Victorian Institute of Teaching

The intersection of Cybersecurity and AI has garnered increasing attention in recent years due to the growing importance of securing digital assets in an interconnected world. This bibliometric analysis aims to provide valuable insights into the research trends and developments within this interdisciplinary domain. Using data extracted from the Scopus database, a total of 501 papers were selected and analyzed to uncover key patterns and themes. The methodology involved conducting a comprehensive literature search using specific keywords related to Cybersecurity and AI applications. The initial search yielded 736 papers, which were subsequently filtered to include research articles, conference papers, editorial papers, and review papers, resulting in the final dataset of 501 papers. The analysis of publication trends revealed a remarkable surge in research output since 2015, indicating the escalating interest in this field. Collaboration patterns among researchers and institutions were analyzed through co-authorship networks, highlighting a well-connected research community that fosters knowledge exchange. Keyword analysis exposed common areas of application, such as network security, deep learning, and the Internet of Things, underscoring the importance of AI technologies in enhancing Cybersecurity measures. Furthermore, examination of the most cited documents showcased influential contributions that have shaped the trajectory of Cybersecurity and AI research. The study emphasizes the significance of Cybersecurity and AI applications research, considering the ever-increasing reliance on technology in various aspects of modern life. By integrating AI technologies, Cybersecurity measures can be fortified with automated threat detection, adaptive defense mechanisms, and proactive risk mitigation, thereby bolstering overall cybersecurity resilience. The findings of this bibliometric analysis have several implications for researchers and policymakers. Researchers can leverage the identified trends and gaps to explore new research directions and potential collaborations. Policymakers can utilize these insights to make informed decisions regarding resource allocation for research initiatives aimed at addressing emerging Cybersecurity challenges. This bibliometric analysis provides a comprehensive overview of the evolving landscape of Cybersecurity and AI applications research. It underscores the growing importance of this interdisciplinary field and its potential to reshape the future of cybersecurity. As technology continues to advance, the integration of AI in Cybersecurity will play a pivotal role in safeguarding digital assets and ensuring the secure functioning of critical systems in an increasingly interconnected world.

PurposeGiven how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.Design/methodology/approachThis research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.FindingsThe tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.Originality/valueCyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

Network-on-Chip (NoC) has been unfolded as a superior alternative for integrating a considerably greater extent of cores on a single chip. Recently, multi-core systems have become prevalent because of the increased processing demands for high-performance embedded applications. Application mapping techniques play a significant role in enhancing the extensive performance of such complex multicore platforms. Developing and implementing efficient application mapping techniques are required for system design to meet the demand of such complicated multi-core systems. The paper primarily focuses on dynamic application mapping techniques, classifying them into a number of subcategories. It highlights such approaches and techniques that aim to enhance the performance of the NoC-based systems by optimizing them in terms of communication cost, latency, energy consumption, power, execution, and computational time. Future challenges, trends, and simulation tools have also been spotlighted. Network-on-Chip, Application mapping, System-on-Chip, VOPD. I. INTRODUCTION D UE to the rise in complexity of embedded This is some system devices, system-on-chip (SoC) incorporating the numerous processing cores on a single chip to perform various functions is the primary paradigm of today's digital world. The SoC uses a shared medium bus to communicate intellectual property (IP) cores and is widely utilized in domain-specific devices [1], [2], such as aerospace, medical sciences, microprocessors-based technology, and wireless communications. Due to the higher density of components in SoC, the implementation of a shared-bus architecture is getting complicated. As the number of cores increases on the chip, the performance is not enhanced and scaled by the increased processing cores due to the limitations of the shared-bus architecture. Network-on-chip (NoC) has emerged as a feasible substitute to cater to the new inter-core communication demands of the growing number of 17 components on a chip and faster communication between 18 the cores [3]. NoC architecture is considered a part of or 19 a subset of SoC-based technology [4]. NoC uses IP cores 20 connected with the routers and inter-switch links [5], and 21 the communication between the cores is done by transferring 22 packets with these routers and links. Messages are divided 23 into smaller packets to be transmitted between various cores 24 that allow for the efficient use of network resources. NoC 25 employs a routing algorithm for the determination of the 26 path that each packet follows from the source to the destina-27 tion. Various switching techniques are developed to transfer 28 packets such as circuit and packet switching. A physical 29 path from source to destination is reserved before the data 30 transmission in circuit switching. While in packet switching, 31 each message is partitioned into fixed-length packets which 32

For the past few years, the market has changed a lot and it has become dynamic and demanding which has put the market into a competitive environment. The supply chain plays a crucial role to adapt the business to the dynamic environment as it is very reliant on collaboration integration as well as flexibility. The applications related to the supply chain have gotten the attention of many business owners and to improve the flow control of the supply chain many specialized applications are implemented. One of the most important new technological applications in the supply chain is blockchain technology which has garnered the attention of many business owners as it can be quickly adapted to dynamic market conditions and in the business environment. One upon reading this will get to know about the effect of blockchain technology utilization on this field. The results of the research paper recommend that companies invest in blockchain technology so that the supply chain becomes more transparent, flexible, and secure. There is no doubt in the fact that blockchain technology plays an important role in developing trust with the stakeholder of the supply chain. In the end, the research paper has also given some considerations on the implications that are positive as well as the potential of the blockchain in the field of collaboration and integration.