Enhancing cybersecurity by generating user-specific security policy through the formal modeling of user behavior

Cyber risks on IoT platforms and zero trust solutions

Digital care transformation, the proliferation of disruptive technologies and the changing hybrid workforce have forced the evolution of traditional information technology network boundaries of healthcare organizations. The new landscape has rendered legacy existing perimeter defined and based cybersecurity solutions inadequate to meet increasing regulatory and federal demands for highly secure access management. Emerging compliance requirements, coupled with the concerning increase in healthcare data breaches, ransomware attacks, and security incidents targeting the healthcare sector, have transformed our historic notion of trust into an organizational vulnerability. A “Zero Trust” approach to information security is driven by an imperative to “never trust, always verify,” and requires strict, rigorous and continuous identity verification to minimize trust zones and their associated risk of security breach. Healthcare delivery organizations need to appreciate the importance of a Zero Trust strategy in reducing vulnerabilities, strengthening health system information security, and preventing successful security breaches, while also recognizing how identity and access management serves as the foundation of achieving Zero Trust.

This article explores opportunities of implementing Zero Trust (ZT) model into organizations with Perimeter-Based security system. It describes changes that occurred in recent years and had an impact on organizations’ corporate security systems. The reasons of the declining relevance of Perimeter-Based model and its shortcomings have been explained. Explained why organizations increasingly adopt Zero Trust in their information security systems and which problems of Perimeter-Based model it solves. Outlined problems that organizations usually encounter during ZT implementation. Presented brief overview of Zero Trust model. This includes a description of its core principles, experts’ perspectives on the model, and considerations regarding its implementation. The specific features of implementing Zero Trust within organizations’ existing security systems have been examined. It has been established that the effective implementation of Zero Trust requires a comprehensive, multi-step approach. The initial steps involve auditing the current state of an organization’s information security and assessing the risks associated with its assets. These preparatory measures aim to define the scope of implementation and identify the organization’s priority needs regarding ZT. The next step is defining and implementation of security policies. It has been established that the main part of implementation process is direct deployment of functionalities that enforce principles of Zero Trust. It begins with developing implementation plan, specifying which security measures will be applied to particular system components and in what sequence. To measure completeness of Zero Trust, an example of “maturity model” has been provided. Additionally, the functionalities whose implementation enforces the principles of Zero Trust, as well as the relationships between them, were presented. This also includes a description of network microsegmentation, the implementation of MFA technologies and IAM (Identity and Access Management) systems, the use of UEM and UDR solutions for device control, as well as SIEM for threat detection and SOAR for automating and coordinating the security system. As a conclusion of the implementation process, the necessity for continuous review and improvement of the security system has been substantiated. Practical examples of Zero Trust implementation have also been examined, demonstrating the effectiveness and relevance of transitioning to a Zero Trust architecture. The results may serve as practical demonstration of capabilities and benefits of transitioning to a ZT security architecture, to provide an understanding of the challenges encountered during its implementation, and define effective and cost-efficient approach to carry out this process.

As the number of people working remotely increases, it is insufficient for organizations to protect the boundaries of their digital networks. To protect these boundaries, organizations need adaptive solutions that perform full authentication, authenticate every access request, and quickly detect and respond to both on- and off-network threats. Within this motivation, zero trust is a next generation security strategy based on the principle of “never trust, always verify”. In this study, the basic principles applied from the transition processes to zero trust architecture are evaluated and the advantages of this architecture to the security scope are examined. At the same time, the challenges that organizations that want to implement zero trust architecture will face in this transition are evaluated. The transition to zero trust architecture requires cumulative serious changes in the IT infrastructure of organizations. Zero trust architecture aims to build a system in which all information assets, users and data flow are constantly labeled as untrustworthy and therefore need to be constantly verified. The successful implementation of the zero-trust approach in organizational structures provides advantages such as dynamic authentication, increased endpoint security and strict control over data flows. However, it is also seen that challenges such as network identity management and data monitoring arise during the transition and implementation of zero trust architecture.

BFLS: Blockchain and Federated Learning for sharing threat detection models as Cyber Threat Intelligence

The rapid adoption of cloud computing has witnessed unprecedented enhancements in data storage capacities, scalability, and operation flexibility for diverse organizations. Yet, the transition from legacy on-premises to cloud-based environments has raised grave concerns about information safety and compliance with regulations in evolving regulatory environments. This research will discuss the most significant challenges and innovative solutions for the deployment of data security mechanisms in cloud environments, with special reference to regulatory compliance from 2015 to 2024. Though cloud vendors have taken major leaps in enhancing the security of cloud centers, some of the longstanding issues like threats to multi-tenancy, poor encryption methods, vendor lock-in situations, and jurisdictional issues continue to create significant problems. In addition, with the regulatory environment in flux, organizations are also facing challenges in terms of compliance with regulations like GDPR, HIPAA, and CCPA in the context of cloud computing. This research tries to determine the gaps in existing literature regarding the integration of sophisticated technologies, including artificial intelligence (AI), blockchain, and machine learning (ML), into cloud security frameworks. In addition, it addresses the organizational challenges in ensuring continuous compliance and adequate data protection, especially in hybrid and multi-cloud settings. The research further explores the limitations of conventional security controls, emphasizing the requirement for automated compliance solutions, strong encryption techniques, and new security models such as Zero Trust and federated identity management.

With the fast growth of mobile networks and the daily use of smartphones for sharing personal and business information, keeping data safe is more important than ever. Traditional security methods, like passwords and basic encryption, are no longer strong enough to stop advanced cyber-attacks. Deep learning, a powerful part of artificial intelligence (AI), provides new and smart ways to improve mobile data security. This study looks at how deep learning can protect data in mobile networks by automatically detecting unusual behavior, viruses, and hacking attempts. Models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN) can understand traffic patterns and learn to find threats without needing constant human monitoring. These techniques also aid with safe user recognition, aberrant behavior identification, and encryption improvement of data. We evaluate deep learning against more conventional security solutions and discover that it is more accurate and adept at changing with new kinds of assaults. Still under development, however, are issues include maintaining user privacy, controlling system speed, and knowing how the models decide. Our results generally indicate that deep learning may provide a clever and powerful solution to safeguard data in mobile networks.

Cyberphysical systems connect physical devices and large private network environments in modern communication systems. A fundamental worry in the establishment of large private networks is mitigating the danger of transactional data privacy breaches caused by adversaries using a variety of exploitation techniques. This study presents a privacy-preserving architecture for ensuring the privacy and security of transaction data in large private networks. The proposed model employs digital certificates, RSA-based public key infrastructure, and the blockchain to address user transactional data privacy concerns. The model also guarantees that data in transit remains secure and unaltered and that its provenance remains authentic and secure during node-to-node interactions within a large private network. The proposed model has increased the encryption speed by about 17 times, while the decryption process is expedited by 4 times. Therefore, the average overall acceleration obtained was 16.5. Both the findings of the security analysis and the performance analysis demonstrate that the proposed model can safeguard transactional data during communications on large private networks more effectively and securely than the existing solutions.

Application of network data security based on wireless sensor technology in the development of digital sharing economy

With the rapid development of information technology and the widespread application of computer networks, data security issues have become increasingly prominent, especially in key industries such as finance and medical care, where the protection of sensitive data has become a top priority. In order to meet these challenges, this article proposes a data security protection system based on deep learning (DL) technology, which can monitor and identify potential security threats in real time and greatly enhance data security by automatically analyzing and learning network traffic, user behavior, and intrusion characteristics. The study uses a deep belief network to complete high-intensity protection of personal or corporate private data while automatically countering and reminding some network intrusion behaviors. The performance differences in data security of individuals or enterprises before and after the use of the protection system were compared through experiments. The results show that the new system can improve data security performance by about 54.6% in all aspects. The results show that the new security protection model based on DL technology not only improves detection accuracy and response speed but also effectively resists emerging threats, providing a more solid protection barrier for network data.

This study proposes an privacy-protection method for blockchain transactions based on lightweight homomorphic encryption, aiming to ensure the security of transaction data and user privacy, and improve transaction efficiency. We have built a blockchain infrastructure and, based on its structural characteristics, adopted zero-knowledge proof technology to verify the legitimacy of data, ensuring the authenticity and accuracy of transactions from the application end to the smart-contract end. On this basis, the Paillier algorithm is used for key generation, encryption, and decryption, and intelligent protection of blockchain transaction privacy is achieved through a secondary encryption mechanism. The experimental results show that this method performs well in privacy and security protection, with a data leakage probability as low as 2.8%, and can effectively defend against replay attacks and forged-transaction attacks. The degree of confusion remains above 0.9, with small fluctuations and short running time under different key lengths and moderate CPU usage, achieving lightweight homomorphic encryption. This not only ensures the security and privacy of transaction data in blockchain networks, but also reduces computational complexity and resource consumption, better adapting to the high-concurrency and low-latency characteristics of blockchain networks, thereby ensuring the efficiency and real-time performance of transactions.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Contemporary research is geared towards integrating multidisciplinary domains and targeted to developing robust solutions. This implies that single-discipline solutions most times do not give the desired results. This study intends to apply this concept statement in mitigating the hydra-headed challenge facing the world today called sharp-practice (corruption) which some believe that it is worse than the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The sharp practice is a major challenge in the world today and a threat to the development of any country. Sharp practices range from higher offices of the state institutions to the small offices located in remote and rural local areas. Non-profit Organizations (NPOs) are also affected by sharp practices as the office-bearers of these institutions are constantly misappropriating donors’ funds meant for these facilities for their selfish interests. State and private donors offer a large portion of their resources to NPOs to improve the standard of living of needy people in remote and rural areas. However, most NPOs sited in rural areas do not have systems that can be used to monitor and capture the management of resources. In this paper, we attempt to solve this challenge by proposing the integrating Smartphone Network Architecture (SMA) and Data Security Techniques (DST) to mitigate sharp practices in NPOs. In our approach, a smartphone-mobile app algorithm was developed using JavaScript. The app uses the Least Significant Bits (LSB) method to secretly embed a Date Stamp on a PNG image whenever an event is captured by a secret camera at the NPO’s center. The results showed that the LSB method is the most suitable technique to embed a small-scale secret data on the pixels of a PNG image file. The LSB technique does not require the original cover image for decoding the secret data hidden in the image over the network. From the study, these techniques have shown to improve accountability and alleviate corrupt practices in NPOs.

A lightweight data security model is of much importance in view of security and privacy of data in several networks (e.g., fog networks) where available computing units at edge nodes are often constrained with low computing capacity and limited storage/availability of energy. To facilitate lightweight data security at such constrained scenarios, cellular automata (CA)-based lightweight data security model is presented in this chapter to enable low-cost physical implementation. For this reason, a detailed investigation is presented in this chapter to explore the potential capabilities of CA-based scheme towards the design of lightweight data security model. Further, a comparison among several existing lightweight data security models ensure the effectiveness for proposed CA-based lightweight data security model. Thus, application suitability in view of fog networks is explored for the proposed CA-based model which has further potential for easy training of a reservoir of computers towards uses in IoT (internet of things)-based multiple industry applications.

Mobile Ad Hoc Networks (MANETs) have become a likely candidate for a wide range of threats. Each and every routing protocol in MANETs should preserve anonymity of mobile devices and data, along with unlinkability of data packets. With an aim of improving routing efficiency, many routing protocols either partially sacrificed anonymity or disregarded unlinkability. Here, we propose a multipath routing protocol that will enhance security as well as efficiency of routing in MANETs. Anonymity and unlinkability are completely preserved through the concepts of keyed hash chain and per-hop data packet appearance alteration. Also, the routing efficiency is enhanced through the formulation of a route selection parameter called Link Stability Metric (LSM) and the deployment of an elaborate route maintenance phase. The proposed system has been simulated and tested, and the results indicate that security as well as routing efficiency is enhanced considerably.