrid expert model based on federated learning to deal with the problem of alarm information analysis and identification. The hybrid expert system can divide the metering warning problem into multiple sub-problems for processing, which greatly improves the recognition and prediction accuracy. The experimental results show that our model has high accuracy in judging and identifying equipment faults.

While the existence of many security elements in software can be measured (e.g., vulnerabilities, security controls, or privacy controls), it is challenging to measure their relative security impact. In the physical world we can often measure the impact of individual elements to a system. However, in cyber security we often lack ground truth (i.e., the ability to directly measure significance). In this work we propose to solve this by leveraging human expert opinion to provide ground truth. Experts are iteratively asked to compare pairs of security elements to determine their relative significance. On the back end our knowledge encoding tool performs a form of binary insertion sort on a set of security elements using each expert as an oracle for the element comparisons. The tool not only sorts the elements (note that equality may be permitted), but it also records the strength or degree of each relationship. The output is a directed acyclic ‘constraint’ graph that provides a total ordering among the sets of equivalent elements. Multiple constraint graphs are then unified together to form a single graph that is used to generate a scoring or prioritization system.For our empirical study, we apply this domain-agnostic measurement approach to generate scoring/prioritization systems in the areas of vulnerability scoring, privacy control prioritization, and cyber security control evaluation.

Cloud computing is becoming a demanding technology due to its flexibility, sensibility and remote accessibility. Apart from these applications of cloud computing, privacy and security are two terms that pose a circumstantial discussion. Various authors have argued on this topic that cloud computing is more secure than other data sharing and storing methods. The conventional data storing system is a computer system or smartphone storage. The argument debate also states that cloud computing is vulnerable to enormous types of attacks which make it a more concerning technology. This current study has also tried to draw the circumstantial and controversial debate on the security and privacy system of cloud computing. Primary research has been conducted with 65 cloud computing experts to understand whether a cloud computing security technique is highly secure or not. An online survey has been conducted with them where they provided their opinions based on the security and privacy system of cloud computing. Findings showed that no particular technology is available which can provide maximum security. Although the respondents agreed that blockchain is a more secure cloud computing technology; however, the blockchain also has certain threats which need to be addressed. The study has found essential encryption systems that can be integrated to strengthen security; however, continuous improvement is required.

Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.

Developers of mobile applications rely on the trust of their customers. On the one hand the requirement exists to create feature-rich and secure apps, which adhere to privacy standards to not deliberately disclose user information. On the other hand the development process must be streamlined to reduce costs. Here third-party libraries come into play. Inclusion of many, possibly nested libraries pose security risks, app-creators are often not aware of. This paper presents a way to combine free open-source tools to support developers in checking their application that it does not induce security issues by using third-party libraries. The tools FlowDroid, Frida, and mitm-proxy are used in combination in a simple and viable way to perform checks to identify privacy leaks of third-party apps. Our proposed setup and configuration empowers average app developers to preserve user privacy without being dedicated security experts and without expensive external advice.

Online template attacks (OTA), high-efficiency side-channel attacks, are initially presented to attack the elliptic curve scalar. The modular exponentiation is similarly vulnerable to OTA. The correlation between modular multiplication's intermediate products is a crucial leakage of the modular exponent. This paper proposed a practical OTA countermeasure based on randomized domain Montgomery multiplication, which combines blinding and shuffling methods to eliminate the correlation between modular multiplication's inner products without additional computation requirements. The proposed OTA countermeasure is implemented on the Sakura-G board with a suppose that the target board and template board are identical. The experiment results show that the proposed countermeasure is sufficient to protect the modular exponentiation from OTA.

The suggested IsoQuadratic Exponentiation Randomized isocryptosystem design is the unique approach for public key encipher algorithm using IsoPartial Discrete Logarithm Problem and preservation of the recommended IsoQuadratic Exponentiation Randomized isocryptosystem be established against hardness of IsoPartial Discrete Logarithm Problem. Therewith, we demonstrated the possibility of an additional secured algorithm. The offered unique IsoQuadratic Exponentiation Randomized isocryptosystem is suitable for low bandwidth transmission, low storage and low numeration in cyberspace.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.

The concept of a connected vehicle refers to the linking of vehicles to each other and to other things. Today, developments in the Internet of Things (IoT) and 5G have made a significant contribution to connected vehicle technology. In addition to many positive contributions, connected vehicle technology also brings with it many security-related problems. In this study, a digital signature algorithm based on elliptic curve cryptography is proposed to verify the message and identity sent to the vehicles. In the proposed model, with the anonymous identification given to the vehicle by the central unit, the vehicle is prevented from being detected by other vehicles and third parties. Thus, even if the personal data produced in the vehicles is shared, it cannot be found which vehicle it belongs to.

This paper presents a novel approach for a facial security system using elliptic curve cryptography. Face images extracted from input video are encrypted before sending to a remote server. The input face images are completely encrypted by mapping each pixel value of the detected face from the input video frame to a point on an elliptic curve. The original image can be recovered when needed using the elliptic curve cryptography decryption function. Specifically, we modify point multiplication designed for projective coordinates and apply the modified approach in affine coordinates to speed up scalar point multiplication operation. Image encryption and decryption operations are also facilitated using our existing scheme. Simulation results on Visual Studio demonstrate that the proposed systems help accelerate encryption and decryption operations while maintaining information confidentiality.

Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

The design of safety-critical embedded systems is a complex process that involves the reuse of proven solutions to fulfill a set of requirements. While safety is considered as the major requirement to be satisfied in safety-critical embedded systems, the security attacks can affect the security as well as the safety of these systems. Therefore, ensuring the security of the safety-critical embedded systems is as important as ensuring the safety requirements. The concept of design patterns, which provides common solutions to widely recurring design problems, have been extensively engaged in the design of the hardware and software in many fields, including embedded systems. However, there is an inadequacy of experience with security patterns in the field of safety-critical embedded systems. To address this problem, this paper proposes an approach to integrate security patterns with safety patterns in the design of safety-critical embedded systems. Moreover, it presents a customized representation for security patterns to be more relevant to the common safety patterns in the context of safety-critical embedded systems.

Embedded systems involve an integration of a large number of intellectual property (IP) blocks to shorten chip's time to market, in which, many IPs are acquired from the untrusted third-party suppliers. However, existing IP trust verification techniques cannot provide an adequate security assurance that no hardware Trojan was implanted inside the untrusted IPs. Hardware Trojans in untrusted IPs may cause processor program execution failures by tampering instruction code and return address. Therefore, this paper presents a secure RISC-V embedded system by integrating a Security Monitoring Unit (SMU), in which, instruction integrity monitoring by the fine-grained program basic blocks and function return address monitoring by the shadow stack are implemented, respectively. The hardware-assisted SMU is tested and validated that while CPU executes a CoreMark program, the SMU does not incur significant performance overhead on providing instruction security monitoring. And the proposed RISC-V embedded system satisfies good balance between performance overhead and resource consumption.

With the development of embedded systems towards networking and intelligence, the security threats they face are becoming more difficult to prevent. Existing protection methods make it difficult to monitor jump instructions and their target addresses for tampering by attackers at the low hardware implementation overhead and performance overhead. In this paper, a hardware-assisted security monitoring module is designed to monitor the integrity of jump instructions and jump addresses when executing programs. The proposed method has been implemented on the Xilinx Kintex-7 FPGA platform. Experiments show that this method is able to effectively monitor tampering attacks on jump instructions as well as target addresses while the embedded system is executing programs.

Archival services are one of the main functions of an information security management system for archival management, and the conversion and updating of archival intelligence services is an important means to meet the increasing diversity and wisdom of the age of intelligence. The purpose of this paper is to study an information security management system for archival management based on embedded artificial intelligence. The implementation of an embedded control management system for intelligent filing cabinets is studied. Based on a configurable embedded system security model, the access control process and the functional modules of the system based on a secure call cache are analysed. Software for wireless RF communication was designed, and two remote control options were designed using CAN technology and wireless RF technology. Tests have shown that the system is easy to use, feature-rich and reliable, and can meet the needs of different users for regular control of file room management.

Cloud computing is a fast growing field that provides the user with resources like software, infrastructure and virtual hardware processing power. The steady rise of cloud computing in recent times allowed large companies and even individual users to move towards working with cloud storage systems. However, the risks of leakage of uploaded data in the cloud storage and the questions about the privacy of such systems are becoming a huge problem. Security incidents occur frequently everywhere around the world. Sometimes, data leak may occur at the server side by hackers for their own profit. Data being shared must be encrypted before outsourcing it to the cloud storage. Existing encryption/decryption systems utilize large computational power and have troubles managing the files. This paper introduces a file system that is a more efficient, virtual, with encryption/decryption scheme using parallel encryption. To make encryption and decryption of files easier, Parallel encryption is used in place of serial encryption which is integrated with Identity-Based Encryption in the file system. The proposed file system aims to secure files, reduce the chances of file stored in cloud storage getting leaked thus providing better security. The proposed file system, OutFS+, is more robust and secure than its predecessor, OutFS. Cloud outsourcing takes place faster and the files can be downloaded to the OutFS+ instance on the other side. Moreover, OutFS+ is secure since it is a virtual layer on the operating system and can be unmounted whenever the user wants to.

Encryption defined as change information process (which called plaintext) into an unreadable secret format (which called ciphertext). This ciphertext could not be easily understood by somebody except authorized parson. Decryption is the process to converting ciphertext back into plaintext. Deoxyribonucleic Acid (DNA) based information ciphering techniques recently used in large number of encryption algorithms. DNA used as data carrier and the modern biological technology is used as implementation tool. New encryption algorithm based on DNA is proposed in this paper. The suggested approach consists of three steps (conventional, stream cipher and DNA) to get high security levels. The character was replaced by shifting depend character location in conventional step, convert to ASCII and AddRoundKey was used in stream cipher step. The result from second step converted to DNA then applying AddRoundKey with DNA key. The evaluation performance results proved that the proposed algorithm cipher the important data with high security levels.

Accurately constructing dynamic network topology is one of the core tasks to provide on-demand security services to the ubiquitous network. Existing schemes cannot accurately construct dynamic network topologies in time. In this paper, we propose a novel scheme to construct the ubiquitous network topology. Firstly, ubiquitous network nodes are divided into three categories: terminal node, sink node, and control node. On this basis, we propose two operation primitives (i.e., addition and subtraction) and three atomic operations (i.e., intersection, union, and fusion), and design a series of algorithms to describe the network change and construct the network topology. We further use our scheme to depict the specific time-varying network topologies, including Satellite Internet and Internet of things. It demonstrates that their communication and security protection modes can be efficiently and accurately constructed on our scheme. The simulation and theoretical analysis also prove that the efficiency of our scheme, and effectively support the orchestration of protection capabilities.