Security Mechanisms Research Articles

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.

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

Opportunities and Challenges of Intelligent Computer Network Configuration and Security with its Technologies

This paper explores the dual aspects of opportunities and challenges posed by intelligent computer network configuration and security, highlighting the role of emerging technologies while emphasizing the need for robust strategies to address the accompanying risks. Intelligent network configuration utilizes technologies like artificial intelligence (AI), machine learning (ML) and software-defined networking (SDN) to automate and optimize network management. This automation enhances efficiency, scalability and adaptability, allowing networks to respond dynamically to traffic patterns and emerging threats. AI-powered security mechanisms further bolster defenses against sophisticated cyberattacks by enabling real-time monitoring, threat detection and proactive mitigation strategies. However, alongside these opportunities come challenges. Implementing intelligent technologies requires addressing issues like system complexity, interoperability and the need for continuous updates to handle evolving cyber threats. Additionally, privacy concerns, the integration of legacy systems and ensuring compliance with regulatory standards are key hurdles in adopting these technologies

Transformation of international security mechanisms in the context of the russian-ukrainian war

The article provides a comprehensive analysis of the transformations in international security mechanisms in the context of the Russia-Ukraine war, ongoing since 2014, with a particular focus on the activities of NATO and the United Nations. The author aims to examine the crisis in the effectiveness of traditional security structures, assess their ability to adapt to new geopolitical realities, and identify the need for the creation of new forms of international security. The research methodology is based on the analysis and synthesis of scientific sources, including works dedicated to the activities of international security organizations, as well as the application of a systematic approach to evaluate the effectiveness of existing security mechanisms. The article examines NATO and the UN's decision-making mechanisms and responses to the Russia-Ukraine war, analyzing their capacity to adequately address the new challenges arising from the armed conflict. The author employs comparative analysis methods to study the changes in the activities of these organizations and analyzes expert assessments to identify key changes in their operations. The study concludes that the Russia-Ukraine war has acted as a catalyst for changes in international security mechanisms, revealing the weaknesses and shortcomings of traditional approaches to global stability. This underscores the need to revise existing strategies, seek innovative approaches to ensuring international security, and potentially create new organizations or coalitions capable of responding more effectively to similar conflicts in the future. The findings of this research may be useful for policymakers, international security experts, and all those interested in the transformation of the global security system.

An intelligent native network slicing security architecture empowered by federated learning

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated-learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately 95.60% in the network slicing architecture and 99.99% for the deployed slice – intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.

Onion Services in the Wild: A Study of Deanonymization Attacks

Tor, the leading anonymization network, routes traffic over multiple relays to ensure client anonymity. Its Onion Services allow users to host services within the Tor network without revealing their location. While these techniques are crucial for users in authoritarian regimes and whistleblowers, they are also exploited by criminals. This paper diverges from the common focus on the technical vulnerabilities of the Tor protocol and instead explores the practical aspects of deanonymizing Onion Service users and operators. Despite Tor's robust security mechanisms, human errors in its usage and operation frequently lead to deanonymization. This study models law enforcement agencies as powerful attackers and evaluates documents from 136 court cases to determine investigative methods. We find that investigators employ different methods depending on the offense, with user mistakes being the dominant angle. Technical attacks, though comparatively rare, are highly effective and can potentially impact a large number of users simultaneously. Attacks on the well-researched Tor protocol are exceptionally rare, but their impact is even more significant. We argue that the human aspect of using Tor is the most critical deanonymization angle and that tailored guidelines for ethical users can help protect them from oppressive retaliation while still enabling the prosecution of criminal activity.

Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.

Nuancing Maritime Security Governance in The Straits of Malacca: A Comparative Analysis on Indonesia, Malaysia, and Singapore

Indonesia, Malaysia, and Singapore are littoral states of the Straits of Malacca (SOM), the world’s busiest waterway connecting the Indian and Pacific oceans, with around 80,000 ships passing through this route annually. While maritime security governance of this waterway is a serious concern especially to those states, their responses to it has differed particularly in regard to three security mechanisms: the Proliferation Security Initiative (PSI), the Malacca Straits Patrol (MSP), and the Regional Cooperation Agreement on Combating Piracy and Armed Robbery against Ships in Asia (ReCAAP). The states’ approaches can be viewed primarily through the lens of sovereign interests, economic interests, and legal obligations. In addition, the role of institutions appears as an intervening factor influencing their responses. This article first analyses maritime security governance and examines why it has garnered much attention in contemporary international relations. Second, it assesses the importance of the SOM as a strategic location, a critical trading route since ancient times, as well as the value it holds for international shipping. Third, it examines the littoral states’ responses to maritime security governance, focusing on the factors influencing them. The article finds that their responses to external power security initiatives differ compared to the indigenous security mechanism in the SOM. Despite that, the underlying theme among the three states is to uphold their obligations as coastal states as provided under UNCLOS and in cooperating with each other and international users, though several caveats do seem to also apply.

Novel approaches in Paediatric Drug Delivery System

The development of medication delivery technology has led to an increase in the popularity of pediatric formulations. However, there is absence of secure and efficient drug delivery mechanism for pediatric patients. It is crucial to design and create a novel and suitable delivery system to guarantee precise and reliable medication administration to pediatric patients, as their pharmacokinetics and pathophysiologic profiles differ from those of adults. Under this heading, discussion will be carried out in order to increase stability; new medication delivery techniques and technology are being developed to enhance the stability, toxicity, flexibility, moreover secure aspects of pediatric oral formulations. Specifically, issues with patient acceptability and manufacturability are discussed in this section, and innovative medication delivery methods are examined to address current issues.

Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management

In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.

Enhancing MQTT-SN Security with a Lightweight PUF-Based Authentication and Encrypted Channel Establishment Scheme

The communication of Industrial Internet of Things (IIoT) devices faces important security and privacy challenges. With the rapid increase in the number of devices, it is difficult for traditional security mechanisms to balance performance and security. Although schemes based on encryption and authentication exist, there are still difficulties in achieving lightweight security. In this paper, an authentication and key exchange scheme combining hardware security features and modern encryption technology is proposed for the MQTT-SN protocol, which is not considered security. The scheme uses Physical Unclonable Functions (PUFs) to generate unpredictable responses, and combines random numbers, time stamps, and shared keys to achieve two-way authentication and secure communication between devices and broker, effectively preventing network threats such as replay and man-in-the-middle attacks. Through verification, the proposed scheme has proved effective in terms of security and robustness, has computational and communication cost advantages compared with recent schemes, and provides higher availability.