Biblio

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.

The security of embedded systems is particularly crucial given the prevalence of embedded devices in daily life, business, and national defense. Firmware for embedded systems poses a serious threat to the safety of society, business, and the nation because of its robust concealment, difficulty in detection, and extended maintenance cycle. This technology is now an essential part of the contemporary experience, be it in the smart office, smart restaurant, smart home, or even the smart traffic system. Despite the fact that these systems are often fairly effective, the rapid expansion of embedded systems in smart cities have led to inconsistencies and misalignments between secured and unsecured systems, necessitating the development of secure, hacker-proof embedded systems. To solve this issue, we created a sizable, original, and objective dataset that is based on the latest Linux vulnerabilities for identifying the embedded system vulnerabilities and we modified a cutting-edge machine learning model for the Linux Kernel. The paper provides an updated EVDD and analysis of an extensive dataset for embedded system based vulnerability detection and also an updated state of the art deep learning model for embedded system vulnerability detection. We kept our dataset available for all researchers for future experiments and implementation.

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.

With the development of technology, the invention of computers, the use of cyberspace created by information communication systems and networks, increasing the effectiveness of knowledge in all aspects and the gains it provides have increased further the importance of cyber security day by day. In parallel with the developments in cyber space, the need for cyber defense has emerged with active and passive defense approaches for cyber security against internal and external cyber-attacks of increasing type, severity and complexity. In this framework, proactive cyber defense and deterrence strategies have started to be implemented with new techniques and methods.

The Internet has evolved to the point that gigabytes and even terabytes of data are generated and processed on a daily basis. Such a stream of data is characterised by high volume, velocity and variety and is referred to as Big Data. Traditional data processing tools can no longer be used to process big data, because they were not designed to handle such a massive amount of data. This problem concerns also cyber security, where tools like intrusion detection systems employ classification algorithms to analyse the network traffic. Achieving a high accuracy attack detection becomes harder when the amount of data increases and the algorithms must be efficient enough to keep up with the throughput of a huge data stream. Due to the challenges posed by a big data environment, some monitoring systems have already shifted from deep packet inspection to flow-level inspection. The goal of this paper is to evaluate the applicability of an existing intrusion detection technique that performs deep packet inspection in a big data setting. We have conducted several experiments with Apache Spark to assess the performance of the technique when classifying anomalous packets, showing that it benefits from the use of Spark.

The issues of development and legal regulation of cybersecurity in Ukraine are considered. The expediency of further improvement of the regulatory framework, its implementation and development of cybersecurity systems is substantiated. Further development of the theoretical base of cyber defense using spline functions is proposed. The characteristics of network traffic are considered from the point of view of detecting DDoS cyber attacks (SYN-Flood, ICMP-Flood, UDP-Flood) and predicting DDoS cyber-attacks using spline functions. The spline extrapolation method makes it possible to predict DDoS cyber attacks with great accuracy.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

In the 21st century, world-leading industries are under the accelerated development of digital transformation. Along with information and data resources becoming more transparent on the Internet, many new network technologies were introduced, but cyber-attack also became a severe problem in cyberspace. Over time, industrial control networks are also forced to join the nodes of the Internet. Therefore, cybersecurity is much more complicated than before, and suffering risk of browsing unknown websites also increases. To practice defenses against cyber-attack effectively, Cyber Range is the best platform to emulate all cyber-attacks and defenses. This article will use VMware virtual machine emulation technology, research cyber range systems under industrial control network architecture, and design and implement an industrial control cyber range system. Using the industrial cyber range to perform vulnerability analyses and exploits on web servers, web applications, and operating systems. The result demonstrates the consequences of the vulnerability attack and raises awareness of cyber security among government, enterprises, education, and other related fields, improving the practical ability to defend against cybersecurity threats.

There is a stark contrast between the state of cyber security of national infrastructure in Ireland and the efforts underway to support cyber security technologists to work in the country. Notable attacks have recently occurred against the national health service, universities, and various other state bodies, prompting an interest in changing the current situation. This paper presents an overview of the security projects, commercial establishments, and policy in Ireland.

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features. When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available.

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

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.

Cyber security is everybody's responsibility. It is the capability of the person to protect or secure the use of cyberspace from cyber-attacks. Cyber security awareness is the combination of both knowing and doing to safeguard one's personal information or assets. Online threats continue to rise in the Philippines which is the focus of this study, to identify the level of cyber security awareness among the students and teachers of Occidental Mindoro State College (OMSC) Philippines. Results shows that the level of cyber security awareness in terms of Knowledge, majority of the students and teachers got the passing score and above however there are almost fifty percent got below the passing score. In terms of Practices, both the teachers and the students need to strengthen the awareness of system and browser updates to boost the security level of the devices used. More than half of the IT students are aware of the basic cyber security protocol but there is a big percentage in the Non-IT students which is to be considered. Majority of the teachers are aware of the basic cyber security protocols however the remaining number must be looked into. There is a need to intensity the awareness of the students in the proper etiquette in using the social media. Boost the basic cyber security awareness training to all students and teachers to avoid cybercrime victims.

Still in many countries COVID19 virus is changing its structure and creating damages in terms of economy and education. In India during the period of January 2022 third wave is on its high peak. Many colleges and schools are still forced to teach online. This paper describes how cyber security actionable or practical fundamental were taught by school or college teachers. Various cyber security tools are used to explain the actionable insight of the subject. Main Topics or concepts covered are MITM (Man In the Middle Attack) using ethercap tool in Kali Linux, spoofing methods like ARP (Address Resolution Protocol) spoofing and DNS (Domain Name System) spoofing, network intrusion detection using snort , finding information about packets using wireshark tool and other tools like nmap and netcat for finding the vulnerability. Even brief details were given about how to crack password using wireshark.

For modern Automatic Test Equipment (ATE), one of the most daunting tasks conducting Information Assurance (IA). In addition, there is a desire to Network ATE to allow for information sharing and deployment of software. This is complicated by the fact that typically ATE are “unmanaged” systems in that most are configured, deployed, and then mostly left alone. This results in systems that are not patched with the latest Operating System updates and in fact may be running on legacy Operating Systems which are no longer supported (like Windows XP or Windows 7 for instance). A lot of this has to do with the cost of keeping a system updated on a continuous basis and regression testing the Test Program Sets (TPS) that run on them. Given that an Automated Test System can have thousands of Test Programs running on it, the cost and time involved in doing complete regression testing on all the Test Programs can be extremely expensive. In addition to the Test Programs themselves some Test Programs rely on third party Software and / or custom developed software that is required for the Test Programs to run. Add to this the requirement to perform software steering through all the Test Program paths, the length of time required to validate a Test Program could be measured in months in some cases. If system updates are performed once a month like some Operating System updates this could consume all the available time of the Test Station or require a fleet of Test Stations to be dedicated just to do the required regression testing. On the other side of the coin, a Test System running an old unpatched Operating System is a prime target for any manner of virus or other IA issues. This paper will discuss some of the pro's and con's of a managed Test System and how it might be accomplished.

The evolving and new age cybersecurity threats has set the information security industry on high alert. This modern age cyberattacks includes malware, phishing, artificial intelligence, machine learning and cryptocurrency. Our research highlights the importance and role of Software Quality Assurance for increasing the security standards that will not just protect the system but will handle the cyber-attacks better. With the series of cyber-attacks, we have concluded through our research that implementing code review and penetration testing will protect our data's integrity, availability, and confidentiality. We gathered user requirements of an application, gained a proper understanding of the functional as well as non-functional requirements. We implemented conventional software quality assurance techniques successfully but found that the application software was still vulnerable to potential issues. We proposed two additional stages in software quality assurance process to cater with this problem. After implementing this framework, we saw that maximum number of potential threats were already fixed before the first release of the software.

To train new staff to be efficient and ready for the tasks assigned is vital. They must be equipped with knowledge and skills so that they can carry out their responsibility to ensure smooth daily working activities. As transitioning to IPv6 has taken place for more than a decade, it is understood that having a dual-stack network is common in any organization or enterprise. However, many Internet users may not realize the importance of IPv6 security due to a lack of awareness and knowledge of cyber and computer security. Therefore, this paper presents an approach to educating people by introducing a security mechanisms model that can be applied in handling security challenges via network sandboxing by setting up an isolated dual stack network testbed using GNS3 to perform network security analysis. The finding shows that applying security mechanisms such as access control lists (ACLs) and host-based firewalls can help counter the attacks. This proves that knowledge and skills to handle dual-stack security are crucial. In future, more kinds of attacks should be tested and also more types of security mechanisms can be applied on a dual-stack network to provide more information and to provide network engineers insights on how they can benefit from network sandboxing to sharpen their knowledge and skills.

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.

Phishing has become a prominent method of data theft among hackers, and it continues to develop. In recent years, many strategies have been developed to identify phishing website attempts using machine learning particularly. However, the algorithms and classification criteria that have been used are highly different from the real issues and need to be compared. This paper provides a detailed comparison and evaluation of the performance of several machine learning algorithms across multiple datasets. Two phishing website datasets were used for the experiments: the Phishing Websites Dataset from UCI (2016) and the Phishing Websites Dataset from Mendeley (2018). Because these datasets include different types of class labels, the comparison algorithms can be applied in a variety of situations. The tests showed that Random Forest was better than other classification methods, with an accuracy of 88.92% for the UCI dataset and 97.50% for the Mendeley dataset.

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.

The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios.

Smart grid (SG) is considered the next generation of the traditional power grid. It is mainly divided into three main infrastructures: power system, information and communication infrastructures. Cybersecurity is imperative for information infrastructure and the secure, reliable, and efficient operation of the smart grid. Cybersecurity or a lack of proper implementation thereof poses a considerable challenge to the deployment of SG. Therefore, in this paper, A comprehensive survey of cyber security is presented in the smart grid context. Cybersecurity-related information infrastructure is clarified. The impact of adopting cybersecurity on control and management systems has been discussed. Also, the paper highlights the cybersecurity issues and challenges associated with the control decisions in the smart grid.