Biblio

Successful information and communication technology (ICT) may propel administrative procedures forward quickly. In order to achieve efficient usage of TCT in their businesses, ICT strategies and plans should be examined to ensure that they align with the organization's visions and missions. Efficient software and hardware work together to provide relevant data that aids in the improvement of how we do business, learn, communicate, entertain, and work. This exposes them to a risky environment that is prone to both internal and outside threats. The term “security” refers to a level of protection or resistance to damage. Security can also be thought of as a barrier between assets and threats. Important terms must be understood in order to have a comprehensive understanding of security. This research paper discusses key terms, concerns, and challenges related to information systems and security auditing. Exploratory research is utilised in this study to find an explanation for the observed occurrences, problems, or behaviour. The study's findings include a list of various security risks that must be seriously addressed in any Information System and Security Audit.

Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN's forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.

Scam websites or illegitimate internet portals are widely used to mislead users into fraud or malicious attacks, which may involve compromise of vital information. Scammers misuse the secrecy and anonymity of the internet of facade their true identity and purposes behind numerous disguises. These can include false security alerts, information betrayal, and other misleading presentations to give the impression of legality and lawfulness. The proposed research is a web-based application - Scam Website Analyser- which enables checking whether a website is a scammed one.. The main aim of the research is to improve security and prevent scams of public websites. It ensures maintaining the details of scam websites in a database and also requests the websites of other databases using external APIs. The basic idea behind the research is the concept of user -orienteers where the user is able to get information about scam websites and prevent themselves from using those sites in future.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

Nowadays the provision of online services by government or business organizations has become a standard and necessary operation. Transferring data including the confidential or sensitive information via Internet or insecure network and exchange of them is also increased day by day. As a result, confidential information leakage and cyber threats are also heightened. Confidential information trading became one of the most profitable businesses. Encrypting the data is a solution to secure the data from being exposed. In this paper, we would like to propose a solution for the secure transfer of data using symmetric encryption, asymmetric encryption technologies and Key Generation Server as a mixed hybrid solution. A Symmetric encryption, modified AES algorithm, is used to encrypt data. Digital certificate is used both for data encryption and digital signing to assure data integrity. Key generation server is used to generate the second secret key from the publicly recognized information of a person and this key is used as a second secret key in the modified AES. The proposed hybrid solution can be utilized in any applications that require high confidentiality, integrity of data and non-repudiation.

The lack of strong cyber-physical modeling capabilities presents many challenges across the design, development, verification, and maintenance phases of a system [7]. Novel techniques for modeling the cyber-grid components, along with analysis and verification techniques, are imperative to the deployment of a resilient and robust power grid. Several works address False Data Injection (FDI) attacks to the power grid. However, most of them suffer from the lack of a model to investigate the effects of attacks. This paper proposed a cyber-physical model using Architecture Analysis & Design Language (AADL) [15] and power system information models to address different attacks in power systems.

Social media are increasingly reflecting and influencing the behavior of human and financial market. Cognitive hacking leverages the influence of social media to spread deceptive information with an intent to gain abnormal profits illegally or to cause losses. Measuring the information content in financial social media can be useful for identifying these attacks. In this paper, we developed an approach to identifying social media features that correlate with abnormal returns of the stocks of companies vulnerable to be targets of cognitive hacking. To test the approach, we collected price data and 865,289 social media messages on four technology companies from July 2017 to June 2018, and extracted features that contributed to abnormal stock movements. Preliminary results show that terms that are simple, motivate actions, incite emotion, and uses exaggeration are ranked high in the features of messages associated with abnormal price movements. We also provide selected messages to illustrate the use of these features in potential cognitive hacking attacks.

Control systems for critical infrastructure are becoming increasingly interconnected while cyber threats against critical infrastructure are becoming more sophisticated and difficult to defend against. Historically, cyber security has emphasized building defenses to prevent loss of confidentiality, integrity, and availability in digital information and systems, but in recent years cyber attacks have demonstrated that no system is impenetrable and that control system operation may be detrimentally impacted. Cyber resilience has emerged as a complementary priority that seeks to ensure that digital systems can maintain essential performance levels, even while capabilities are degraded by a cyber attack. This paper examines how cyber security and cyber resilience may be measured and quantified in a control system environment. Load Frequency Control is used as an illustrative example to demonstrate how cyber attacks may be represented within mathematical models of control systems, to demonstrate how these events may be quantitatively measured in terms of cyber security or cyber resilience, and the differences and similarities between the two mindsets. These results demonstrate how various metrics are applied, the extent of their usability, and how it is important to analyze cyber-physical systems in a comprehensive manner that accounts for all the various parts of the system.

The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach detects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.

A understanding of the nature of targeted cyber-attack processes is needed to defend against this kind of cyber threats. Generally, the models describing processes of targeted cyber attacks are called in the literature as cyber kill chains or rarely cyber-attacks life cycles. Despite the fact that cyber-attacks have random nature, almost no stochastic models of cyber kill chains bases on the theory of stochastic processes have been proposed so far. This work, attempting to fill this deficiency, proposes to start using Markov processes for modeling some cyber-attack kill chains. In this paper two example theoretical models of cycles of returning cyber-attacks are proposed which have been generally named as the models of cyber kill chains with iterations. Presented models are based on homogeneous continuous time Markov chains.

Cyber threats have increased extensively during the last decade, especially in smart grids. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and Advanced Persistent Threat (APT) is almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques can mine data to detect different attack stages of APT. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits.

A prioritized cyber defense remediation plan is critical for effective risk management in cyber-physical systems (CPS). The increased integration of Information Technology (IT)/Operational Technology (OT) in CPS has to lead to the need to identify the critical assets which, when affected, will impact resilience and safety. In this work, we propose a methodology for prioritized cyber risk remediation plan that balances operational resilience and economic loss (safety impacts) in CPS. We present a platform for modeling and analysis of the effect of cyber threats and random system faults on the safety of CPS that could lead to catastrophic damages. We propose to develop a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in CPS. We develop an operational impact assessment to quantify the damages. Finally, we propose the development of a strategic response decision capability that proposes optimal mitigation actions and policies that balances the trade-off between operational resilience (Tactical Risk) and Strategic Risk.

The article considers some aspects of modeling information security circuits for information and communication systems used in Smart City. As a basic research paradigm, the postulates of game theory and mathematical dependencies based on Markov processes were used. Thus, it is possible to sufficiently substantively describe the procedure for selecting rational variants of cyber security systems used to protect information technologies in Smart City. At the same time, using the model proposed by us, we can calculate the probability of cyber threats for the Smart City systems, as well as the cybernetic risks of diverse threats. Further, on the basis of the described indicators, rational contour options are chosen to protect the information systems used in Smart City.

Rapid evolution of cyber threats and recent trends in the increasing number of cyber-attacks call for adopting robust and agile cybersecurity techniques. Cybersecurity information sharing is expected to play an effective role in detecting and defending against new attacks. However, reservations and or-ganizational policies centering the privacy of shared data have become major setbacks in large-scale collaboration in cyber defense. The situation is worsened by the fact that the benefits of cyber-information exchange are not realized unless many actors participate. In this paper, we argue that privacy preservation of shared threat data will motivate entities to share threat data. Accordingly, we propose a framework called CYBersecurity information EXchange with Privacy (CYBEX-P) to achieve this. CYBEX-P is a structured information sharing platform with integrating privacy-preserving mechanisms. We propose a complete system architecture for CYBEX-P that guarantees maximum security and privacy of data. CYBEX-P outlines the details of a cybersecurity information sharing platform. The adoption of blind processing, privacy preservation, and trusted computing paradigms make CYBEX-P a versatile and secure information exchange platform.

Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.

For complex technical objects the research of cybersecurity problems should take into account both physical and information properties of the object. The paper considers a hybrid model that unifies information and physical models and may be used as a tool for countering cyber threats and for cybersecurity risk assessment at the design and operational stage of an object's lifecycle.

Public sector and private organizations began using cybersecurity control in order to defend their assets against cybercriminals attack. Cybersecurity audits assist organizations to deal with cyber threats, cybercriminals, and cyber-attacks thatare growing in an aggressive cyber landscape. However, cyber-attacks and threats become more increase and complex in complicated cyber landscapes challenge auditors to perform an effective cybersecurity audit. This current situation puts in evidens ce the critical need for a new approach in the cybersecurity audit execution. This study reviews an alternative method in the execution of cybersecurity security checks. The analysis is on the character and behavioral of cyber-attacks and threats using feature extraction and selection method to get crucial elements from the common group of cyber-attacks and threats. Cyber-attacks and threats profile are systematic approaches driven by a clear understanding of the form of cyber-attacks and threats character and behavior patterns in cybersecurity requirements. As a result, this study proposes cyber-attacks and threats profiling for cybersecurity audit as a set of control elements that are harmonized with audit components that drive audits based on cyber threats.

Over the last years, the maritime sector has seen an important increase in digital systems on board. Whether used for platform management, navigation, logistics or office tasks, a modern ship can be seen as a fully featured, complex and moving information system. Meanwhile, cyber threats on the sector are real and, for instance, the year 2018 has seen a number of harmful public ransomware attacks impacting shore and ashore assets. Gaining cyber situation recognition, comprehension and projection through Maritime Cyber Situational Awareness is therefore a challenging but essential task for the sector. However, its elaboration has to face a number of issues, such as the collect and fusion of real-time data coming from the ships and an efficient visualization and situation sharing across maritime actors. In this paper, we describe our current work and results for maritime cyber situational awareness elaboration. Even if its development is still going on, the first operational feedback is very encouraging.

The advent of the cyber domain has introduced a new dimension into warfare and complicated existing strategic concepts, provoking divergent responses within different national contexts and strategic cultures. Although current theories regarding cyber deterrence remain relatively nascent, a comparison of U.S. and Chinese strategic thinking highlights notable asymmetries between their respective approaches. While U.S. debates on cyber deterrence have primarily focused on the deterrence of cyber threats, Chinese theorists have also emphasized the potential importance of cyber capabilities to enhance strategic deterrence. Whereas the U.S. government has maintained a consistent declaratory policy for response, Beijing has yet to progress toward transparency regarding its cyber strategy or capabilities. However, certain PLA strategists, informed by a conceptualization of deterrence as integrated with warfighting, have advocated for the actualization of deterrence through engaging in cyber attacks. Regardless of whether these major cyber powers' evolving strategic thinking on cyber deterrence will prove logically consistent or feasibly operational, their respective perspectives will certainly shape their attempts to achieve cyber deterrence. Ultimately, cyber deterrence may continue to be "what states make of it," given conditions of "cyber anarchy" and prevailing uncertainties regarding cyber conflict. Looking forward, future strategic stability in Sino-U.S. cyber interactions will require mitigation of the misperceptions and heightened risks of escalation that could be exacerbated by these divergent strategic approaches.

In this cyber era, the cyber threats have reached a new level of menace and maturity. One of the major threat in this cyber world nowadays is ransomware attack which had affected millions of computers. Ransomware locks the valuable data with often unbreakable encryption codes making it inaccessible for both organization and consumers, thus demanding heavy ransom to decrypt the data. In this paper, advanced and improved version of the Petya ransomware has been introduced which has a reduced anti-virus detection of 33% which actually was 71% with the original version. System behavior is also monitored during the attack and analysis of this behavior is performed and described. Along with the behavioral analysis two mitigation strategies have also been proposed to defend the systems from the ransomware attack. This multi-layered approach for the security of the system will minimize the rate of infection as cybercriminals continue to refine their tactics, making it difficult for the organization's complacent development.

The healthcare sector is exploring the incorporation of digital solutions in order to improve access, reduce costs, increase quality and enhance their capacity in reaching a higher number of citizens. However, this opens healthcare organisations' systems to external elements used within or beyond their premises, new risks and vulnerabilities in what regards cyber threats and incidents. We propose the creation of a Security Assessment as a Service (SAaaS) crosslayered system that is able to identify vulnerabilities and proactively assess and mitigate threats in an IT healthcare ecosystem exposed to external devices and interfaces, considering that most users are not experts (even technologically illiterate") in cyber security and, thus, unaware of security tactics or policies whatsoever. The SAaaS can be integrated in an IT healthcare environment allowing the monitoring of existing and new devices, the limitation of connectivity and privileges to new devices, assess a device's cybersecurity risk and - based on the device's behaviour - the assignment and revoking of privileges. The SAaaS brings a controlled cyber aware environment that assures security, confidentiality and trust, even in the presence of non-trusted devices and environments.

The success and widespread adoption of the Internet of Things (IoT) has increased many folds over the last few years. Industries, technologists and home users recognise the importance of IoT in their lives. Essentially, IoT has brought vast industrial revolution and has helped automate many processes within organisations and homes. However, the rapid growth of IoT is also a cause for significant concern. IoT is not only plagued with security, authentication and access control issues, it also doesn't work as well as it should with fourth industrial revolution, commonly known as Industry 4.0. The absence of effective regulation, standards and weak governance has led to a continual downward trend in the security of IoT networks and devices, as well as given rise to a broad range of privacy issues. This paper examines the IoT industry and discusses the urgent need for standardisation, the benefits of governance as well as the issues affecting the IoT sector due to the absence of regulation. Additionally, through this paper, we are introducing an IoT security framework (IoTSFW) for organisations to bridge the current lack of guidelines in the IoT industry. Implementation of the guidelines, defined in the proposed framework, will assist organisations in achieving security, privacy, sustainability and scalability within their IoT networks.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.

The technological development of automated vehicles opens novel cybersecurity threats and risks for road safety. Increased connectivity often results in increased risks of a cyber-security attacks, which is one of the biggest challenges for the automotive industry that undergoes a profound transformation. State of the art studies evaluated potential attacks and recommended possible measures, from technical and organizational perspective to face these challenges. In this position paper, we review these techniques and methods and show that some of the different solutions complement each other while others overlap or are even incompatible or contradictory. Based on this gap analysis, we advocate for the need of a comprehensive framework that integrates technical and organizational mitigation measures to enhance the cybersecurity of automotive vehicles.

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.