Mesopotamian Journal of CyberSecurity

Cybersecurity is always evolving, and the year 2024 will present new challenges but also opportunities. As such, this editorial paper aims to discuss the major phenomena in that field, which are a key element of modern cybersecurity concerns. This part of such vital complexity in these matters deserves further consideration, besides hoping that the comprehensive presentation will inspire other scholars and professionals to do their best to move forward with research. The future of cybersecurity is being shaped by trends such as the complex role of AI in cyber warfare and the emphasis on human-centric security measures.

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimes
has grown significantly. However, the concept of systematic pre-preparation for potential forensic
examinations during the software design phase, known as forensic readiness, has only recently gained
attention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous and
precise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence
(AI) technologies. This research amalgamates diverse datasets encompassing crime history, various
socio-economic indicators, and geographical locations to attain a comprehensive understanding of how
crimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizing
subtle periodic patterns and uncovering relationships among the collected datasets. Through this
comprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations in
frequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates the
efficacy of the AI model in generating productive insights and providing the most accurate predictions
of future criminal trends. These predictive insights are poised to revolutionize the strategies of law
enforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethical
considerations, this research ensures the continued feasibility of AI use while safeguarding individuals'
constitutional rights, including privacy. The anticipated outcomes of this research are anticipated to
furnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in the
identification of effective crime prevention strategies. By harnessing the potential of AI, this research
contributes to the promotion of proactive strategies and data-driven models in crime analysis and
prediction, offering a promising avenue for enhancing public security in Los Angeles and other
metropolitan areas.

The tiger beetle is a fierce and cunning predator insect that uses deception to hunt its prey. The tiger beetle traps and hunts them by digging holes along the path of other insects. This study has used the tiger beetle's hunting strategy to create the tiger beetle optimization (TBO) algorithm. In this algorithm, each solution represents the position of a tiger beetle, with the optimal position being the prey's location. Using this method, the tiger beetles gradually converge to the optimal solution, creating holes around them and searching for them. We evaluate the TBO algorithm's search capability using a series of well-known mathematical test functions. Moreover, Among the sophisticated forms of malware are polymorphic viruses, which are adept at changing their behaviour while maintaining the same essential functions. Thus, a machine learning-based malware analysis system utilizing the power of the proposed TBO is introduced in this article. Compared to other optimization methods, the proposed algorithm has shown less error in finding the optimal solution when implemented and evaluated on different functions. The tiger beetle optimization algorithm has proven helpful in various applications, including image clustering and reservoir well placement, where it can identify damaged areas or tissues with greater accuracy. When diagnosing lung cancer, the proposed method has shown a sensitivity, validity, and accuracy of 88.63\%, 87.58\%, and 89.86\%, respectively, using EBT, WKNN, ESKNM, and QSVM methods.

The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks.