Security Mechanisms Research Articles

Seamless Transition to Post-Quantum TLS 1.3: A Hybrid Approach Using Identity-Based Encryption

We propose a novel solution to streamline the migration of existing Transport Layer Security (TLS) protocol implementations to a post-quantum Key Encapsulation Mechanism for Transport Layer Security (KEMTLS). By leveraging Identity-Based Encryption (IBE), our solution minimizes the necessary modifications to the surrounding infrastructure, enabling the reuse of existing keys and certificates. We provide a proof-of-concept implementation and performance analysis, demonstrating the practical feasibility and effectiveness of our proposed approach.

Evolution and Transformation of Security Patterns in Central Asia in the Context of Chinese Institutional Building

The regional security system in Central Asia is complex. The geopolitical conflict between Russia and the West around Ukraine has had a serious strategic shift in the balance of power and, as a consequence, changes in the architecture of the regional security system. Strategically, this means that Central Asia, as part of the wider post-Soviet space, is undergoing a transition from a Euro-centric to an Asia-centric model with the increasing role of China. Russia and China, as two leading geopolitical actors, have their own security patterns, and at the moment, these security patterns are evolving, in the context of the formation of new regional security mechanisms with the strengthening of the role of China as a leading regional power and security provider.

Malware Classification Using Few-Shot Learning Approach

Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security. Hardware Performance Counter data are used by machine learning algorithms in security mechanisms, such as hardware-based malware detection, to categorize and detect malware. It is crucial to determine whether or not a file contains malware. Many issues have been brought about by the rise in malware, and businesses are losing vital data and dealing with other issues. The second thing to keep in mind is that malware can quickly cause a lot of damage to a system by slowing it down and encrypting a large amount of data on a personal computer. This study provides extensive details on a flexible framework related to machine learning and deep learning techniques using few-shot learning. Malware detection is possible using DT, RF, LR, SVM, and FSL techniques. The logic is that these algorithms make it simple to differentiate between files that are malware-free and those that are not. This indicates that their goal is to reduce the number of false positives in the data. For this, we use two different datasets from an online platform. In this research work, we mainly focus on few-shot learning techniques by using two different datasets. The proposed model has an 97% accuracy rate, which is much greater than that of other techniques.

PROSPECTS FOR THE INTEGRATION OF UKRAINE'S NATIONAL SECURITY MECHANISMS WITH THE COLLECTIVE SECURITY SYSTEMS OF THE EU AND NATO

This article examines the prospects for integrating Ukraine's national security mechanisms with the collective security systems of the European Union and NATO. The study addresses the current challenges and opportunities arising from Ukraine's efforts to align its national security policy with the standards and requirements of international security organizations. The analysis underscores the significance of this integration in the context of ongoing geopolitical tensions and the necessity for enhanced cooperation in safeguarding national and regional security. Utilizing a qualitative research methodology, the article employs a comprehensive literature review, analyzing existing frameworks, policies, and strategic documents relevant to Ukraine's security landscape. Comparative analysis is also applied to assess the effectiveness of Ukraine's security initiatives against those of EU and NATO member states, identifying best practices that can be adapted to the Ukrainian context. The findings reveal critical areas of cooperation, including military interoperability, intelligence sharing, and joint training exercises, which are essential for strengthening Ukraine's defense capabilities. The article further explores the legal, institutional, and operational frameworks necessary for harmonizing Ukraine's security strategies with those of its partners, emphasizing the importance of aligning legislative measures and defense policies with EU and NATO standards. In conclusion, the article advocates for a strategic approach to integration that prioritizes resilience and adaptability within Ukraine's national security mechanisms. By enhancing collaboration with EU and NATO, Ukraine can bolster its security architecture, thereby contributing to regional stability and collective defense initiatives. The recommendations presented aim to guide policymakers in implementing effective strategies for fostering greater alignment with international security frameworks, ensuring a robust response to evolving threats in the region. Through these measures, Ukraine can not only advance its national security objectives but also play a pivotal role in the broader European security landscape.

Clean-Label Backdoor Watermarking for Dataset Copyright Protection via Trigger Optimization

High-quality datasets are essential for training high-performance models, while the process of collection, cleaning, and labeling is costly. As a result, datasets are considered valuable intellectual property. However, when security mechanisms are symmetry-breaking, creating exploitable vulnerabilities, unauthorized use or data leakage can infringe on the copyright of dataset owners. In this study, we design a method to mount clean-label dataset watermarking based on trigger optimization, aiming to protect the copyright of the dataset from infringement. We first perform iterative optimization of the trigger based on a surrogate model, with targets class samples guiding the updates. The process ensures that the optimized triggers contain robust feature representations of the watermark target class. A watermarked dataset is obtained by embedding optimized triggers into randomly selected samples from the watermark target class. If an adversary trains a model with the watermarked dataset, our watermark will manipulate the model’s output. By observing the output of the suspect model on samples with triggers, it can be determined whether the model was trained on the watermarked dataset. The experimental results demonstrate that the proposed method exhibits high imperceptibility and strong robustness against pruning and fine-tuning attacks. Compared to existing methods, the proposed method significantly improves effectiveness at very low watermarking rates.

THE GEOPOLITICAL AWAKENING OF THE EUROPEAN UNION AGAINST THE BACKDROP OF THE RUSSIAN-UKRAINIAN WAR

The article analyses the impact of the Russian-Ukrainian war on the geopolitical transformation of the European Union (EU). It examines the key changes in the EU's security, energy and international relations strategy, as well as the development of its strategic autonomy and role in supporting Ukraine. The study is based on the analysis of scientific sources and political articles related to the EU's foreign policy and security mechanisms. A comparative analysis of the EU's decisions on sanctions, energy security and military assistance to Ukraine is used. Expert assessments of the geopolitical situation and economic consequences of the war are also taken into account. The article contributes to understanding the role of the Russian-Ukrainian war as a catalyst for the EU's political and strategic awakening. In particular, it highlights the development of the EU's strategic autonomy in response to its energy dependence on Russia and the growth of defence cooperation between member states. The author also explores how the war has contributed to the consolidation of European identity and the strengthening of the desire for global leadership in the face of current international challenges. The author notes that the Russian-Ukrainian war has accelerated changes in the EU's approach to foreign and security policy. The EU has strengthened its strategic autonomy, increased economic sanctions against Russia and mobilised resources to support Ukraine. At the same time, the war has shown the need to develop energy resources independent of external suppliers and to increase the EU's internal defence capabilities. Ukraine's integration into the European space has become an important strategic direction of the EU's policy. An important aspect has been the strengthening of European defence cooperation, which demonstrates the EU's desire for greater independence in security matters. Ukraine's integration into the European space is becoming one of the key directions of the EU's foreign policy, which may accelerate the process of Ukraine's accession to the Union in the future. The author also draws attention to the fact that the issue of strengthening the EU's defence capabilities affects the problem of the international security system, which today, as a result of the Russian-Ukrainian war, is undergoing the greatest transformations since the Second World War.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Sine Cosine Algorithm Based on Optimal Convolutional Autoencoder for Intrusion Detection and Classification Models

Network security comprises a multifaceted method that aims to protect computer networks from malicious activities, unauthorized access, and data breaches. The security mechanism is Intrusion Detection which is an important constituent that is employed to monitor and analyse the network traffic for recognizing and responding to intrusive or suspicious behavior. Innovative methods such as deep learning (DL) are employed to enhance the effectiveness of Intrusion Detection Systems (IDSs). DL is extremely implemented for IDS owing to its proficiency for automatically learning and extracting complex patterns and features from massive and multifaceted network datasets. Neural network (NN) models, permit the system to distinguish between anomalous patterns and normal network behaviors, increasing the accuracy of intrusion detection. The flexibility of DL methods to emerging cyberattacks with their adeptness to handle large - scale and various data, positions them as a strong and efficient tool for proactive and intelligent intrusion detection in existing cybersecurity settings. This article presents a Sine Cosine Algorithm with Optimal Convolutional Autoencoder for Intrusion Detection and Classification (SCAOCAE - IDC) method. The developed SCAOCAE - IDC system presents a wide - ranging strategy to improve the precision and effectiveness of IDSs. The method combines diverse advanced mechanisms like Min - Max scalar normalization for data preprocessing, Sine Cosine Algorithm (SCA) for feature selection (FS), Convolutional Autoencoder (CAE) for better feature extraction and classification, and Heap - Based Optimization (HBO) for hyperparameter tuning. The Min - Max scalar makes sure of robust data normalization, SCA increasingly chooses main features, CAE capably captures complex patterns in the data, and HBO fine - tunes hyperparameters for improved system performance. By employing the synergistic combination of such modules, the presented SCAOCAE - IDC algorithm indicates considerable outcomes for increasing the reliability and accuracy of IDSs and classification systems.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

TRENDS OF GLOBAL REGIONALIZATION IN THE CONSTRUCTION OF THE MODERN SECURITY SPACE

One of the most expected manifestations of globalization has become a noticeable intensification of regionalization trends, which are characterized by the dominance of locally oriented values and priorities. The purpose of the article is the analysis of methodological approaches to the study of the modern stage of the development of globalization transformations in the world, which was called "global regionalization", the representation of conceptual guidelines for its study in the context of modern spatial paradigms, the rethinking of the categories of territoriality and regionalism, tracing the influence of global regionalization trends on the development of modern security strategies. The research hypothesis is focused on defending the priority role of global regionalization in the processes of forming the axial coordinates of the new system of structuring of the global world, creating mechanisms of geopolitical balance and security, determining the main trends and factors of fragmentation of the geopolitical space. It has been proven that the ruptures in regional structures caused by neo-turbulence are accompanied by an intense but not problem-free "superstructure" of the global level over the system of international relations. Significant acceleration of the pace of transformational changes under the influence of military conflicts and confrontations conditions the perception of the world system as a global-regional dynamic object and rethinking of existing theories of security development. The study of the content and forms of territorialization using the methodological tools of global regionalism will make it possible to integrate both theoretical approaches to the understanding of the modern stage of the development of globalization processes, and to actualize regional practices related to the safety of human life in the modern global-regional space.

A comprehensive analysis of security mechanism for CSMA/CD Networks: A Survey

In this essay, we examine security against several types of harmful assaults. We also include several known practicable assaults in this. We also learn about secure communication for various mechanisms that offer confidentiality, security, and authentication for the majority of security concerns. An active repeater in CSMA/CD for network comparison for data transfer between two addresses is what we learn about in a logical part. If there is no match, the logic portion instructs the transmitter to switch from broadcasting the data packet to transmitting the clock signal. Bidirectional routing messages are sent from the wireless network to the wired network.At the conclusion, we receive comparison results for the transmitted and received messages to determine if a collision has happened.

Hybridization of Learning Techniques and Quantum Mechanism for IIoT Security: Applications, Challenges, and Prospects

This article describes our point of view regarding the security capabilities of classical learning algorithms (CLAs) and quantum mechanisms (QM) in the industrial Internet of Things (IIoT) ecosystem. The heterogeneity of the IIoT ecosystem and the inevitability of the security paradigm necessitate a systematic review of the contributions of the research community toward IIoT security (IIoTsec). Thus, we obtained relevant contributions from five digital repositories between the period of 2015 and 2024 inclusively, in line with the established systematic literature review procedure. In the main part, we analyze a variety of security loopholes in the IIoT and categorize them into two categories—architectural design and multifaceted connectivity. Then, we discuss security-deploying technologies, CLAs, blockchain, and QM, owing to their contributions to IIoTsec and the security challenges of the main loopholes. We also describe how quantum-inclined attacks are computationally challenging to CLAs, for which QM is very promising. In addition, we present available IIoT-centric datasets and encourage researchers in the IIoT niche to validate the models using the industrial-featured datasets for better accuracy, prediction, and decision-making. In addition, we show how hybrid quantum-classical learning could leverage optimal IIoTsec when deployed. We conclude with the possible limitations, challenges, and prospects of the deployment.

Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in vehicular ad hoc networks (VANETs)

This research tackles the critical challenge of BeiDou signal spoofing in vehicular ad-hoc networks and addresses significant risks to vehicular safety and traffic management stemming from increased reliance on accurate satellite navigation. The study proposes a novel hybrid machine learning framework that integrates Autoencoders and long short-term memory (LSTM) networks with an advanced cryptographic method, attribute-based encryption, to enhance the detection and mitigation of spoofing attacks. Our methodology leverages both real-time and synthetic navigational data in a comprehensive experimental setup that simulates various spoofing scenarios to test the resilience of the proposed system. The findings demonstrate a significant improvement in the accuracy of spoofing detection and the robustness of mitigation strategies by ensuring the integrity and reliability of navigational data. This investigation enhances the existing body of knowledge by demonstrating the effectiveness of integrating machine learning with cryptographic techniques to secure VANETs. Ultimately, it effectively paves the way for future research into adaptive security mechanisms that can dynamically respond to evolving cyber threats.

S&SEM: A Secure and Speed-Up Election Mechanism for PoS-Based Blockchain Network

To be a stakeholder/validator/token holder is not so difficult in the Proof of Stake (POS)-based blockchain networks; that is why the number of validators is large in these networks. These validators play an essential part in the block creation process in the PoS-based blockchain network. Due to the large validators, the block creation time and communication message broadcasting overhead get increased in the network. Many consensus algorithms use different techniques to reduce the number of validators, such as Delegated Proof of Stake (DPoS) consensus algorithms, which select the set of delegators via stake transactions for the block creation process. In this paper, we propose S&SEM, a secure and speed-up election process to select the ‘z’ number of validators/delegators. The presented election process is based on a traditional voting style with multiple numbers of rounds. The presented election mechanism reduces the possibility of malicious activity in the voting process by introducing a special vote message and a round that checks duplicate votes. We did horizontal scaling in the network to speed up the election process. We designed an improved incentive mechanism for the fairness of the election process. The designed reward and penalty procedure controls the nodes’ behaviors in the network. We simulate the S&SEM, and the result shows that the presented election process is faster and more secure to select delegators than the existing process used by DPOS.

Quantum-Resilient Self-Evolving Blockchains: AI- Driven Consensus and Autonomous Security Upgrades

The fragility of conventional blockchain systems is becoming more apparent in face of the relentless march forward in quantum computing. In this paper, we suggest the optimal architecture for quantum-resistant blockchains deploying AI regarding consensus self- evolving security protocols and mechanisms. An intuitive example of this integration is the use of machine learning algorithms for a self-adapting security system, which automatically adjusts the protection on its infrastructure capabilities to evolving quantum threats. In this paper, we address the consequences of quantum computing in blockchain security introduce a framework for AI-assisted consensus (Section 3) and cover the feasibility of self- evolving blockchains that guarantee their reliability as well as integrity in a post-quantum era.

Enhancing Cybersecurity through AI-Powered Security Mechanisms

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.

StarMalloc: Verifying a Modern, Hardened Memory Allocator

We present StarMalloc, a verified, efficient, security-oriented, and concurrent memory allocator. Using the Steel separation logic framework, we show how to specify and verify a multitude of low-level patterns and delicate security mechanisms, by relying on a combination of dependent types, SMT, and modular abstractions to enable efficient verification. We produce a verified artifact, in C, that implements the entire API surface of an allocator, and as such works as a drop-in replacement for real-world projects, notably the Firefox browser. As part of StarMalloc, we develop several generic datastructures and proof libraries directly reusable in future systems verification projects. We also extend the Steel toolchain to express several low-level idioms that were previously missing. Finally, we show that StarMalloc exhibits competitive performance by evaluating it against 10 state-of-the-art memory allocators, and against a variety of real-world projects, such as Redis, the Lean compiler, and the Z3 SMT solver.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Fortifying the Cloud: Navigating Data Security Challenges and Pioneering Future-Ready Solutions

Cloud computing has transformed data storage, management, and processing, offering scalability, flexibility, and cost-efficiency. However, it presents significant security challenges. This paper examines critical data security elements in cloud computing, with a focus on maintaining confidentiality, integrity, and availability. Current security mechanisms, including encryption, identity management, firewalls, and intrusion detection, are reviewed alongside their limitations in multi-tenant environments, insecure APIs, and insider threats. Network challenges are addressed, particularly the integration of IoT, edge, and fog computing for improved security and efficiency. Emerging trends such as zero trust architecture, AI-driven security, quantum-safe encryption, and blockchain solutions are highlighted as pivotal developments. The paper underscores the need for proactive, adaptive security strategies to protect sensitive data amid evolving threats and regulatory complexities in cloud security.

Building a Fingerprint Recognition Structure as a Security Mechanism to Verify the Identities of Users in Cloud Environment through Resnet-18

Objectives: To authenticate users employing fingerprint recognition thereby strengthening a security mechanism in a cloud environment. Methods: This study presents a novel approach for fingerprint detection in cloud systems by combining deep learning with metaheuristic optimization. A novel approach based on ResNet-18 for feature extraction and Thermal Exchange Optimization (TEO) classification is presented. This method aims to reduce data duplicity and provide safe access rights. When tested on 80% of the NIST and BDAL datasets, the proposed ResNet-18-TEO method outperforms existing models concerning recognition rates of 95.3% and 94.3% respectively. Findings: The experimental results reveal a False Acceptance Rate (FAR) spanning 0.6% to 4.8% and a False Rejection Rate (FRR) spanning 1.1% to 5.3%. Novelty: The proposed method would help cloud-based security since its fingerprint recognition time is substantially less than 25 seconds in training and 15 seconds in testing that of existing processes. In future work, this research will improvise using several deep learning techniques which are adopted using real time or research dataset. Keywords: Fingerprint recognition, Cloud, Security, Authentication, Privacy