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1. A Survey on Security Applications with SmartNICs: Taxonomy, Implementations, Challenges, and Future Trends

   Elizalde, Sergio; AlSabeh, Ali; Mazloum, Ali; Choueiri, Samia; Kfoury, Elie; Gomez, Jose; Crichigno, Jorge (October 2025, Journal of Network and Computer Applications)

   Over the last decade, network applications have grown exponentially, demanding high-speed interconnects. Unfortunately, chip manufacturers are approaching the upper limits of silicon-based computing with slow improvements in computational performance and energy efficiency. This trend has forced the industry to shift paradigms, moving from monolithic architectures to heterogeneous, domain-specific designs. Moreover, the ever-evolving threats compromise digital services and demand more scalable and flexible solutions to ensure service continuity in production networks. Smart Network Interface Cards (SmartNICs) are a product of this new paradigm, integrating domain-specific engines and general-purpose cores to offload various network infrastructure tasks, including those related to security. This paper provides a comprehensive overview of SmartNICs, with a particular focus on their role in strengthening network defenses. It introduces SmartNIC technology and presents a taxonomy of security applications offloaded to SmartNICs, categorized into Intrusion Detection and Prevention Systems (IDS/IPS), defenses against volumetric attacks, and data confidentiality mechanisms. Additionally, the paper explores vulnerabilities associated with adopting SmartNICs in the cloud, examining the threat model and reviewing proposed remediations in the literature. Finally, it discusses challenges and future trends in SmartNIC security applications, highlighting current initiatives and open research areas. 

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2. Security Analysis of AWS-based Video Surveillance Systems

   https://doi.org/10.1109/ICEET53442.2021.9659574  

   Aklamati, Davies; Abdus-Shakur, Basheerah; Kacem, Thabet (October 2021, 2021 International Conference on Engineering and Emerging Technologies (ICEET))

   In the last few years, Cloud computing technology has benefited many organizations that have embraced it as a basis for revamping the IT infrastructure. Cloud computing utilizes Internet capabilities in order to use other computing resources. Amazon Web Services (AWS) is one of the most widely used cloud providers that leverages the endless computing capabilities that the cloud technology has to offer. AWS is continuously evolving to offer a variety of services, including but not limited to, infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service. Among the other important services offered by AWS is Video Surveillance as a Service (VSaaS) that is a hosted cloud-based video surveillance service. Even though this technology is complex and widely used, some security experts have pointed out that some of its vulnerabilities can be exploited in launching attacks aimed at cloud technologies. In this paper, we present a holistic security analysis of cloud-based video surveillance systems by examining the vulnerabilities, threats, and attacks that these technologies are susceptible to. We illustrate our findings by implementing several of these attacks on a test bed representing an AWS-based video surveillance system. The main contributions of our paper are: (1) we provided a holistic view of the security model of cloud based video surveillance summarizing the underlying threats, vulnerabilities and mitigation techniques (2) we proposed a novel taxonomy of attacks targeting such systems (3) we implemented several related attacks targeting cloud-based video surveillance system based on an AWS test environment and provide some guidelines for attack mitigation. The outcome of the conducted experiments showed that the vulnerabilities of the Internet Protocol (IP) and other protocols granted access to unauthorized VSaaS files. We aim that our proposed work on the security of cloud-based video surveillance systems will serve as a reference for cybersecurity researchers and practitioners who aim to conduct research in this field. 

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3. Protection of Network Security Selector Secrecy in Outsourced Network Testing

   Sultan Alasmari, Weichao Wang (July 2023, The 32nd International Conference on Computer Communications and Networks (ICCCN 2023))

   With the emergence and fast development of cloud computing and outsourced services, more and more companies start to use managed security service providers (MSSP) as their security service team. This approach can save the budget on maintaining its own security teams and depend on professional security persons to protect the company infrastructures and intellectual property. However, this approach also gives the MSSP opportunities to honor only a part of the security service level agreement. To pre- vent this from happening, researchers propose to use outsourced network testing to verify the execution of the security policies. During this procedure, the end customer has to design network testing traffic and provide it to the testers. Since the testing traffic is designed based on the security rules and selectors, external testers could derive the customer network security setup, and conduct subsequent attacks based on the learned knowledge. To protect the network security configuration secrecy in outsourced testing, in this paper we propose different methods to hide the accurate information. For Regex-based security selectors, we propose to introduce fake testing traffic to confuse the testers. For exact match and range based selectors, we propose to use NAT VM to hide the accurate information. We conduct simulation to show the protection effectiveness under different scenarios. We also discuss the advantages of our approaches and the potential challenges. 

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4. Using Cloudmesh GAS for Speedy Generation of Hybrid Multi-Cloud Auto Generated AI Services

   https://doi.org/10.1109/COMPSAC51774.2021.00032  

   Laszewski, Gregor von; Orlowski, Anthony; Otten, Richard H.; Markowitz, Reilly; Gandhi, Sunny; Chai, Adam; Fox, Geoffrey C.; Chang, Wo L. (July 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC))

   Today’s problems require a plethora of analytics tasks to be conducted to tackle state-of-the-art computational challenges posed in society impacting many areas including health care, automotive, banking, natural language processing, image detection, and many more data analytics-related tasks. Sharing existing analytics functions allows reuse and reduces overall effort. However, integrating deployment frameworks in the age of cloud computing are often out of reach for domain experts. Simple frameworks are needed that allow even non-experts to deploy and host services in the cloud. To avoid vendor lock-in, we require a generalized composable analytics service framework that allows users to integrate their services and those offered in clouds, not only by one, but by many cloud compute and service providers.We report on work that we conducted to provide a service integration framework for composing generalized analytics frame-works on multi-cloud providers that we call our Generalized AI Service (GAS) Generator. We demonstrate the framework’s usability by showcasing useful analytics workflows on various cloud providers, including AWS, Azure, and Google, and edge computing IoT devices. The examples are based on Scikit learn so they can be used in educational settings, replicated, and expanded upon. Benchmarks are used to compare the different services and showcase general replicability. 

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5. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   https://doi.org/10.3390/fi14120354  

   Xu, Ronghua; Chen, Yu; Chen, Genshe; Blasch, Erik (December 2022, Future Internet)

   The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. 

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