Security Solutions Research Articles

Modelling Decision and Reaction Against Detected Intrusions in IoT System Basing on Security Policy Rules

In recent years, The Internet of Things (IoT) is considered as one of the main technological revolutions, it connects heterogeneous devices, peoples, and services in order to exchange information and to improve existing deployments in different sectors. So far, existing security solutions are not adapted to this development, considering that IoT resources are exposed to different intrusions, which impact security management efficiency and the need for human interventions to increase. Although, it is too hard to depend on manual approaches that require the deep involvement of security managers to deliver the aimed security level. Therefore, a new solution is needed, that will facilitate the decision against detected intrusions and according to their magnitudes and their intentions to put the necessary reaction in the right place. In this context, this paper proposes a model which specify how the decision and correlation scenario will be carried out when a critical alert comes from the intrusion detector basing on security policy rules. This model simplifies and facilitates the decision and reaction against detected intrusions by enriching it with the rules defined in security policy to ensure the protection of IoT resources and help security administrators to make the right decisions in other tasks.

Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

DATA SECURITY IN IOT DEVICES AND SENSOR NETWORKS FOR ROBUST THREAT DETECTION AND PRIVACY PROTECTION

The rapid proliferation of Internet of Things (IoT) devices and sensor networks has revolutionized various industries by enhancing automation, connectivity, and operational efficiency. However, these advancements have also introduced significant security challenges due to the resource constraints and decentralized nature of IoT environments. This paper provides a systematic review of IoT security solutions, focusing on encryption techniques, authentication protocols, and machine learning-based anomaly detection methods. A total of 55 peer-reviewed articles were analyzed following the PRISMA guidelines. The findings reveal that while lightweight cryptographic algorithms, such as elliptic curve cryptography (ECC), offer robust security with low energy consumption, scalability across large IoT networks remains a challenge. Blockchain-based authentication has emerged as a promising decentralized solution, but issues related to energy consumption and latency hinder its widespread adoption. Machine learning techniques have shown high accuracy in detecting threats in real-time, but their resource-intensive nature limits their application in low-power IoT devices. This review underscores the need for multi-layered, integrated security frameworks and highlights gaps in research on quantum-resistant cryptography and interoperable security standards. Future research must focus on developing scalable, energy-efficient security solutions to ensure data integrity and privacy in expanding IoT ecosystems.

The Planning Process of “HOMERESC” Home Service System

This paper presents the planning process of the HOMERESC mobile application, the home service system. In contemporary times, locating companies or individuals providing home repair services has become increasingly challenging. Home repairing services in Malaysia encompass a range of maintenance and renovation tasks to enhance the condition and functionality of residential properties. Some examples include the Taman Seri Berembang flat building, which was formerly in critical condition. A 22-year-old complex of flats with 30 homes was discovered to be damaged and unsafe to live in. Therefore, a mobile application named "HOMERESC" Home Service System was developed to address all the issues and ensure the welfare of users. The name ‘HOMERESC’ was chosen to represent this application's ability to "rescue" all damaged homes. An Agile methodology has been used to build this system, ensuring the project’s goals are met and the development process goes smoothly. The implication of the research suggests that the mobile application offers a secure and viable solution for home repair services. In conclusion, the research demonstrates that the HOMERESC mobile application could enhance the safety and welfare of residential property owners.

Decentralized Integrity: Blockchain-Based Frameworks for Enhancing Software Update Security

This article examines the application of blockchain technology as a solution for secure and transparent software update distribution. As cyber threats evolve, traditional centralized update mechanisms face increasing vulnerabilities to tampering and unauthorized modifications. We propose a blockchain-based framework that leverages distributed ledger technology to create an immutable, decentralized environment for software updates. Our article demonstrates how this approach can eliminate single points of failure, ensure update integrity through cryptographic signing and network-wide verification, and enhance transparency in the update process. We discuss the system architecture, including update creation, distribution, and verification mechanisms, and evaluate its integration with existing software ecosystems. While acknowledging challenges such as scalability and implementation costs, we argue that blockchain-based solutions significantly advance securing software distribution. Our findings suggest that this approach has the potential to become a standard practice in the industry, substantially improving trust and reliability in software update systems. This article contributes to the growing body of research on blockchain applications in cybersecurity and provides insights for future developments in secure software distribution methods.

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.

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Harnessing the Power of Secure and Scalable Generative AI : A Deep Dive into AWS and SAP's Cutting-Edge Collaboration

This article explores the groundbreaking collaboration between Amazon Web Services (AWS) and SAP in developing secure and scalable generative AI solutions for businesses. It examines the critical need for robust AI implementations in the face of growing security and scalability challenges, highlighting the unique strengths that AWS and SAP bring to their partnership. The article delves into real-world applications across industries, discusses key benefits for businesses including operational efficiency and enhanced decision-making, and outlines implementation strategies and best practices. Finally, it looks ahead to emerging trends and the long-term vision for AI in business, emphasizing how the AWS-SAP collaboration is poised to shape the future of enterprise AI.

Designing cost-effective data solutions: Best practices from multi-million dollar projects involving cross-functional teams and diverse data sources

This paper explores the design and implementation of cost-effective data solutions, focusing on multi-million dollar projects that involve cross-functional teams and diverse data sources. The study aims to identify best practices and technological advancements that enhance data management efficiency, ensuring scalability, security, and compliance. Employing a comprehensive review of existing literature and case studies, the paper examines the challenges and opportunities in integrating technologies such as AI, machine learning, cloud computing, and blockchain. Key findings highlight the importance of cross-functional collaboration in addressing data management complexities and the adoption of agile methodologies to enhance adaptability and efficiency. The study also underscores the significance of advanced data integration techniques and the application of circular economy principles to promote sustainability in data management. The conclusion provides actionable recommendations for organizations seeking to optimize their data solutions, emphasizing the need for strategic investment in emerging technologies and a robust data governance framework. By offering insights into future trends and research directions, this paper contributes to the ongoing discourse on developing cost-effective, secure, and sustainable data solutions in an increasingly digitalized world. Keywords: Cost-Effective Data Solutions, Cross-Functional Teams, Data Integration, Cybersecurity, Artificial Intelligence, Circular Economy Principles

Integrating Deep Learning and Homomorphic Encryption for Secure Image Transmission

This paper introduces a novel approach to securing medical image transmission through the integration of deep learning techniques into cryptographic processes. Leveraging the capabilities of Backpropagation (BP), Convolutional Neural Networks (CNN), Residual Networks (ResNet), and Generative Adversarial Network (GAN), our method aims to enhance the privacy and security of medical images in real-time applications like telemedicine. The proposed system focuses on optimizing performance metrics including Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), Mean Average Precision (MAP), and encryption speed. Through experimental evaluation, our approach demonstrates promising results in terms of encryption efficiency and preservation of image quality. By addressing the critical need for secure transmission methods in healthcare, this research contributes to advancing the field of medical image cryptography and lays the groundwork for further exploration in deep learning-based security solutions for healthcare data.

Enhancing consumer trust in financial services: the role of technological security innovations

The financial services industry increasingly relies on technological advancements to maintain and enhance consumer trust. Consumer trust is a critical component influencing customer loyalty, financial inclusion, and overall economic stability. This paper explores the impact of technological security innovations on consumer trust in financial services. Fundamental innovations such as blockchain, advanced encryption, biometric authentication, artificial intelligence, and cloud security play significant roles in addressing security challenges. Blockchain technology provides a decentralized, tamper-proof method for recording transactions, enhancing transparency and reducing fraud risks. Advanced encryption techniques ensure the confidentiality and integrity of financial data, which is critical for protecting sensitive information. Biometric authentication methods offer secure and user-friendly solutions for identity verification, reducing reliance on traditional passwords and enhancing security. Artificial intelligence and machine learning improve fraud detection capabilities by providing real-time monitoring and response. Cloud security solutions offer scalable protection for financial data, ensuring compliance with regulatory standards. Through empirical analysis and case studies, this study demonstrates that these technologies significantly enhance consumer confidence by providing greater transparency, security, and reliability in financial transactions. The findings offer valuable insights for policymakers, financial institutions, and technology developers aiming to foster a more resilient and trust-based financial ecosystem. This research contributes to the existing literature by providing evidence of the direct impact of technological security innovations on consumer trust. Keywords: Consumer Trust, Financial Services, Technological Innovations, Blockchain, Encryption, Biometric Authentication, Artificial Intelligence, Cloud Security.

A Comprehensive Analysis of Maritime Cyber Security Incidents: Trends, Impacts, and Countermeasures

The maritime industry is currently experiencing a process of digital transformation, which involves a significant level of automation and enhanced communication with external networks. As a result, various facilities in the maritime sector such as commercial and navy vessels, shipping companies, ports, and shipbuilders, are becoming more susceptible to cyber threats. In addition to the potential economic and reputational harm to shipping companies, a cyber-attack on maritime systems could result in significant incidents such as the release of hazardous substances, collisions, grounding, and fires. This poses significant risks to both ship crew, ship, cargo, and environment. This study examines cyber security events in the maritime sector. The main objective is to cultivate a thorough comprehension of cyber-attacks that specifically target systems in maritime facilities, by analyzing insights derived from incidents in the Maritime Cyber-Attack Database. The work involves the construction and examination of a number of cyber security incidents. An inquiry is carried out to determine the time patterns, geographical spread, sector-specific consequences, and attributes of these cyber-attacks, including the identity of the perpetrator, intention (whether deliberate or unintentional), and the affected systems inside the maritime domain. The paper examines particular instances to identify the main stages of a cyber-attack on maritime facilities' systems, the fundamental strategies employed by attackers, and proposes standard cyber security solutions to reduce these risks. The study's contribution entails the methodical delineation of the cyber security terrain that is unique to the maritime industry.

Beyond Clouds: Locally Runnable LLMs as a Secure Solution for AI Applications

Beyond Clouds: Locally Runnable LLMs as a Secure Solution for AI Applications

Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications.

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Design and implementation of a decentralized document management system

Digital document management systems (DMSs) can enhance organizational efficiency, security, and compliance, fostering collaboration and reducing operational costs, and are widely used for convenience and productivity. However, DMSs are vulnerable to security issues, such as unauthorized access, document forgery, and malicious insider activities. Traditional DMSs grant access permissions to individual users, which increases the risk of document leakage or manipulation when these permissions are misused. This article aims to design and implement a decentralized document management system (DDMS) that enhances the security and integrity of digital documents by decentralizing access permissions and using blockchain technology. The proposed DDMS encrypts digital documents using a symmetric key and then the key is divided and distributed among multiple users through Shamir’s secret sharing scheme. The encrypted documents are stored using the InterPlanetary file system (IPFS), ensuring integrity through content-based addressing. And blockchain-based smart contracts manage access permissions and document retrieval, ensuring access is only granted when a predefined number of participants agree. We implement our system using the Ethereum blockchain and IPFS and evaluated its performance against other types of DMSs. The experimental results demonstrate that the proposed DDMS significantly enhances security and integrity compared to three types of DMSs. While the system introduces a reasonable performance overhead, particularly with larger documents, it effectively prevents unauthorized access and tampering. For instance, the proposed system incurs approximately 12 s (1.2%) overhead when handling an 8GB document compared to the P2 system, but ensures tamper-proof access control and document integrity. As a result, the proposed DDMS addresses the critical security vulnerabilities found in conventional DMSs by decentralizing document access and leveraging blockchain and decentralized storage. This approach offers a robust solution for secure, tamper-resistant document management, making it suitable for organizations requiring high levels of confidentiality and document integrity.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.