Biblio

Artificial intelligence is a subfield of computer science that refers to the intelligence displayed by machines or software. The research has influenced the rapid development of smart devices that have a significant impact on our daily lives. Science, engineering, business, and medicine have all improved their prediction powers in order to make our lives easier in our daily tasks. The quality and efficiency of regions that use artificial intelligence has improved, as shown in this study. It successfully handles data organisation and environment difficulties, allowing for the development of a more solid and rigorous model. The pace of life is quickening in the digital age, and the PC Internet falls well short of meeting people’s needs. Users want to be able to get convenient network information services at any time and from any location

The new paradigm of industrial development, called Industry 4.0, faces the problems of Cybersecurity, and as it has already manifested itself in Information Systems, focuses on the use of Artificial Intelligence tools. The authors of this article build on their experience with the use of the above mentioned tools to increase the resilience of Information Systems against Cyber threats, approached to the choice of an effective structure of Cyber-protection of Industrial Systems, primarily analyzing the objective differences between them and Information Systems. A number of analyzes show increased resilience of the decentralized architecture in the management of large-scale industrial processes to the centralized management architecture. These considerations provide sufficient grounds for the team of the project to give preference to the decentralized structure with flock behavior for further research and experiments. The challenges are to determine the indicators which serve to assess and compare the impacts on the controlled elements.

Data Analytics is at the core of almost all modern ap-plications ranging from science and finance to healthcare and web applications. The evolution of data analytics over the last decade has been dramatic - new methods, new tools and new platforms - with no slowdown in sight. This rapid evolution has pushed the boundaries of data analytics along several axis including scalability especially with the rise of distributed infrastructures and the Big Data era, and interoperability with diverse data management systems such as relational databases, Hadoop and Spark. However, many analytic application developers struggle with the challenge of production deployment. Recent experience suggests that it is difficult to deliver modern data analytics with the level of reliability, security and manageability that has been a feature of traditional SQL DBMSs. In this tutorial, we discuss the advances and innovations introduced at both the infrastructure and algorithmic levels, directed at making analytic workloads scale, while paying close attention to the kind of quality of service guarantees different technology provide. We start with an overview of the classical centralized analytical techniques, describing the shift towards distributed analytics over non-SQL infrastructures. We contrast such approaches with systems that integrate analytic functionality inside, above or adjacent to SQL engines. We also explore how Cloud platforms' virtualization capabilities make it easier - and cheaper - for end users to apply these new analytic techniques to their data. Finally, we conclude with the learned lessons and a vision for the near future.

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.

Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

In recent years, new types of cyber attacks called targeted attacks have been observed. It targets specific organizations or individuals, while usual large-scale attacks do not focus on specific targets. Organizations have published many Word or PDF files on their websites. These files may provide the starting point for targeted attacks if they include hidden data unintentionally generated in the authoring process. Adhatarao and Lauradoux analyzed hidden data found in the PDF files published by security agencies in many countries and showed that many PDF files potentially leak information like author names, details on the information system and computer architecture. In this study, we analyze hidden data of PDF files published on the website of police agencies in Japan and compare the results with Adhatarao and Lauradoux's. We gathered 110989 PDF files. 56% of gathered PDF files contain personal names, organization names, usernames, or numbers that seem to be IDs within the organizations. 96% of PDF files contain software names.

In this work, we discuss data breaches based on the “2012 SocialArks data breach” case study. Data leakage refers to the security violations of unauthorized individuals copying, transmitting, viewing, stealing, or using sensitive, protected, or confidential data. Data leakage is becoming more and more serious, for those traditional information security protection methods like anti-virus software, intrusion detection, and firewalls have been becoming more and more challenging to deal with independently. Nevertheless, fortunately, new IT technologies are rapidly changing and challenging traditional security laws and provide new opportunities to develop the information security market. The SocialArks data breach was caused by a misconfiguration of ElasticSearch Database owned by SocialArks, owned by “Tencent.” The attack methodology is classic, and five common Elasticsearch mistakes discussed the possibilities of those leakages. The defense solution focuses on how to optimize the Elasticsearch server. Furthermore, the ElasticSearch database’s open-source identity also causes many ethical problems, which means that anyone can download and install it for free, and they can install it almost anywhere. Some companies download it and install it on their internal servers, while others download and install it in the cloud (on any provider they want). There are also cloud service companies that provide hosted versions of Elasticsearch, which means they host and manage Elasticsearch clusters for their customers, such as Company Tencent.

In control systems, the operation of the system after an incident occurs is important. This paper proposes to design a whitelist model that can detect anomalies and identify locations of anomalous actuators using finite automata during multiple actuators attack. By applying this model and comparing the whitelist model with the operation data, the monitoring system detects anomalies and identifies anomaly locations of actuator that deviate from normal operation. We propose to construct a whitelist model focusing on the order of the control system operation using binary search trees, which can grasp the state of the system when anomalies occur. We also apply combinatorial compression based on BDD (Binary Decision Diagram) to the model to speed up querying and identification of abnormalities. Based on the model designed in this study, we aim to construct a secured control system that selects and executes an appropriate fallback operation based on the state of the system when anomaly is detected.

Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

In today’s world, digital data are enormous due to technologies that advance data collection, storage, and analyses. As more data are shared or publicly available, privacy is of great concern. Having privacy means having control over your data. The first step towards privacy protection is to understand various aspects of privacy and have the ability to quantify them. Much work in structured data, however, has focused on approaches to transforming the original data into a more anonymous form (via generalization and suppression) while preserving the data integrity. Such anonymization techniques count data instances of each set of distinct attribute values of interest to signify the required anonymity to protect an individual’s identity or confidential data. While this serves the purpose, our research takes an alternative approach to provide quick privacy measures by way of anonymity especially when dealing with large-scale data. This paper presents a study of anonymity measures based on their relevant properties that impact privacy. Specifically, we identify three properties: uniformity, variety, and diversity, and formulate their measures. The paper provides illustrated examples to evaluate their validity and discusses the use of multi-aspects of anonymity and privacy measures.

As one of the effective methods to enhance traffic safety and improve traffic efficiency, the Internet of vehicles has attracted wide attention from all walks of life. V2X secure communication, as one of the research hotspots of the Internet of vehicles, also has many security and privacy problems. Attackers can use these vulnerabilities to obtain vehicle identity information and location information, and can also attack vehicles through camouflage.Therefore, the identity authentication process in vehicle network communication must be effectively protected. The anonymous identity authentication scheme based on moving target defense proposed in this paper not only ensures the authenticity and integrity of information sources, but also avoids the disclosure of vehicle identity information.

Counterfeited products are a significant problem in both developed and developing countries and has become more critical as an aftermath of COVID-19, exclusively for drugs and medical equipment’s. In this paper, an innovative approach is proposed to resist counterfeiting which is based on the principles of Synthetic DNA. The proposed encryption approach has employed the distinctive features of synthetic DNA in amalgamation with DNA encryption to provide information security and functions as an anticounterfeiting method that ensures usability. The scheme’s security analysis and proof of concept are detailed. Scyther is used to carry out the formal analysis of the scheme, and all of the modeled assertions are verified without any attacks.

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

Network security isolation technology is an important means to protect the internal information security of enterprises. Generally, isolation is achieved through traditional network devices, such as firewalls and gatekeepers. However, the security rules are relatively rigid and cannot better meet the flexible and changeable business needs. Through the double sandbox structure created for each user, each user in the virtual machine is isolated from each other and security is ensured. By creating a virtual disk in a virtual machine as a user storage sandbox, and encrypting the read and write of the disk, the shortcomings of traditional network isolation methods are discussed, and the application of cloud desktop network isolation technology based on VMwarer technology in universities is expounded.

Virtual worlds was becoming increasingly popular in a variety of fields, including education, business, space exploration, and video games. Establishing the security of virtual worlds was becoming more critical as they become more widely used. Virtual users were identified using a behavioral biometric system. Improve the system's ability to identify objects by fusing scores from multiple sources. Identification was based on a review of user interactions in virtual environments and a comparison with previous recordings in the database. For behavioral biometric systems like the one described, it appears that score-level biometric fusion was a promising tool for improving system performance. As virtual worlds become more immersive, more people will want to participate in them, and more people will want to be able to interact with each other. Each region of the Meta-verse was given a glimpse of the current state of affairs and the trends to come. As hardware performance and institutional and public interest continue to improve, the Meta-verse's development is hampered by limitations like computational method limits and a lack of realized collaboration between virtual world stakeholders and developers alike. A major goal of the proposed research was to verify the accuracy of the biometric system to enhance the security in virtual world. In this study, the precision of the proposed work was compared to that of previous work.

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

A classification issue in machine learning is the issue of spotting Distributed Denial of Service (DDos) attacks. A Denial of Service (DoS) assault is essentially a deliberate attack launched from a single source with the implied intent of rendering the target's application unavailable. Attackers typically aims to consume all available network bandwidth in order to accomplish this, which inhibits authorized users from accessing system resources and denies them access. DDoS assaults, in contrast to DoS attacks, include several sources being used by the attacker to launch an attack. At the network, transportation, presentation, and application layers of a 7-layer OSI architecture, DDoS attacks are most frequently observed. With the help of the most well-known standard dataset and multiple regression analysis, we have created a machine learning model in this work that can predict DDoS and bot assaults based on traffic.

With the rapid development of the Internet of Things and the exploration of its application scenarios, embedded devices are deployed in various environments to collect information and data. In such environments, the security of embedded devices cannot be guaranteed and are vulnerable to various attacks, even device capture attacks. When embedded devices are attacked, the attacker can obtain the information transmitted by the channel during the encryption process and the internal operation of the encryption. In this paper, we analyze various existing white-box schemes and show whether they are suitable for application in IoT. We propose an application of WBEAs for distributed devices in IoT scenarios and conduct experiments on several devices in IoT scenarios.

Currently in El Salvador, efforts are being made to implement the digital signature and as part of this technology, a Public Key Infrastructure (PKI) is required, which must validate Certificate Authorities (CA). For a CA, it is necessary to implement the software that allows it to manage digital certificates and perform security procedures for the execution of cryptographic operations, such as encryption, digital signatures, and non-repudiation of electronic transactions. The present work makes a proposal for a digital certificate management system according to the Digital Signature Law of El Salvador and secure cryptography standards. Additionally, a security discussion is accomplished.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

Information Technology (IT) governance crosses the organization practices, culture, and policy that support IT management in controlling five key functions, which are strategic alignment, performance management, resource management, value delivery, and risk management. The line of sight is extended from the corporate strategy to the risk management, and risk controls are assessed against operational goals. Thus, the risk management model is concerned with ensuring that the corporate risks are sufficiently controlled and managed. Many organizations rely on IT services to facilitate and sustain their operations, which mandate the existence of a risk management model in their IT governance. This paper examines prior research based on IT governance by using a risk management framework. It also proposes a new method for calculating and classifying IT-related risks. Additionally, we assessed our technique with one of the critical IT services that proves the reliability and accuracy of the implemented model.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

Confidentiality and integrity security are the key challenges in future 5G networks. To encounter these challenges, various signature and key agreement protocols are being implemented in 5G systems to secure high-speed mobile-to-mobile communication. Many security ciphers such as SNOW 3G, Advanced Encryption Standard (AES), and ZUC are used for 5G security. Among these protocols, the AES algorithm has been shown to achieve higher hardware efficiency and throughput in the literature. In this paper, we implement the AES algorithm on Field Programmable Gate Array (FPGA) and real-time performance factors of the AES algorithm were exploited to best fit the needs and requirements of 5G. In addition, several modifications such as partial pipelining and deep pipelining (partial pipelining with sub-module pipelining) are implemented on Virtex 6 FPGA ML60S board to improve the throughput of the proposed design.