Infrastructure as Code (IaC) for Advanced Security Automation

Cloud computing has become an essential driver of digital transformation, enabling businesses to op- timize IT infrastructure, e-commerce operations, artificial intelligence (AI) applications, and Internet of Things (IoT) networks [1, 2]. Service models such as Infrastructure-as-a-Service (IaaS), Platform- as-a-Service (PaaS), and Software-as-a-Service (SaaS) offer scalability, cost efficiency, and operational flexibility [3, 4]. However, the transition from traditional data centers to cloud environments has introduced security vulnerabilities that conventional perimeter-based defenses fail to address [5, 6]. The increasing sophistication of cyber threats, including data breaches, unauthorized access, and ran- somware attacks, necessitates the adoption of advanced security strategies [7, 8]. This paper explores AI-driven security automation, blockchain-based identity management, and zero-trust security archi- tectures as key solutions for mitigating modern cloud security risks [9, 10]. Additionally, this study identifies existing research gaps, such as real-time adaptability, interoperability challenges, scalability concerns, and quantum computing risks [11, 12]. Future research directions in- clude quantum-resistant encryption, AI-enhanced security automation, and AI-blockchain convergence for self-adaptive cloud security solutions [13, 14]. By addressing these areas, this research aims to contribute to the development of next-generation cloud security frameworks that enhance cyber threat mitigation, regulatory compliance, and secure cloud integration [15, 16].

Named Data Networking, by design, provides built-in security features to build a secured network. In NDN, every Data packet is signed at the time of production by the producer. Therefore, a data consumer can verify its legitimacy by checking the signature. However, there need to be rules about which key is authorized to sign which Data packet(s), and a trust schema defines this relationship. However, for any security tool to be used effectively, it should be user-friendly. Therefore, to ensure the proper use of trust schema, it should be easy to define and, most importantly, automated. Recently proposed Versec—a declarative language to express and restrict structural relationships between data and key names and complementary binary representation of the “compiled” schema — is a promising next step towards automation and user-friendliness of the NDN security. Versec is designed to ensure versatile security, but the primary design choices focus on local environments. However, we want to use Versec in a universal environment. Therefore, we perform a systematic analysis to decide whether the current binary encoding can achieve the versatility we desire in the present form or not. Specifically, the paper's focus is on the overall design aspect of Versec, the structure of the binary output format, and implementation details for the binary encoding. The paper also suggests two potential simplified variants of Versec binary encoding that, while providing a compact representation can overcome the limitations we identified to achieve more versatility.

The aim of this study was to understand how voters’ perceptions of voting system security might be impacted by the addition of actual security features and fake security features (i.e., security theater) to a voting method. In the context of voting, positive perceptions of security might be more important than security itself because voters must feel voting systems are safe enough to keep their votes secure and anonymous, or else they might choose to avoid participating in an election—potentially impacting election outcomes. To investigate this problem, 90 Rice University undergraduate students participated in a mock election using one of three systems, each associated with differing levels of security: a standard paper ballot, a paper ballot enhanced by end-to-end (e2e) security mechanisms, and a paper ballot that included fake security mechanisms to give the feeling of enhanced security without actually making the system safer. After voting, each participant completed a survey that included questions about voting system security to understand if one system was perceived to be more secure than another. In this study, participants rated the paper voting method with advanced e2e security to be the most secure; both the traditional paper ballot and security theater ballot were found to be the least secure. In summary, the advanced security features positively impacted voters’ perceptions of system security—indicating that people accurately assessed the safety of the provided voting method. This finding is promising and suggests that during real elections, there is the potential for voters to assess accurately a system’s security and then act-on and make decisions based on this information.

Urban defences against terrorism have traditionally been based on territorial interventions that sought to seal off and surveil certain public and private spaces considered targets. Lately, though, a much wider range of crowded and public spaces have been viewed as potential targets and thus have been identified as requiring additional security. This has immense implications for the experience of the ‘everyday’ urban landscape. Drawing on contemporary notions that incorporate the study of aesthetics and emotions within critical security and terrorism studies, this article discusses the visual impact of counter-terrorism security measures. It analyses the ‘transmission’ of symbolic messages, as well as the variety of ways in which security might be ‘received’ by various stakeholders. The analysis takes place against the backdrop of concern that obtrusive security measures have the capacity to radically alter public experiences of space and in some cases lead to (intended and unintended) exclusionary practices or a range of negative emotional responses. The article concludes by outlining a ‘spectrum of visible security’ ranging between traditional obtrusive fortified approaches and approaches that embed security features seamlessly or even ‘invisibly’ into the urban fabric.

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas.

The technology is increasing in leaps and bounds in the present era. With many technical developments and advances taking place, there has been an advancing trend in the techniques of handling money, money transaction and exchange methods. The Automatic Teller Machine (ATM) is the most widely used method to withdraw money and get bank statements. In the era where there are a wide range of banking companies being established, the usage of ATM cards has increased drastically. ATMs of different banks in every locality and almost thousands of people use them to withdraw money every day. As every coin has two sides, no doubt that ATMs has made the withdrawal of money much easier and reliable, yet there are major challenges to this money withdrawal process. Nowadays people have multiple bank accounts and every bank has the ATM card associated with it. In addition to this, for every ATM card there is a unique pin associated with it. Hence, it becomes a cumbersome task to handle so many ATM cards which involve so many passwords and pins. Apart from this disadvantage there is yet another greater risk that is theft of the Atm cards or losing or misplacing them. Though there is Pin associated with an ATM card which serves as security, it is quite possible to crack the PIN of it. Hence, we have come up with the solution, a card which integrates all the bank accounts into a single card which makes withdrawal of money much more easy, feasible and secure. The additional security features such as fingerprint verification and two factor authentication provide protection against identity theft during the money transactions from ATMs. This system with advanced security features would have many features which would solve various disadvantages associated with ATM systems present today. We have used fingerprint sensor, GSM module, RFID to provide two factor authentication for the ATM system.

4 - Aspects of Windows Client Security

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the Industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrates that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

An examination is made of the current approaches to sensor line supervision and their relative effectiveness against the range of easily available circumvention techniques is assessed, compared to encrypted line supervision. Features of high security encrypted line supervision, including encryption techniques, interactive polling, the generation of random data, proprietary algorithms and keys, serial numbers and the processing of such an approach are discussed, including the flexibility offered by miniaturization of the microprocessor controlled circuits and modular system design. How an encryption module can be deployed within an overall high security system is highlighted. Examples of practical applications provide insight into the effective and efficient use of this advanced security technology. >

In recent years, the convergence of data mining and robotic systems has emerged as a key trend shaping the future of artificial intelligence. This study aimed to explore how data mining techniques can enhance robotic decision-making, enabling robots to learn, adapt, and respond to dynamic environments. A qualitative-analytical research design was used to examine the impact of intelligent data processing on robotic behavior, particularly in social robots. The analysis revealed that real-time data mining significantly improves robot autonomy, adaptability, and human interaction. Moreover, the integration of data mining introduced critical security challenges, such as distributed denial of service (DoS) attacks, sensor data eavesdropping, and manipulation of input data. The study found that implementing advanced security mechanisms—including encryption, authentication, and anomaly detection through artificial intelligence algorithms—helps mitigate these risks effectively. The findings emphasize the transition from task-executing machines to intelligent systems capable of independent decision-making. One notable application discussed in the paper is the development of social robots that interact naturally and empathetically with humans. This research highlights the importance of embedding data mining within robotic architectures not only to enhance performance but also to strengthen security and resilience. The study concludes that the intelligent fusion of data mining and robotics paves the way for scalable, secure, and context-aware robotic systems. Future work is needed to explore real-world implementations and evaluate long-term performance under various operational scenarios.

This study examines government agencies' critical security challenges when adopting cloud-based solutions. As governments increasingly migrate their services to the cloud to enhance efficiency and reduce costs, they must contend with significant risks, including data breaches, unauthorized access, and regulatory compliance. To mitigate these threats, the paper explores implementing robust security measures such as advanced encryption methods, multi-factor authentication (MFA), continuous monitoring, and Zero Trust Architecture. The research emphasizes the importance of adhering to international security standards like ISO 27001 and NIST guidelines to ensure data integrity and protect sensitive government information. Additionally, the study highlights the role of AI and machine learning (ML) technologies in enhancing security measures and automating threat detection. By leveraging these advanced security protocols, government agencies can safeguard digital assets while improving service delivery and public trust. The findings of this research suggest that a well-secured cloud infrastructure is essential for the modernization of government services, significantly enhancing operational efficiency, regulatory compliance, and citizen engagement. These insights underscore the necessity of continuous investment in cloud security to support the evolving needs of digital governance.

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

The oil and gas industry, a vital lifeline fueling the global economy, find itself at a pivotal juncture where the convergence of operational technology (OT) and information technology (IT) has ushered in unprecedented opportunities and challenges. As digital transformation sweeps across this sector, the imperative to fortify cybersecurity defenses against ever-evolving threats has become paramount. Innovative and forward-looking oil and gas organizations across the globe are adopting the Cloud in many forms because of their digital transformation initiatives. Data lakes, edge technology, machine-to-machine communication, and machine learning (ML) algorithms have been enabling this industrial digital transformation. This transformation is also driving changes to the OT landscape, and as these environments continue to evolve, OT environments are leveraging well proven IT solution patterns to improve the productivity and efficiency of production operations. Industrial customers often start their digital transformation journey by sending OT data to the cloud for analysis and analytics without sending commands back to the industrial automation and control systems (ICAS). This process is often called "open loop" operations, since there is one-way communication from edge to cloud. Customers generally find this relatively easy to secure and manage. However, more often we are witnessing requirements to optimize operations by generating an automatic or operator-initiated response in the oil and gas production operation, rig management based on insights gained from cloud analytics. This process is often referred to as "closed loop" operations with two-way communication between edge and cloud. The security and compliance practices for closed loop operations are more rigorous because closed operations manipulate OT devices remotely. Developing these practices should be rooted in a cyber risk assessment to help businesses understand and prioritize security concerns. In this paper we propose how strengths of Cloud computing can become key enabler for oil and gas organizations in helping them enhance their overall security posture and manage risks within OT environments. We have deployed solution patterns described in this paper as the foundational pillar of several Oil and Gas organization's overall OT system architecture to unlock both "open loop" and "closed loop" operations in a secure, reliable and cost-effective manner. The specific scope items we will cover in this paper focus on a custom security uplift framework having four foundational components – 1) Cloud led architecture patterns to provide next generation network segmentation strategies in OT De-militarized Zone 2) OT asset inventory and vulnerability management 3) Centralized security monitoring and incident response with help of Artificial Intelligence and most recently providing Generative AI based virtual assistant to query security event data from OT systems. For organizations to plan their industrial digital transformation safely and securely, it is recommended that a multi-layered approach to secure the Industrial Control Systems (ICS)/OT and Cloud environments be implemented as captured in ten security golden rules in the following paper [1]. In addition to this, in the paper we propose a comprehensive architecture framework that aligns with established cybersecurity framework (CSF) such as NIST. The use case studies discussed in the paper will highlight how customer have been able to remediate critical security vulnerabilities within weeks post implementation by deploying a comprehensive asset inventory discovery and vulnerability assessment. This has help reduce mean time to identify and mitigate vulnerabilities from months to days with automated testing in pre-production environments, ensure quick detection and response towards security incidents with help of advanced security monitoring and incident response playbooks, covering 100% of the OT assets through this capability and leverage advanced data analytics, machine learning to perform log mining, data cleansing, data validation, log mining and analysis through natural language processing such as conversational AI assistants powered by large language model (LLM) in the Cloud.

The concept of smart homes has evolved significantly in recent years, fueled by the advancements in Internet of Things (IoT) technologies. By connecting everyday devices to the internet, IoT enables homes to become more efficient, secure, and personalized to the needs of their inhabitants. From intelligent lighting and energy management systems to voice-controlled assistants and advanced security features, IoT innovations are transforming how we interact with our living spaces. This article explores the evolution of smart homes, highlighting key IoT innovations that have improved the living experience, enhanced energy efficiency, and enhanced convenience and safety. It also examines the challenges associated with the widespread adoption of smart homes and presents future directions for further integration of IoT technologies in home environments.

This paper presents solutions developed in the ACTS ASPeCT project for advanced security features in UMTS. In particular, a secure billing scheme for value-added information services using micropayments is presented. The solutions will be validated in a trial to be conducted over an experimental UMTS platform.