Security Professionals Research Articles

Machine Learning-Based Security Pattern Recognition Techniques for Code Developers

Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are complex and matching different and variate skills, most applications are launched intrinsically vulnerable. We have advanced our research for a security scanner able to use automated learning techniques based on machine learning algorithms to recognize patterns of security weaknesses in source code. To make the scanner independent on the programming language, the source code is converted to a vectorial representation using natural language processing methods, which are able to retain semantical traits of the original code and at the same time to reduce the dependency on the lexical structure of the program. The security flaws detection performance is in the ranges accepted by software security professionals (recall > 0.94) even when vulnerable samples are very low represented in the dataset (e.g., less than 4% vulnerable code for a specific CWE in the dataset). No significant change or adaptation is needed to change the source code language under scrutiny. We apply this approach on detecting Common Weaknesses Enumeration (CWE) vulnerabilities in datasets provided by NIST (Test suites–NIST Software Assurance Reference Dataset).

Praca zespołowa nauczycieli – od kultury indywidualizmu do kultury współdziałania

The text is devoted to the problems of the work of teaching teams in Polish schools. The paper presents selected results of pedeutological research on teachers’ opinions on team cooperation at school in terms of organisational practices undertaken by a group of teachers in order to carry out jointly agreed tasks, the effective performance of which is associated with the awareness of the goal, specific standards and synergistic use of the competence potential of team members. The research was carried out using a survey in a group of teachers and school principals from the Mazowieckie voivodship. The results of the research show that the respondents appreciate the value of teamwork, although some of them have a fairly critical attitude to the implementation of assumptions in school practice. They see both positive and negative aspects of teamwork. They notice the symptoms of the isolation of the teacher’s work and the dominance of ways of thinking and acting, characteristic of the culture of individualism and competition. Others, on the other hand, express themselves very positively about teamwork in their home schools or treat “working behind closed doors” as a natural phenomenon, a manifestation of teaching autonomy, conducive to gaining self-confidence and a sense of professional security. Teachers and school principals presented their own suggestions for improving the quality of teamwork. The research made it possible to identify the potential of cooperation in teaching teams, which also allowed to expand the area of knowledge in the field of pedeutology. The collected research material and conclusions resulting from its analysis may – in the longer term – serve to improve educational practice.

First-hand Accounts? Walter Duranty, William Henry Chamberlin and Eugene Lyons as Moscow Correspondents in the 1930s

ABSTRACT Previous studies on Moscow correspondents in the 1930s have prioritised the work of individual reporters over the infrastructure that actually conditioned how Western journalists could operate. This article addresses that imbalance, setting the careers of three correspondents – Walter Duranty, William Henry Chamberlin and Eugene Lyons – against the system of censorship and the control over information that prevailed in the Soviet Union. Using their published writing rather than newspaper reports, it considers how they made sense of their circumstances, and rationalised their subordinacy to Soviet oversight. It argues that for a range of reasons – ideological sympathy, financial improvement, and professional security – all three journalists themselves became parts in the machinery of censorship, learning to self-regulate and calibrate their reports to satisfy the requirements of the host regime. With the exception of Duranty, this process was subconscious as much as it was deliberate, demonstrating how the system eroded individual agency and journalistic integrity. In affirming systemic constraints on the activity of Moscow correspondents, the article also delineates Duranty as a special case of journalists who themselves became clients of the authoritarian states that they covered.

Strengthening cyber security with digital twins

Cyberthreats are becoming more numerous and complex. This poses a major challenge for security professionals trying to stay up to date with the latest developments. This is why many organisations are turning to graph-based digital twin technology to improve their cyber resilience management.

Detection of Prohibited Items Based upon X-ray Images and Improved YOLOv7

Safety inspection nowadays is an effective means to safeguard public security, which mainly relies on professional security personnel to carry out inspections. In order to detect automatically contraband in X-ray images, a new prohibited item detection method on the strength of the modified YOLOv7 algorithm is present. The spatial attention constructed by large kernel attention was introduced into the lower layer of the YOLOv7 backbone network to extract the remote dependence information and texture information of the lower layer feature map. The proposed method was tested on public X-ray data set for a safety inspection, and the result showed that the improved means can advance the detection accuracy of the model.

Identity and Access Management: High-level Conceptual Framework

Information security is shifting from a traditional perimeter-based approach to an identity-based approach where the organization’s boundaries are where their digital identities exist. The organization has multiple stakeholders having access to various organization resources. Systems and applications are part of organization resources that help them achieve their business goals. These systems and applications are internally or externally exposed to allow all stakeholders to have seamless access, thus making identity and access management a big challenge. Identity and Access Management (IAM) is a fundamental part of information security. It plays a critical role in keeping the organization’s information security posture resilient to cyber attacks. This paper will identify various components of an IAM solution that are essential and should be considered while implementing and assessing the IAM solution and provides a high-level IAM framework that will allow information security professionals to assess the IAM security posture of an organization.

Machine Learning and Data Mining Methods for Cyber Security: A Survey

Data mining and machine learning (ML) methods are used more than ever in cyber security. The use of machine learning (ML) is one of the potential solutions that may be successful against zero-day attacks, starting with categorising IP traffic and filtering harmful traffic for intrusion detection. In this field, certain published systematic reviews were taken into consideration. Recent systematic reviews may incorporate older and more recent works in the topic of investigation.. Both security professionals and hackers use data mining capabilities. Applications for data mining may be used to analyze programme activity, surfing patterns, and other factors to identify potential cyber-attacks in the future. The new study uses statistical traffic features, ML, and data mining approaches. This research performs a concentrated literature review on machine learning and its usage in cyber analytics for email filtering, traffic categorization, and intrusion detection. Each approach was identified, and a summary was provided based on the relevancy and quantity of citations. Some well-known datasets are also discussed since they are a crucial component of ML techniques. On when to utilize a certain algorithm is also offered some advice. Four ML algorithms have been evaluated on MODBUS data gathered from a gas pipeline. Using ML algorithms, other assaults have been categorized, and then the effectiveness of each approach has been evaluated. This study demonstrates the use of ML and data mining for threat research and detection, focusing on malware detection with high accuracy and short detection times.

Obstacles to harnessing analytic innovations in foreign policy analysis: a case study of crowdsourcing in the U.S. intelligence community

ABSTRACT We interviewed national security professionals to understand why the U.S. Intelligence Community has not systematically incorporated prediction markets or prediction polls into its intelligence reporting. This behavior is surprising since crowdsourcing platforms often generate more accurate predictions than traditional forms of intelligence analysis. Our interviews suggest that three principal barriers to adopting these platforms involved (i) bureaucratic politics, (ii) decision-makers lacking interest in probability estimates, and (iii) lack of knowledge about these platforms’ capabilities. Interviewees offered many actionable suggestions for addressing these challenges in future efforts to incorporate crowdsourcing platforms or other algorithmic tools into intelligence tradecraft.

MAD* Beyond Defence

Advancements in technology are regularly identified, assessed, and classed into emerging and/or potentially disruptive technologies, according to their ability to cause disruptions to defence systems, and in defence. Perhaps this is because defence capabilities centre on grand technology systems deployed at the level of nations. Hypersonic missiles are one example. The testing of a new hypersonic missile or a research program on types of hypersonic drones immediately sparks questions like: which other nations have such capability? or what types of technologies can be used to detect or counter these? In contrast, the ability to identify weak, faint factors that add up and lead to conflict are not brought together in a systematic manner. Nor is it common for there to be a cross-talk between a combination of methods used within military science and technology organizations over in to social sciences related to intelligence and/or conflict. This is a preventable strategic foresight issue relevant for enhancing, planning for, and investing in the security space. This paper describes the MAD (Methodology for Assessing Disruptions) tool, which is adaptable beyond the defence domain. MAD is a scenario-based two-part table-top exercise conducted to identify weak signals that have the potential to cause disruptions, which by consequence may coalesce into challenges for security. Exercising such methods is essential for security professionals to prepare and plan for future conflicts instead of constantly reacting to immediate acute problems.

User Perceptions of Information Security: Evidence from Takoradi Technical University

This study related to the importance of information and information technology in today's global business. The global nature of information systems also exposes them to threats which make them prone to security breaches. Information risks are several internal and external, making it almost impossible for only information security professionals to handle. This therefore reinforces the need to involve end-users in by educating them to be aware of threats, and their role in curbing those threats. Related information security literature was reviewed to establish the business problem theoretically. Using focus group discussions and open-ended interview guide, data was collected from non-security employees from Takoradi Polytechnic. The data provided understanding of the employees' present security needs, employees perception of information security, employees' personal security initiative, and level of information security awareness. The key findings in the study suggested that, currently some arrangements have been made to ensure information security; however, there is the need for more non-computer-based arrangements such as physical security, training and data backup systems. Further, because some respondents did not perceive the current security arrangement to be adequate, they took personal initiatives like using passwords, using formal communication channels for obtaining information which falls outside their domain or function, and seldom reporting any perceived security threats. These personal initiatives seemed to be the basis for the employees' self-rating of their level of information security awareness, not some training they had acquired.
 
 Received: 8 September 2022 / Accepted: 29 October 2022 / Published: 5 November 2022

Cleaning the NVD: Comprehensive Quality Assessment, Improvements, and Analyses

Vulnerability databases are vital sources of information on emergent software security concerns. Security professionals, from system administrators to developers to researchers, heavily depend on these databases to track vulnerabilities and analyze security trends. However, are these databases reliable and accurate? In this article, we explore this question with the National Vulnerability Database (NVD), the U.S. government's repository of vulnerability information that arguably serves as the industry standard. Through a systematic investigation, we uncover inconsistent or incomplete data in the NVD that can impact its practical uses, affecting information such as the vulnerability publication dates, applications affected by the vulnerability, their severity scores, and their high level type categorization. We explore the extent of these discrepancies and identify methods for their automated corrections. Finally, we demonstrate the impact that these data issues can pose by comparing analyses using the original and our rectified versions of the NVD. Ultimately, our investigation of the NVD not only produces an improved source of vulnerability information, but also provides important insights and guidance for the security community on the curation and use of such data sources.

Using Game-based Learning Methods to Demystify Cyber Security Concepts for Adult Learners

The digital society we live in today has compelled both businesses and individuals to become increasingly dependent on the internet and online services. With the increasing cyber security threats and attacks, there is a growing demand for cyber security professionals and a need for every individual to be cyber literate and cyber-aware. But cyber security is often regarded as extremely complex and quite technical, which acts as a roadblock in educating the adult population about cyber security. 'Play as a natural means for learning' is commonly exploited in early learning and primary education settings. But it is seldom used in secondary education and rarely considered in an adult learning context. This paper presents a game-based learning resource developed as part of the 'Gamified Intelligent Cyber Aptitude and Skills Training' (GICAST) course to introduce cyber security topics to adult learners. The course is targeted at adult learners who work in low-skilled and low-paid jobs and have no formal degree-level qualification. The aim was to demystify cyber security concepts and equip the low-confidence learners with the confidence to engage in learning and gain additional digital skills to enhance their career options. This paper provides an overview of the GICAST project, the game design principles and the game mechanic employed to achieve the intended pedagogic outcomes. The paper illustrates how the power of games helped demystify a complex area of cyber security among adult learners. This is demonstrated in the evaluation results and findings from the study on the effectiveness of game-based learning methods in enhancing learner engagement and motivation to learn among adult learners.

Adoption of Augmented Reality (AR) and Virtual Reality (VR) in healthcare systems

Virtual reality (VR) and Augmented reality (AR) are the cutting-edge technological innovations that is going to shape how members of the society live and interact in future. In recent years, such technologies have been successfully implemented in various sectors including, military, education, healthcare, gaming among others. In the same way, its explosion more so in healthcare sector has resulted to various research being done that have revealed potential benefits and challenges in its adoption. This paper aimed at researching and providing an understanding of the role of VR and AR in healthcare systems as well as investigating its applications, potential benefits and challenges. The article applied exploratory research design to review the future applications, benefits and provide solutions to the challenges of VR and AR in healthcare. The review revealed that despite the tremendous growth and potential of such technologies, challenges resulting from cost implication of the technologies, technical capabilities of devices, infrastructural issues have all impacted on adoption of VR and AR in healthcare sector. As a result of advancement in technology over years, most of the challenges have been addressed due to invention of computers with more processing power and screens with better resolution. However, the issues of data privacy and security of both healthcare professionals and patients need to be addressed. This can be achieved by stakeholders developing and implementing universally acceptable standards and procedures that will guide research, development, and implementation of such technologies. This calls for parties involved in the development and usage of this devices to be assured of data privacy and security in healthcare sector.

Driving best practice security in a hybrid working world

The UK is settling into its new hybrid working routine. This is introducing new cyber security challenges for organisations. To secure personal and sensitive data across this ever-growing, sprawling and increasingly insecure environment, organisations have stepped up their use of end-to-end encryption. But this has created visibility challenges for security and compliance teams. And to many IT security professionals, it feels like a Catch-22 as they try to secure the needs of this increasingly hybrid workforce.

Uncovering data relationships to tackle fraud

Graph technology has reached a point of maturity where it can run off-the-shelf algorithms over massive networks to discover previously undetectable patterns. These algorithms also permit security professionals to take the necessary actions to render systems more robust and secure. As a result, financial and government institutions are making increasing use of graph database technology to tackle fraud.

Regarding the Current National Security Challenges

Issues such as globalization, developments in communication and information technologies, and economic and social inequalities that are becoming more evident between countries, cause conflicts to become globalized beyond regional borders. Therefore, in the process of national security administration, it is needed to be referred to the process and direction of developments, rather than creating pre-absolute equations. In this context, the current security environment is characterized by an increase both in the number of military conflicts and other human-induced problems that threaten human life and co-existence. These challenges force national security professionals, whose primary mission is to establish security, to focus on security issues in a multidimensional way in order to fulfill their duties. Therefore, the quality of the adopted national security conception is critical to the establishment of today's security and peaceful environment. In this study, which is carried out on current national security challenges; it is aimed to present the current security environment and its challenges in detail. This study focuses on wider areas affecting the perception and execution of national security and so provides a comprehensive overview of information about the dimensions of the current security environment.

A Review on Cyber Security

Virtual simulation experiment teaching is an important content of higher education information construction and experimental teaching demonstration centre construction, and is the product of the deep integration of discipline and information technology. At present, traditional crimes in China are gradually shifting to the Internet, and cybercrimes are frequently and frequently occurring, seriously endangering national security, social order and people's interests. The forging of a team of high-quality professionals in network security and law enforcement is the inevitable path to effectively crack down on network crimes and realise the comprehensive governance of network crimes, and also provides a new examination question for the teaching of network security law enforcement. In view of the network security law enforcement routine practice teaching cannot cover all types of the experiment, especially some reach or irreversible operations (such as electronic data on-site inspection, etc.), and need high cost, large comprehensive experiments training (such as a variety of types involved network crimes probing experiment, etc.), virtual simulation experiment teaching has become an important method in the teaching of network security law enforcement Therefore, combining with the characteristics of network security law enforcement major, exploring the establishment of virtual simulation experimental teaching platform for network security law enforcement plays a crucial role in the teaching of network security law enforcement experiment and practical training

Facilitating Cyber Security Threat Modelling: A Social Capital Perspective

To identify and manage their cyber security risks, organisations need to form a thorough understanding of various factors that may expose them to these risks. While cyber security professionals and scholars have developed a plethora of practical methodologies and frameworks to support cyber security risk identification and mitigation, the theoretical foundations on what promotes effective knowledge creation when using these methodologies and frameworks are nascent. Yet, theories developed in the field of knowledge management and intellectual capital may provide valuable insight on how to enhance cyber security risk related knowledge creation in organisations. For example, social capital is considered as an important prerequisite for knowledge exchange and combination when creating new intellectual capital (Nahapiet & Ghoshal, 1998). However, more focused research is required to understand how social capital affects knowledge creation in the context of organisational cyber security risk related activities.
 
 Using qualitative data gathered from three cyber security threat modelling workshops, this paper examines how social capital enables conditions for exchanging and combining knowledge on cyber security threats. By comparing the empirical observations with Nahapiet and Ghoshal’s (1998) model, this study identifies practical approaches that are used by threat modelling workshop facilitators to create conditions for effective knowledge exchange and combination. This study provides both cyber security scholars and professionals with an example on how to use knowledge creation related academic theories to analyse and further enhance cyber security risk management approaches by creating a connection between Nahapiet and Ghoshal’s (1998) social capital model and cyber security threat modelling.

Enhancing academic integrity in a UAE safety, security defence emergency management academy – the Covid- 19 response and beyond

Globally, academic integrity and misconduct is a continuing conundrum for education institutions. Whilst the online (internet based and remote) delivery of education is not new, the onset of Covid-19 with accompanying health and safety limitations and the consequential rapid transition to emergency online delivery of education has, for many, exacerbated the need to focus on emerging potential for new forms of student academic misconduct i.e., e-dishonesty. This paper presents the strategies developed by a higher education institution specializing in university courses for safety and security professionals in the United Arab Emirates to address academic integrity during Covid-19 and beyond. The fundamental approach draws on lessons learnt from across the education community and focuses on engaging a comprehensive whole of Academy (faculty and student) commitment to a high standard of academic integrity. The research investigated the outcomes of the academic integrity interventions in a total student population of 631. The early trending results of the strategies are encouraging, whilst monitoring of the ever-changing academic assessment landscape is pivotal to ensure these early trends are validated and sustained. The strategies developed and deployed by the Academy are replicable and offer a contribution to the demanding and complex challenge of establishing academic integrity within educational institutions across the world.

Deep Learning (DL) Oriented Forensic Analysis

Cyber-attacks are now more prevalent than ever before in all aspects of our daily lives. As a result of this circumstance, both individuals and organizations are fighting cybercrime on a regular basis. Furthermore, today's hackers have advanced a step further and are capable of employing complex cyber-attack strategies, exacerbating the problem. Some of these approaches are minute and undetectable, and they frequently masquerade as genuine requests and directives. To combat this threat, cyber security professionals, as well as digital forensic investigators, are constantly compelled to filter through massive and complicated pools of data, also known as Big Data, in order to uncover Potential Digital Forensic Evidence. that can be used as evidence in court. Potential Digital Evidence can then be used to assist investigators in reaching certain conclusions and/or judgments. The fact that Big Data frequently comes from various sources and has diverse file formats makes cyber forensics even more difficult for investigators. When it comes to the processing of vast amounts of complicated data for forensic purposes, forensic investigators typically have less time and budget to fulfil the rising demands. This paper will be studying how to incorporate Deep Learning cognitive computing approaches into Cyber Forensics Keywords: Deep Learning, Forensic Analysis, Artificial Intelligence, Online Safety, Evidence BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Herbert Cyril Dodoo (2022): Deep Learning (DL) Oriented Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 320-328 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P51