Spectrum Of Threats Research Articles

Priority threats to the economic security system of Ukraine in wartime conditions: conceptual views on their nature and typology

The presented scientific study is devoted to the issues of seeing priority threats to the system of economic security of Ukraine in wartime conditions. It is emphasized and proven that the existing spectrum of threats to the economic security component, in practice, has undergone significant changes in terms of complexity and realism, as well as quantity, as a result of radical transformations in the middle of the country and the action of a number of external factors. Attention was drawn and the essence of the original term «threat», which forms the basis of the definitions in official circulation, was revealed. The conditions that should be considered threatening from the point of view of a typical worldview of security are defined and meaningfully characterized. Attention is focused on the concept of «threats to the economic security system of Ukraine», which is a legislative guideline for other aspects in this area. Its indisputable importance and value for law-making and law-enforcement activities is emphasized. It was found that such threats represent a large cohort of phenomena, processes, trends and factors of various origins, to which the state and society must respond effectively. It has been proven that the lack of a proper reaction, including in the case of inefficient use of personnel potential and other resource provision, is accompanied by the appearance of significant risks for Ukraine’s defense capability. It has been established that the national economic interests of Ukraine play a significant role in the identification and typology of priority threats to the analyzed security component. Taking into account the views of famous scientists and current legislation, the category of the same name was analyzed in detail. On the basis of the above, a typology of urgent main current threats to the system of economic security of Ukraine is proposed. Attention was drawn to the fact that the successful solution of tasks in these aspects will form a favorable basis for effective prevention and countermeasures against other factors of economic destruction. It was noted that there is an absolute need for systematic work in all the represented directions, based on considerations of the specifics of the named factors, including their destructive impact on all components of Ukraine’s national security.

Issues of personal data protection when using cloud technologies

It is indicated that in today’s digital world, where the amount of stored and processed data is growing at a frantic pace, issues of personal data protection are becoming increasingly important. With the proliferation of cloud technologies that allow companies and individuals to store and process information on remote servers, a new spectrum of threats to data privacy and security is emerging. The article examines the issue of personal data protection when using cloud technologies, in particular, the need to integrate legal and technological aspects to ensure comprehensive protection. The impact of international regulatory acts, such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Protection Act), on cloud data protection processes is analyzed. Particular attention is paid to problems arising from differences in the legal regimes of different countries, which complicate data management at the international level. The article examines in detail modern technical solutions, such as data encryption and multi­factor authentication, which are key elements in protecting confidential information. These technologies have been found to provide a high level of protection, but also have their vulnerabilities. Based on this, it is proposed to invest in the latest technologies, in particular quantum encryption and artificial intelligence, to improve the effectiveness of data protection. Practical cases of data leaks are separately considered, which emphasize the importance of proper configuration of security systems and regular monitoring. Analysis of the results of empirical research shows the need to adapt business processes to changes in the regulatory environment and introduce new technologies to reduce risks. Based on the received data, recommendations for organizations are formulated, which include reviewing data protection policies, investing in modern technologies and training employees. The conclusions of the article emphasize the need for a comprehensive approach to ensuring the protection of personal data in cloud environments. This involves taking into account both modern technologies and current legal requirements to ensure adequate data protection and minimize possible risks.

Estimation of hydrochemical profile and trophic status of a coastal Ramsar site in south west coast of India

Coastal lakes are confronting a spectrum of threats arising from both natural processes and human activities. This study presents an investigation into the nutrient and trophic status of the Ramsar-designated (Ramsar site No. 1204) Ashtamudi Lake (ASL; area= 56 km2), situated along the southwest coast of India. To establish a comprehensive hydrochemical profile, 50 representative water samples comprising lake, surface and bottom layers were systematically collected from 25 distinct sites during the non-monsoon and monsoon seasons of 2017. Physcio-chemical characteristics were evaluated using standard protocols and procedures. The salinity observations consistently position the majority of the lake's area within the 'polyhaline' condition (18–30 PSU) throughout the year. The evaluation of nutrient ratios (DIN/DIP, DSi/DIN, and DSi/DIP) during the respective seasons highlights a prevalent phosphorus (P) and silicon (Si) limited scenario during non-monsoon period. This condition is marked by elevated dissolved inorganic nitrogen (DIN) concentrations juxtaposed with lower levels of dissolved inorganic phosphorus (DIP) and dissolved silicon (DSi). The computed Trophic Index (TRIX) unequivocally denotes a 'hypertrophic' state within the lake during the non-monsoon season. This characterization is upheld by TRIX values of 7.73±0.61 and 8.06±0.62 for surface and bottom water during non-monsoon. Further insight into the TRIX findings is derived from the supplementary Efficiency Coefficient (EF) index, which accentuates ASL's limited nutrient assimilation in conjunction with a substantial input of DIN. Overall, the sustained condition of occurrence of low dissolved oxygen and elevated DIN/DIP ratios exacerbates the hypertrophic state. This complex interplay heightens ecological strain, thereby imperiling aquatic organisms, particularly valuable fishery resources. Unfortunately, this situation invariably curtails the essential ecosystem services furnished by ASL. Therefore, the adoption of strategic interventions aimed at ameliorating nutrient influx and restoring a balanced trophic equilibrium stands imperative to mitigate ecological degradation and uphold the lake's pivotal contributions to its surrounding environment.

Vulnerability-oriented risk identification framework for IoT risk assessment

The proliferation of Internet of Things (IoT) systems across diverse applications has led to a notable increase in connected smart devices. Nevertheless, this surge in connectivity has induced a broad spectrum of vulnerabilities and threats, jeopardizing the security and safety of IoT applications. Security risk assessment methods are commonly employed to analyze risks. However, traditional IT and existing IoT-tailored security assessment methods often fail to fully address key IoT aspects: complex assets intercommunication, dynamic system changes, assets’ potential as attack platforms, safety impacts of security breaches, and assets resource constraints. Such oversights lead to significant risks being overlooked in the IoT ecosystem. In this paper, we propose a novel vulnerability-oriented risk identification framework comprising a four-step process as a core element of IoT security risk assessment, applicable to any IoT system. Our process enhances both traditional and IoT-specific security risk assessment methods by providing tailored approaches that address their crucial oversights for comprehensive IoT risk assessment. We validate our process with a case study of an IoT smart healthcare system using a proposed expert-driven approach. The results confirm that our process effectively identifies critical attack scenarios originating from the lack of proper security measures, mobility, and intercommunication processes of IoT devices in the healthcare system. Furthermore, our analysis reveals potential attacks that exploit the IoT devices as platforms to target the backend and user domains. We demonstrate the feasibility of our process for identifying realistic risks by conducting simulations of two derived attack scenarios using the Contiki Cooja network simulator.

Stepping out of the shadows: the legitimacy of the Bahamas’ NCIA

ABSTRACT The Bahamas is a relatively small archipelago challenged by a broad spectrum of diverse, complex, and consequential national security threats that extend to the ‘unknown’ of a global security threat equation. To address the threat spectrum, Bahamian policymakers incorporated the National Crime Intelligence Agency (NCIA) within the national security architecture. For various reasons, Bahamians are sceptical about, scared of, and uncomfortable with the idea or reality of the NCIA. As such, the NCIA has to pass the legitimacy test as it steps out of the shadows. This article provides a preview into the legitimacy test of The Bahamas’ professional secret intelligence service.

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications.

High-altitude illnesses, encompassing a spectrum of health threats including Acute Mountain Sickness (AMS), pose significant challenges to individuals exposed to high altitude environments, necessitating effective prophylaxis and immediate management. Given the variability in individual responses to these conditions, accurate prediction of high-altitude illnesses onset is of paramount importance. This review systematically consolidates recent advancements in research on predicting AMS by evaluating existing cohort data, predictive models, and methodologies, while also delving into the application of emerging technologies. Through a thorough analysis of scholarly literature, we discuss traditional prediction methods anchored in physiological parameters (e.g., heart rate, respiratory frequency, blood pressure) and biochemical markers, as well as the integration and utility of novel technologies such as biosensors, genetic testing, and artificial intelligence within high-altitude prediction research. While conventional pre-diction techniques have been extensively used, they are often constrained by limitations in accuracy, reliability, and multifactorial influences. The advent of these innovative technologies holds promise for more precise individual risk assessments and personalized preventive and therapeutic strategies across various forms of AMS. Future research endeavors must pivot decisively towards the meticulous identification and stringent validation of innovative predictive biomarkers and models. This strategic re-direction should catalyze intensified interdisciplinary cooperation to significantly deepen our mechanistic insights into the pathogenesis of AMS while refining existing prediction methodologies. These groundbreaking advancements harbor the potential to fundamentally transform preventive and therapeutic frameworks for high-altitude illnesses, ultimately securing augmented safety standards and wellbeing for individuals operating at elevated altitudes with far-reaching global implications.

Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring

The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, encompassing diverse approaches such as machine learning, artificial intelligence, behavioral analysis and anomaly detection. Machine learning plays a central role in cyber threat detection, highlighting the effectiveness of deep neural networks in identifying evolving threats. Their adaptability to changing attack patterns is emphasized, underlining their importance for real-time security monitoring. In parallel, ensemble learning is explored, combining multiple models to improve overall detection accuracy and create a robust defense against a spectrum of cyber threats. The literature reviewed highlights the importance of behavioral analysis, with a novel approach that integrates user behaviour profiling with anomaly detection. This has proven effective in identifying suspicious activity within a network, particularly insider threats and stealthy attacks. Another behavioral framework using User and Entity Behavior Analytics (UEBA) is presented for enhanced anomaly detection, highlighting the importance of context-aware monitoring in improving threat detection accuracy. Collaborative defense mechanisms emerge as a major focus of the research papers reviewed, exploring the potential of sharing threat information between organisations to enhance collective security monitoring. Their findings underscore the importance of a collaborative approach to staying ahead of rapidly evolving cyber threats. Some types of cyber-attacks are also analysed in the context of a security operations centre (SOC) monitoring environment using a security information and event management (SIEM) tool - Splunk. In conclusion, this survey paper synthesizes recent advances in cyber threat detection methods in security monitoring that integrate machine learning, behavioral analysis, and collaborative defense strategies. As cyber threats continue to evolve, these novel methods provide valuable insights for researchers, practitioners, and organisations seeking to strengthen their cybersecurity defenses. This concise overview emphasises the multi-dimensional approach required to secure digital ecosystems, providing a concise yet comprehensive guide to modern cyber threat detection strategies.

Detecting vulnerabilities in website using multiscale approaches: based on case study

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.

Tehrik-e-Taliban Pakistan’s (TTP) Threat Spectrum: Assessing Pakistan’s Counter-Terrorism Policy in Post-US Withdrawal Era

The Afghan Taliban administration has precipitated a substantial shift in regional politics due to the power vacuum left in the wake of the US withdrawal. After the Taliban takeover, Afghanistan has witnessed immense economic challenges, political crises, and security challenges in Afghanistan. Consequently, the chaotic situation in Kabul generated a substantial threat to Pakistan’s security. The main purpose of this research is to investigate the Tehrik-e-Taliban Pakistan (TTP) threat spectrum for Pakistan and its Counter-Terrorism Policy in the post-US withdrawal era. This study adopted a purely qualitative method and collected primary data from the relevant experts through semi-structured interviews. Moreover, it comprehensively discusses and analyses Pakistan’s rising security concerns due to the reactivation of TTP in the post-US withdrawal period. The results expose that Pakistan faces various security challenges from Afghan soil in the post-US withdrawal. Unstable Afghanistan and TTP’s reactive mod on the Taliban conquering return some indispensable challenges for Pakistan’s security and stability. Finally, the findings of the study suggest that fostering cordial relations with the Afghan Taliban administration and proactive countering terrorism policies are crucial for Islamabad to preserve its security interests, particularly countering TTP’s threat. Afghanistan and Pakistan should adopt a collaborative approach and prioritize cooperation and mutual trust for eliminating the transborder terrorism to achieve. These practical measures will not only contribute to reducing radicalism, terrorism, and sectarianism but also achieve peace and prosperity on both sides.

Typology and implications of verified attacks on health care in Ukraine in the first 18 months of war.

Attacks on health care are part of the spectrum of threats that health care endures during conflict. Protecting health care services against attacks depends on understanding the nature and types of attacks that occur during conflict. The World Health Organisation has implemented the Surveillance System for Attacks on Health Care (SSA) in Ukraine since 2020, and the system has continued to monitor and report on attacks on health care during the war in Ukraine. This study aims to analyse the data reported through the SSA for the first 18 months of the war. This paper involves a retrospective, descriptive study based on the analysis of publicly available SSA data of all incidents of attacks on health care in Ukraine reported through the SSA between February 24th 2022 and August 24th 2023. Out of the 1503 verified attacks, 37% occurred in the initial six weeks of the war. Attacks involving violence with heavy weapons were among the most common incidents reported (83%). The reported attacks were associated with a total of 113 deaths and 211 injuries among health care workers and patients: 32 (2%) attacks were associated with a death of a health care worker or patient, and 63 (4%) were associated with an injury. Health transports facing attacks had a higher probability of experiencing casualties than other health resources (p<0.0001, RR 3.1, 95%CI 1.9-4.9). In conclusion, the burden of attacks on health care in Ukraine was high and sustained over the course of the first 18 months of the war. Reported casualties were not homogenously distributed among attack incidents, but occurred in a set of high-casualty incidents. Health transports were found to be particularly vulnerable. In addition to continued calls for a cessation of hostilities, prevention, protection, mitigation, and reconstruction strategies are urgently required.

Blockchain-Enabled Secure and Interoperable Authentication Scheme for Metaverse Environments

The metaverse, which amalgamates physical and virtual realms for diverse social activities, has been the focus of extensive application development by organizations, research institutes, and companies. However, these applications are often isolated, employing distinct authentication methods across platforms. Achieving interoperable authentication is crucial for when avatars traverse different metaverses to mitigate security concerns like impersonation, mutual authentication, replay, and server spoofing. To address these issues, we propose a blockchain-enabled secure and interoperable authentication scheme. This mechanism uniquely identifies users in the physical world as well as avatars, facilitating seamless navigation across verses. Our proposal is substantiated through informal security analyses, employing automated verification of internet security protocols and applications (AVISPA), the real-or-random (ROR) model, and Burrows–Abadi–Needham (BAN) logic and showcasing effectiveness against a broad spectrum of security threats. Comparative assessments against similar schemes demonstrate our solution’s superiority in terms of communication costs, computation costs, and security features. Consequently, our blockchain-enabled, interoperable, and secure authentication scheme stands as a robust solution for ensuring security in metaverse environments.

Network Anomaly Traffic Analysis

This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, artificial intelligence, and behavioral analysis. This integration aims to augment the precision, efficiency, and adaptability of network anomaly traffic monitoring systems. Proposed future strategies include advanced methods in data preprocessing, model development, pattern recognition, and adaptive adjustments. These strategies are directed towards fortifying network defenses in response to the dynamically changing spectrum of cyber threats.

Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips.In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles.While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency.This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems.

Navigating the digital currency landscape: A comprehensive examination from blockchain foundations to website security

This paper offers an exhaustive exploration of the burgeoning digital currency realm, spanning from the foundational tenets of blockchain technology to the evaluation of pivotal website security vulnerabilities. The rise of decentralized cryptocurrencies, anchored in pioneering cryptography and consensus protocols, has deeply transformed traditional financial interactions. However, this transformation brings to the forefront new cybersecurity risks, borne from the intricate nature of these systems. Addressing these imminent challenges, the study introduces a holistic security model, meticulously designed for the Ethereum blockchain environment. This model integrates methods such as smart contract rigorous validation, transaction irregularity spotting, and network assault emulation. Rigorous experiments and simulations vouch for the models efficiency in pinpointing security breaches, marking an impressive 85% detection precision and an 81% robustness against uncharted zero-day onslaughts not encountered during model preparation. When juxtaposed with individual security tactics, the model exhibits a dominant stance in terms of attack deterrence, threat spectrum, and system productivity. Yet, the relentless advent of innovative attack strategies in this field means vulnerabilities remain. To bolster applicability in real-world scenarios, delving deeper into forecasting methodologies and broader tests on active systems prove essential. In essence, this multifaceted research initiative illuminates both theoretical and practical pathways to refine the strategic outline for unyielding security measures, championing prudent innovation and oversight in the rapidly evolving cryptocurrency landscape.

Chemical composition, antifungal, antibacterial, and insecticidal activity of Echinophora chrysantha essential oil

This study presents an investigation of the chemical composition and biological activities of the essential oil (EO) extracted from aerial parts of Echinophora chrysantha (EC). Gas chromatography-mass spectrometry (GC-MS) analysis revealed the presence of twenty-two components, representing 97.5% of the total oil. Major constituents were determined as α-phellandrene (55.74%), β-phellandrene (17.41%), and p-cymene (8.95%). The EO was screened for antimicrobial and antifungal activity against a panel of bacteria and fungi using a broth micro-dilution and fumigant activity assay, respectively. EO sample exhibited better activity against Listeria monocytogenes, Staphylococcus aureus Enterococcus faecium with 31.25, 31.25, and 62.5 μg/mL MIC values. EOs showed moderate mycelium growth inhibitory activity against Phytophthora infestans (32.9±1.9%), Verticillium dahliae (42.3±1.8%), and Fusarium oxysporum f. sp. lycopersici (53.0±2.9%) at 8 μL/petri concentration. The insecticidal activity of EO was also evaluated against two insects (Rhyzopertha dominica and Tribolium confusum) showing moderate activity with mortality rates of 25.8±0.6% and 37.9±1.2% at a 5% (v/v) concentration. Literature search has not revealed any previous work on the antimicrobial, antifungal, and insecticidal activities of the EO from E. chrysantha. In conclusion, this is the first report describing the biological activities of this plant EOs. Additionally, these findings highlight the practical implications of the utilizing essential oil of EC as a potential natural remedy against a spectrum of microbial threats in both pharmaceutical and food preservation applications.

Analyze and Forecast the Cyber Attack Detection

Abstract: The relentless evolution of digital technologies has ushered in an era of unprecedented connectivity and convenience, but it has also exposed our digital infrastructure to a growing spectrum of cyber threats. In this context, the importance of robust cyber attack detection mechanisms cannot be overstated.The increasing complexity and sophistication of cyber threats necessitate equally advanced detection techniques. Cyber attack detection plays a pivotal role in safeguarding digital assets and maintaining the integrity of critical systems. This abstract delves into the foundational principles and contemporary strategies employed in this domain. Detecting cyber attacks presents a multifaceted challenge due to the ever-evolving tactics employed by malicious actors.

Strengths, Weaknesses, Opportunities, and Threats of Using ChatGPT in Scientific Research

The versatility of ChatGPT extends across diverse domains, including scientific research. This study delves into the transformative prospects of integrating ChatGPT into scientific research, achieved through a SWOT analysis. The analysis explores the model's strengths, which encompass a vast knowledge base, language proficiency, information retrieval, and the capacity for continuous learning. Conversely, it exposes its weaknesses, including a lack of contextual understanding, potential overreliance on training data, limitations in verifying information, and constrained critical thinking abilities. Amidst these factors, opportunities arise, such as facilitating literature reviews, fostering collaborative brainstorming, enabling seamless language translation and interpretation, and amplifying knowledge dissemination. Nonetheless, a spectrum of threats looms, encompassing concerns related to plagiarism, ethical quandaries, the propagation of misinformation, and even the potential erosion of higher-order cognitive thinking. These multifaceted aspects necessitate comprehensive consideration. Recommendations for researchers embarking on ChatGPT integration include a balanced approach that harmonizes AI and human ingenuity, thereby upholding research integrity. The potential of ChatGPT to reshape scientific inquiry can only be realized through conscientious use and ongoing oversight.

COMPREHENSIVE REVIEW ON CYBERSECURITY: MODERN THREATS AND ADVANCED DEFENSE STRATEGIES

In the rapidly evolving landscape of cyberspace, the prevalence of sophisticated cyber threats has escalated, posing formidable challenges to individuals, organizations, and nations. This comprehensive review explores the contemporary panorama of cybersecurity, focusing on the latest threats and the advanced defense strategies employed to mitigate them. The analysis encompasses a wide spectrum of cyber threats, including malware, ransomware, phishing attacks, and advanced persistent threats (APTs), shedding light on their evolving tactics, techniques, and procedures. The review delves into the intricate world of cybercrime, emphasizing the motives behind attacks and the diverse range of threat actors involved, from individual hackers to state-sponsored entities. By examining recent case studies and real-world incidents, the review provides valuable insights into the dynamic nature of cyber threats, emphasizing the need for proactive and adaptive cybersecurity measures. Furthermore, the review critically evaluates cutting-edge defense mechanisms and strategies deployed to counteract these threats. It explores advancements in artificial intelligence, machine learning, and behavioral analytics, showcasing their pivotal roles in bolstering cybersecurity defenses. Additionally, the review discusses the importance of threat intelligence sharing, collaborative efforts, and international cooperation to fortify the global cyber defense ecosystem. As cybersecurity extends beyond technical measures, the review also addresses the human element, emphasizing the significance of cybersecurity awareness training and the role of employees in fortifying organizational resilience. It scrutinizes regulatory frameworks and compliance standards that play a crucial role in shaping cybersecurity policies and practices. By synthesizing the latest research, industry best practices, and expert insights, this comprehensive review aims to provide a holistic understanding of the current state of cybersecurity, offering practitioners, policymakers, and researchers a valuable resource to navigate the intricate challenges posed by modern cyber threats and to develop effective defense strategies for the digital age.&#x0D; Keywords: Cybersecurity, Threats, Defense Strategy, Cyber Threats, Review.

Cloud‐based deep learning architecture for DDoS cyber attack prediction

AbstractConventional methodologies employed in detecting distributed denial‐of‐service attacks have frequently struggled to adapt to the dynamic and multi‐faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge this technological chasm, this research introduces a state‐of‐the‐art intrusion detection system firmly rooted in advanced Deep Learning techniques. By leveraging the expansive and adaptable nature of cloud‐centric, service‐oriented architectures, we not only bolster detection precision but also offer a solution designed for modern infrastructures. This system provides enterprises with a robust, easily deployable tool that is both versatile in its application and proactive in its defence approach, ensuring that networks remain resilient against the continuously evolving spectrum of cyber threats.

Increased the Role of Defense Human Resources in Facing of the Threats

National defense is all efforts to defend state sovereignty, territorial integrity, and the safety of the entire nation from threats and disturbances to the integrity of the nation and state. The problems of national defense, which are increasingly varied in the development of the threat spectrum, certainly require reliable human resources for implementing or making national defense policies. Indonesia with a large population certainly has great potential in the procurement of defense resources. However, if the quality cannot keep up with current threat dynamics and technology then the number is meaningless. The purpose of this research is to find out how to increase the role of Indonesian defense human resources to deal with threats that occur to realize national security. The research method used in this study is a qualitative descriptive analysis method with data collection techniques through literature and library studies. The results of this study are that the government’s role is very important in increasing the superiority of defense human resources. Such as providing training, education, providing motivation, and synergizing with institutions and parties related to the defense industry to improve HR capabilities in dealing with technological advances