Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive framework designed to integrate robust security measures throughout the entire ML operations (MLOps) lifecycle. SecMLOps builds on the principles of MLOps by embedding security considerations from the initial design phase through to deployment and continuous monitoring. This framework is particularly focused on safeguarding against sophisticated attacks that target various stages of the MLOps lifecycle, thereby enhancing the resilience and trustworthiness of ML applications. A detailed advanced pedestrian detection system (PDS) use case demonstrates the practical application of SecMLOps in securing critical MLOps. Through extensive empirical evaluations, we highlight the trade-offs between security measures and system performance, providing critical insights into optimizing security without unduly impacting operational efficiency. Our findings underscore the importance of a balanced approach, offering valuable guidance for practitioners on how to achieve an optimal balance between security and performance in ML deployments across various domains.

Connected and Autonomous Vehicles (CAVs) equipped with diverse sensors can enhance environmental perception through cooperative sensing, overcoming individual sensor limitations such as restricted range and occlusion. However, privacy concerns regarding location exposure and data leakage significantly hinder widespread adoption. This paper presents a comprehensive privacy-preserving cooperative sensing framework that enables secure data sharing among CAVs without compromising performance. We introduce two key innovations: the Vehicular Spatial Index Tree (VSITree), which provides efficient spatial indexing while preventing location leakage through cryptographic encoding, and the Vehicular Attribute Matching Protocol (VAMP), which enables oblivious membership testing between encrypted sensing data and queries. Our framework leverages arithmetic secret sharing and predicate encryption to protect both sensing providers and requesters throughout the data lifecycle. The system is designed to operate through roadside units (RSUs) that facilitate secure matching and aggregation without learning sensitive information. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of our approach, confirming its resilience against various attack vectors while maintaining real-time performance suitable for safety-critical vehicular applications.

Vehicular crowdsensing enables Connected and Autonomous Vehicles (CAVs) to jointly contribute driving-related data to support applications such as traffic management and accident analysis. Ensuring the reliability of such data requires identifying observations that have been corroborated by multiple vehicles. However, achieving this corroboration typically necessitates comparing each vehicle’s private set of observations, which can inadvertently reveal sensitive trajectory information. To address this privacy challenge, we introduce Edge-Assisted Private Set Intersection (EA-PSI), a scheme that enables secure computation of the intersection among CAV observation sets without disclosing individual data elements. Our design begins with a protocol that leverages polynomial-based set encoding and additive secret sharing, in which each vehicle encodes its observation set as a polynomial and divides it into two shares, delegating one share to a roadside unit (RSU) while retaining the other locally. The RSU then performs intersection computation on the collected shares using randomized encoding techniques, without learning any private observations or intersection results. To support necessary polynomial operations over secret-shared data, we develop a secure multiplication mechanism based on Beaver triples, enabling the RSU and vehicles to jointly compute polynomial products without reconstructing underlying values. In addition, we design a key-distribution protocol that facilitates secure communication among vehicles through the RSU, eliminating the need for direct vehicle-to-vehicle exchange. We analyze the security of EA-PSI under the simulation-based paradigm and formally prove privacy against semi-honest adversaries. Experimental evaluation of computational and communication costs demonstrates the efficiency and practicality of the proposed EA-PSI scheme.

Industrial Internet of Things (IIoT) systems in sectors like manufacturing, energy, and healthcare are increasingly deployed in cloud-assisted operational environments, where network telemetry and security analytics are routinely processed in the cloud. However, these systems remain highly vulnerable to cyber threats from shared threat actors. Among these, low-rate Denial of Service (LDoS) attacks, marked by subtle periodic traffic patterns, are particularly challenging to detect when analyzed in isolation. Cross-organization collaborative detection across cloud platforms can improve identification accuracy, but sharing threat intelligence risks exposing sensitive operational information. To tackle this challenge, we propose Labelled-Threshold Private Set Intersection (LT-PSI), a cryptographic framework that allows two organizational clouds to securely identify common elements whose associated label vectors satisfy a similarity threshold, without revealing any additional data. Our LT-PSI protocol introduces an innovative combination of position encoding, Diffie-Hellman Oblivious Pseudorandom Functions (DH-OPRF), and Bloom filters, effectively transforming threshold-based label similarity matching into efficient and privacy-preserving set membership tests. Particularly, our protocol achieves sublinear online complexity and is well-suited for cloud execution, integrating an adaptive early termination strategy that significantly reduces the number of OPRF invocations. We provide formal security proofs under the semi-honest model and validate the protocol through extensive experiments across diverse similarity thresholds and dataset sizes. Results show that LT-PSI is significantly more efficient than brute-force threshold matching while preserving privacy. The framework naturally supports cloud-to-cloud collaborative security analytics and generalizes to broader cloud and edge threat intelligence scenarios requiring private, threshold-based feature matching.

Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sensor networks (RSNs) into the CAV ecosystem presents a promising solution to improve sensing performance, but also introduces significant challenges, including heterogeneous sensing requirements, inconsistencies in multisource sensor data, and efficient resource utilization. To address these challenges, this article proposes a novel cooperative sensing framework that leverages multisource and multilevel sensing information from RSNs to optimize CAV sensing performance in resource-constrained scenarios. We develop an improved decision transformer (DT)-based approach that dynamically adapts to diverse driving conditions and efficiently fuses sensor data at various abstraction levels. To tackle the issue of long-delayed rewards, we introduce a reshaped reward function and a bi-level optimization framework that enables effective propagation of rewards along decision sequences. An advanced gradient approximation technique is employed to efficiently solve the optimization problem. Extensive simulations demonstrate the superior performance of our improved DT approach compared to state-of-the-art reinforcement learning (RL) methods in terms of sensing accuracy, coverage, and data efficiency under various traffic conditions.

DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application. This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations. Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape. Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution. This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.

The rapid proliferation of DevOps variants, collectively known as XOps, reflects the growing complexity and specialization within software development and operations. However, the diversity of these practices has led to inconsistencies and confusion, which complicates the development and standardization of the field. While there has been significant research on individual XOps, there is a lack of systematic, horizontal analysis across all XOps practices. This paper addresses this gap by conducting a scoping review of XOps literature, focusing on three fundamental research questions: the definition and categorization of XOps, the methodologies used to study their adoption, and the common challenges identified in their implementation. As the first study to systematically address these questions, we propose the XOps conceptualization framework, offering a structured approach to understanding and studying XOps. This framework serves as an initial step toward bringing clarity to the DevOps landscape, providing guidance in uncovering its complexities and laying the foundation for future research and the emergence of new XOps.

Connected and Autonomous Vehicles (CAVs) can improve their perception by integrating data from roadside sensors. However, ensuring secure authentication between CAVs and sensors is challenging due to the limited capabilities of sensors and the growing number of vehicles. This paper introduces a secure authentication protocol that enables direct communication between CAVs and roadside sensors, addressing a critical gap in existing research focused on vehicle-to-cloud authentication. The proposed multi-factor authentication scheme combines password, biometric, and device-specific factors with Elliptic Curve Cryptography (ECC) and efficient key agreement protocols. A comprehensive adversary model tailored for vehicular networks is presented, along with an in-depth security analysis demonstrating the scheme’s resilience against various threats. The cloud-assisted authentication framework offloads computationally intensive tasks to the cloud server, reducing the burden on resource-constrained Roadside Units (RSUs) and ensuring scalability. Extensive performance evaluations showcase the scheme’s computational efficiency, low communication overhead, and storage costs compared to state-of-the-art solutions, highlighting its practical feasibility and potential for real-world deployment in intelligent transportation systems.

Due to the proliferation of machine learning in various domains and applications, Machine Learning Operations (MLOps) was created to improve efficiency and adaptability by automating and operationalizing ML products. Because many machine learning application domains demand high levels of assurance, security has become a top priority and necessity to be involved at the beginning of ML system design. To provide theoretical guidance, we first introduce the Secure Machine Learning Operations (SecMLOps) paradigm, which extends MLOps with security considerations. We use the People, Processes, Technology, Governance and Compliance (PPTGC) framework to conceptualize SecMLOps, and to discuss challenges in adopting SecMLOps in practice. Since ML systems are often multi-concerned, analysis on how the adoption of SecMLOps impacts other system qualities, such as fairness, explainability, reliability, safety, and sustainability are provided. This paper aims to provide guidance and a research roadmap for ML researchers and organizational-level practitioners towards secure, reliable, and trustworthy MLOps.