Security Threats Research Articles

Data security strategies to avoid data breaches in modern information systems

Introduction: Cyber threats are constantly increasing in severity and targeting organizations in all industries, and result in loss of valuable data and a decline in stakeholders’ confidence. Advanced information systems positively impact overall organizational operations and functionality but bring new opportunities that adversaries seek. This research article seeks to look at how organizations can enhance data protection and prevent breaches in modern technology landscapes. Materials and Methods: An initial search and screener was done on selected journal article from the year 2015-2024 that was peer reviewed, industry report, and case study. The conceptual framework combined the theoretical approach to data breaches, malware attacks, and cybercriminal motives with factors, such as users’ perceptions, legal requirements. Data from 22 original papers were subject to a qualitative content analysis approach, and thematic synthesis was used to determine common patterns in security practices, threats, and risk management mechanisms. Results: The analysis revealed several key themes: 1) The major importance of security awareness training as the key to strengthening the human factor which is the weakest link in the context of data protection. 2) The validity of the layer defense principle that entailed a combination of technical measures and sound policies and procedures. 3) The role of encryption, access controls, and data minimization to protect information and data that is more important today than ever before. 4) Threat Intelligence and incident response planning to detect the breaches in advance before the threat actors begin executing the attack. 5) The role of AI and machine learning as threats and as the possibility to identify and prevent threats while using their opportunities for data analysis in cybersecurity. Discussion: The results imply that the protection of information should be an organizational, risk management approach with current and future threats in mind. However, while technical measures must be adopted, so too must the emphasis be placed on promotion of a security-aware culture as well as the adherence to regulatory requirements. The research indicates areas for future development, with specific emphasis on the issues of insider threats and protection of information in cloud and IoT contexts. Drawbacks include the fact that technological advancements are rather fast, which makes it difficult for academic publications to keep up with them. Additionally, there could be situations when certain industries fail to report breaches. Conclusion: Preventing data breaches in today’s information systems, therefore, cannot be addressed through a single solution that involves merely the application of people, processes or technology, but rather through a holistic approach oriented towards the integration of the three aspects. Organizations need to be creative about the issue and adopt emerging technologies that address existing and fundamental security risks. Further research should focus on the effects of AI-based threats calculate the return of security investments, and establish guidelines for data protection specific to industries.

AI-augmented cyber security threat intelligence – enhancing situational awareness

AI-augmented cyber security threat intelligence – enhancing situational awareness

Imbuing climate security with positive peace: a peace continuum approach to sustaining peace during climate crisis

Abstract Climate change impacts peacebuilding and is an important topic within the UN Security Council (UNSC). Yet, the UN's member states take conflicting positions on whether climate change should be framed as a security threat or a development issue, and several scholars have warned against its securitization. Introducing a ‘peace continuum’ approach to climate security, this policy paper shifts attention from a ‘for or against’ debate on framing climate change as a security threat, instead considering climate crisis as a call for rethinking security itself. A peace continuum approach connects climate finance and adaptation with a continuum of peace efforts. This broadens the outlook of security to integrate human and ecological security concerns and offers a perspective on climate security that emphasizes ‘positive peace.’ Imbuing climate security with the notion of positive peace resonates with peacebuilding norm developments around the primacy of politics, prevention and adaptive approaches. Grounding the discussion of a peace continuum approach in case-studies from the Horn of Africa, the paper highlights three major developments. First, new networked climate security governance has emerged within and around the UN and UNSC, showing promise for scaling up a peace continuum approach. Second, recognizing climate change as a security issue can enhance commitments to climate finance and adaptation, while finance and adaptation can contribute to peace dividends, including in ‘high-risk’ contexts. Third, regional climate security hubs are emerging as vehicles for regional–international knowledge exchanges, and serve as entry-points for flexible UN support, well suited to shifting peacebuilding trends away from large-scale missions. In a context of geopolitical competition, strained multilateralism and competing priorities, there is no automaticity ensuring the advancement of a progressive climate security agenda—achieving that will require continuous, committed efforts.

Machine Learning Algorithms for Intrusion Detection in WSNs

Machine learning (ML) has emerged as a potent tool for optimizing network performance and extending network lifespans. By automating complex data processing tasks, ML algorithms enable real-time solutions that optimize resource allocation and utilization. ML's ability to process vast, intricate datasets with speed and precision empowers networks to adapt dynamically to evolving demands. [13]. Wireless Sensor Networks (WSNs), composed of interconnected sensor and sink nodes, exemplify the transformative potential of ML. These distributed, decentralized networks inherently possess self-organization and self-healing capabilities. By integrating ML techniques, WSNs can significantly enhance their efficiency, reliability, and scalability, enabling a wide array of applications, from environmental monitoring to military operations. As advancements in electronics and wireless communication continue to drive WSN evolution, the fusion of ML and WSNs promises to unlock new horizons in network intelligence and performance. However, WSNs face several challenges, including resource constraints such as limited memory, processing power, and energy. Additionally, the physical infrastructure of WSNs must be secured to protect sensitive data, especially in privacy-critical applications. To address these challenges, the integration of machine learning offers a promising solution. By leveraging ML techniques, WSNs can adapt to dynamic environments, optimize energy consumption, and detect and mitigate potential security threats. This enables the deployment of WSNs in diverse applications, from environmental monitoring to smart cities, while ensuring data privacy and system security. Keywords: Intrusion Detection System (IDS), Security, Wireless Sensor Network (WSN), Attacks, Reinforcement Learning (RL), Denial-of-Service (DoS), Networks, Machine Learning (ML)

Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objective is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach.

Incremental Machine Learning-integrated Blockchain for Real-time Security Protection in Cyber-enabled Manufacturing Systems

Abstract Cyber-enabled manufacturing systems become increasingly data-rich, generating vast amounts of real-time sensor data for quality control and process optimization. However, it also exposes these systems to significant cyber-physical security threats. For instance, attackers may delete, change, or replace data, leading to defective products, damaged equipment, or operational safety hazards. False data injection attacks can compromise machine learning (ML) models, resulting in erroneous outcomes. To mitigate these risks, it is crucial to employ robust data processing techniques that can adapt to varying process conditions and detect anomalies in real-time. In this context, incremental machine learning (IML) can be valuable, allowing models to be updated incrementally with newly collected data without retraining from scratch. Moreover, although recent studies have demonstrated the potential of blockchain in enhancing data security in manufacturing systems, most existing frameworks are primarily based on cryptography, which does not sufficiently address data quality issues. Thus, this study proposes a gatekeeper mechanism to integrate IML with blockchain and discusses how this integration could potentially increase data integrity of cyber-enabled manufacturing systems. The proposed IML-integrated blockchain can potentially address data security concerns from both intentional (e.g., malicious tampering) and unintentional alterations (e.g., process anomalies). The case study results show that the proposed gatekeeper integration algorithm can successfully filter out over 80% of malicious data entries while maintaining comparable classification performance to standard IML models. Furthermore, the integration of blockchain enables effective detection of tampering attempts, ensuring the trustworthiness of the stored information.

Research on power plant security issues monitoring and fault detection using attention based LSTM model

OverviewFor Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society.ObjectivesA prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system.MethodologyFor the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used.ResultsThe findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.

Challenges Faced by the Judiciary in Implementing Cybersecurity Laws in Pakistan

Due to the inherent growth of technological elements, today’s world of governance, commerce, and societies has been revolutionized, opening new vistas of opportunity as well as new horizons of cyber security threats. Fraud such as hacking, identity theft, and ransom ware are important threats that are knowledgeable about to personal and business associations and the government. In Pakistan, law enforcement agencies and the judiciary are overwhelmed with inadequate resources lack of training, and technical knowledge. However, the institutions operating in the country still face legislative gaps and enforcement challenges even after the passing of the Prevention of Electronic Crimes Act (PECA) 2016 means. The paper deals with Pakistan's situation concerning cyber security enhancement judicial practices and legislation and compares it to the United States’ strategic enhancement in the security domain. There are some leading projects like the National Judicial Automation Program and e-Courts, but still, it seems there are a lot more systems problems and no significant digital forensics available in the judiciary of Pakistan. The comparative analysis indicates that Pakistan needs to reform its laws, train its judges specialized in cybercrime and look for international cooperation when it comes to fighting transnational criminals engaged in cybercrimes. The importance of increasing the level of protection in cyberspace is highlighted by raising public awareness or engaging private structures. This research stresses the need to mitigate cyber security threats through legal, organizational, and social change. From best practices and international collaboration so that Pakistan can better address the issue, increase the capacity of its judicial system in combating cyber threats, and protect its critical IT assets. The actual findings of the research include ways to improve the efficiency of Courts, protect digital rights, and strengthen the governance of cyber security in the country.

Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health.

Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This review aims to offer a perspective within the actor-network framework, exploring the technical architectures, linguistic dynamics, and psychological effects underlying human-LLMs interactions. Based on this theoretical foundation, we propose four categories of risks, presenting increasing challenges in identification and mitigation: universal, context-specific, user-specific, and user-context-specific risks. Correspondingly, we introduce CORE: Chain of Risk Evaluation, a structured conceptual framework for assessing and mitigating the risks associated with LLMs in public mental health contexts. Our approach suggests viewing the development of responsible LLMs as a continuum from technical to public efforts. We summarize technical approaches and potential contributions from mental health practitioners that could help evaluate and regulate risks in human-LLMs interactions. We propose that mental health practitioners could play a crucial role in this emerging field by collaborating with LLMs developers, conducting empirical studies to better understand the psychological impacts on human-LLMs interactions, developing guidelines for LLMs use in mental health contexts, and engaging in public education.

Firmware Over the Air for Securely Updating ECUs of the Vehicle

Introduction: An automobile is a software-defined machine on top of the wheels including more than 100 electronic control units (ECU) with million lines of code. The integration of ECU in the automobile ensures the customer’s needs by providing safety, security, entertainment, and comfort features. Method: The firmware integrated into the ECU should be updated to avoid latency in operation and bugs, and to add new features. The traditional update process of ECU holds loopholes like more waiting time, unavailability of service centers, and security threats. To overcome this, over-the-air (OTA) updates are introduced in the vehicle, but security is the major concern while transmitting firmware over the air. Result: The proposed system ensures the wireless firmware update with the uptane framework with background Timestamp Update Framework (TUF) ensures the security. The timestamp generated is valid for 86400 seconds to validate the freshness. In addition, security assessment and reverse engineering are performed on the designed system to check for security breaches. Conclusion: The system secures the firmware over arbitrary and replay attacks on the Original Equipment Manufacturer (OEM) server.

Securing cyber-physical robotic systems for enhanced data security and real-time threat mitigation

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library.

Structural challenges in adapting to modern warfare: lessons from the Ukrainian War and the Czech defence industry

ABSTRACT This paper examines the structural challenges faced by the Czech defence industry in adapting to modern warfare, using lessons from the Ukrainian War (2022–2024). It explores how legacy structures from the Soviet era continue to influence defence procurement, strategy, and military modernisation efforts in post-socialist Central European states. The research underscores the limited adaptability of the Czech defence sector, constrained by entrenched industrial practices and a preference for traditional land systems, despite the demonstrated need for technological innovation in other areas. Using a comprehensive analysis of defence policies, procurement contracts, and interviews with key stakeholders, the paper highlights the barriers to rapid modernisation and integration of advanced military technologies. It concludes that whilst the Czech defense industry remains heavily path-dependent, a strategic realignment focused on technological innovation and greater integration with NATO initiatives is essential for enhancing the country's military capabilities in response to contemporary security threats.

Принципы построения системы органов государственной власти с учетом политических рисков

The aim of this article is to examine the principles of building a system of state authorities taking into account political risks. Currently, there is a discernible interconnection between domestic and foreign policy, as well as an understanding of the overall picture of the world in the public consciousness of the country’s citizens. The author notes that, for state bodies, the category of risks can generate security threats. Ensuring state and public security requires the formation and effective functioning of special mechanisms of strategic management. It is observed that the effectiveness of state authorities in managing political risks is determined by a multitude of factors and varies depending on the structure and condition of the socio-political system. A contradiction is identified between the established political system and the economic form of state organization, as well as the influence of social institutions on the formation of political risks during the implementation of decisions made by state authorities. The laws of development in the dialectical paradigm of the world order and as part of the theory of confrontation are determined by the goals, economic potentials, technological development in the realization of plans, as well as the attitude of the world community. The author concludes that the principles of political decision-making should be grounded in the laws of dialectics – laws of development that do not con-tradict the natural course of global processes and promote the advancement of state power institutions.

Penerapan Super Enkripsi Algoritma Autokey Cipher dan El-Gamal File Gambar

Autokey Cipher is a classic encryption technique that uses substitution methods, providing a higher level of security compared to other classical techniques such as Caesar and Vigenère ciphers. It generates keys that match the length of the plaintext, ensuring that each character of the original data has a corresponding key character, which is crucial for effective encryption.on the other hand, the ElGamal algorithm is an asymmetric encryption method that uses different keys for encryption and decryption, relying on prime numbers and discrete logarithms to secure messages. This algorithm was chosen because of its strong security features, making it difficult for unauthorized users to decrypt information without the right key. The combination of these two algorithms aims to create a more secure encryption system. Autokey Cipher contributes complex substitution techniques, while ElGamal adds an additional layer of security through an asymmetrical key structure. This dual approach is very beneficial for protecting sensitive data, such as image files, from unauthorized access. highlighting the potential of these combined methods to provide stronger defenses against a wide range of security threats in the digital landscape

The Right to Food, Deliberate Starvation, and the EU’s Response to International Courts’ Rulings

This article examines starvation as a crime of global concern, particularly in situations of conflict and humanitarian emergencies, and the European Union’s role in combating this crime. The analysis focuses on the international and European legal framework on the right to food, the impact of armed conflict on this right, and the use of starvation as a method of warfare. The article concludes with a critical assessment of the EU’s response to food security threats, specifically in relation to the recent Israeli-Palestinian crisis.

Robust Access Control for Secure IoT Outsourcing with Leakage Resilience

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities.

A Paradigm Shift in Policy-making: The Evolution of Thailand's Anti-Communism Strategy through Prime Minister's Office Order No. 66/2523

Background and Objectives: This article studies the historical and political significance of the Prime Minister's Office Order No. 66/2523, a landmark directive that transformed Thailand's approach to communist insurgency. This study analyzes how international geopolitical shifts, particularly in Sino-American relations and regional dynamics, combined with domestic factors to catalyze a fundamental change in Thailand's counter-insurgency strategy. The order marked a crucial transition from military suppression to a more nuanced "politics leading military" approach that recognized communist sympathizers not merely as security threats but as citizens influenced by political ideologies and state-induced grievances. Methods: Utilizing historical research methodology and drawing on both primary and secondary documents, this article traces the evolution of Thai anti-communist policies from the 1960s to the early 1980s. The research particularly focuses on how external factors - including the U.S. strategic realignment in Asia, China's pragmatic turn under Deng Xiaoping, and Vietnam's regional ambitions - interacted with domestic political developments to create both the necessity and opportunity for making the unprecedented policy in anti-communism. Results: Key findings demonstrate that Order 66/2523 represented more than a tactical shift in counter-insurgency; it reflected Thailand's growing diplomatic maturity in leveraging changing international dynamics to address domestic challenges. While the order facilitated the reintegration of former insurgents and their sympathizers, it also served broader strategic purposes in Thailand's regional positioning. This analysis contributes to understanding how domestic policy innovation can emerge from the intersection of international pressures and local imperatives, offering lessons for contemporary approaches to political reconciliation and security challenges. Application of this study: This analysis provides insights into the development of Thai military doctrine and civil-military relations. It demonstrates how external and internal factors combine to drive policy evolution, with implications for understanding modern counter-insurgency approaches. Conclusions: While Order 66/2523 marked an important shift in official policy, its success built on pre-existing conditions. The study challenges the superficial perspective about the order's impact while acknowledging its significance in Thai military concepts about political solutions to the security challenge problem. This analysis not only contributes to a deeper understanding of Thailand's political maneuvers during the critical Cold War juncture but also underscores the complex interplay between domestic policies and international relations. The study demonstrates how a successful and creative policy often emerges from a combination of international opportunities with domestic imperatives, offering the thoughts that can be adapted to contemporary challenges in political reconciliation and security policy.

Analysis Of The Development Of An Early Detection System For Cryptographic-Based Ransomware Attacks In A Cloud Environment

In the digital era, ransomware attacks have become a significant threat to security systems, especially in cloud computing environments. These attacks encrypt victim data and demand ransom, causing considerable financial and operational losses. This Study aims to develop a cryptography-based early detection system for ransomware attacks to protect data in cloud environments. Using the Systematic Literature Review (SLR) approach, this Study analyzes literature related to ransomware attacks, cryptographic algorithms, and cloud security. Data are obtained from indexed journals, books, and conferences. The Study's results showed that the implementation of cryptographic algorithms, such as Advanced Encryption Standard (AES), can improve the efficiency and effectiveness of ransomware detection. This system managed to reduce detection time by 48.28%, increase the success rate of data protection from 60% to 95%, and almost double the amount of data protected. This implementation strengthens data security, minimizes the impact of ransomware, and ensures the continuity of cloud user operations. The implications of this Study support the existing literature on the importance of cryptography in mitigating digital security threats while providing practical guidance for organizations in adopting this technology. Further research is recommended to integrate cryptographic algorithms with technologies such as blockchain to increase the scale and complexity of data protection in a broader cloud environment.

Deep Learning Aided SID in Near‐Field Power Internet of Things Networks With Hybrid Recommendation Algorithm

ABSTRACTIn the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning‐based semi‐autoencoders in conjunction with a hybrid recommendation algorithm to enhance SID tasks. Our proposed method utilizes the deep learning‐based semi‐autoencoder to effectively capture and learn complex patterns from high‐dimensional power IoT data, facilitating the identification of anomalies indicative of potential security threats. The hybrid recommendation algorithm, which combines collaborative filtering and content‐based filtering, further refines the detection process by cross‐verifying the identified anomalies with historical data and contextual information, thereby improving the accuracy and reliability of the SID tasks. Through extensive simulations and practical data evaluations, our proposed framework demonstrates superior performance over conventional methods, achieving higher detection accuracy. In particular, the detection accuracy of the proposed scheme is more than 20% higher than that of the competing schemes.

Mitigating Cyber Attacks for Organization Assets using UEBA (User and Entity Behavior Analytics) and other Machine Learning Solutions from Cloud Security Threats: A Literature Review

User and Entity Behavior Analytics has been one of the key steppingstones towards protecting valuable data and information for organizations that decided to store their assets in a cloud-based storage. The implications suggest that third-party software may not be as reliable as these are prone to attacks due to its accessibility and vulnerable nature. This study aims to provide a comprehensive review that covers User and Entity Behavior Analytics (UEBA) and other machine learning techniques to effectively mitigate cyberattacks and protect organizational assets from cloud-based security threats. The objective is to identify and analyze anomalous user and entity behaviors that may indicate potential cyberattacks and provide recommendations for organizations to enhance their cloud security posture and minimize the risk of data breaches and other security incidents. The research covers some of the algorithms used in detecting anomalies such as Isolation Forest, Deep Autoencoder, and Linear Regression, and emphasizes the adaptability of these chosen algorithms to the dynamic landscape of cyber threats, especially in cloud environments. By arriving at a cohesive integration of machine learning algorithms, this study advocates for a holistic approach that aligns with evolving security challenges. Ultimately, the significance lies in offering a nuanced perspective on effective cyber threat mitigation strategies, contributing to the broader conversation on securing organizational assets in the face of evolving cybersecurity landscapes.