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1. Bio-inspired VM Introspection for Securing Collaboration Platforms

   Huseyn Huseynov, H.; Saadawi, T.; and Kourai, K. (September 2021, International Conference on Intelligent Networking and Collaborative Systems (INCoS 2021))

   null (Ed.)

   As organizations drastically expand their usage of collaborative systems and multi-user applications during this period of mass remote work, it is crucial to understand and manage the risks that such platforms may introduce. Improperly or carelessly deployed and configured systems hide security threats that can impact not only a single organization, but the whole economy. Cloud-based architecture is used in many collaborative systems, such as audio/video conferencing, collaborative document sharing/editing, distance learning and others. Therefore, it is important to understand that safety risk can be triggered by attacks on remote servers and confidential information might be compromised. In this paper, we present an AI powered application that aims to constantly introspect multiple virtual servers in order to detect malicious activities based on their anomalous behavior. Once the suspicious process(es) detected, the application in real-time notifies system administrator about the potential threat. Developed software is able to detect user space based keyloggers, rootkits, process hiding and other intrusion artifacts via agent-less operation, by operating directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Conducted experiments on more than twenty different types of malicious applications provide evidence of high detection accuracy 

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2. GPR Sensing and Visual Mapping Through 4G-LTE, 5G, Wi-Fi HaLow, and Wi-Fi Hotspots with Edge Computing and AR Representation

   https://doi.org/10.3390/app15126552  

   Tanch, Scott; Fath, Alireza; Hanna, Nicholas; Xia, Tian; Huston, Dryver (June 2025, Applied Sciences)

   In this study, we demonstrate an application for 5G networks in mobile and remote GPR scanning situations to detect buried objects by experts while the operator is performing the scans. Using a GSSI SIR-30 system in conjunction with the RealSense camera for visual mapping of the surveyed area, subsurface GPR scans were created and transmitted for remote processing. Using mobile networks, the raw B-scan files were transmitted at a sufficient rate, a maximum of 0.034 ms mean latency, to enable near real-time edge processing. The performance of 5G networks in handling the data transmission for the GPR scans and edge computing was compared to the performance of 4G networks. In addition, long-range low-power devices, namely Wi-Fi HaLow and Wi-Fi hotspots, were compared as local alternatives to cellular networks. Augmented reality headset representation of the F-scans is proposed as a method of assisting the operator in using the edge-processed scans. These promising results bode well for the potential of remote processing of GPR data in augmented reality applications. 

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3. Deep Reinforcement Learning for Secure MEC Service in Vehicular Networks with Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/IEEECONF59524.2023.10476897  

   Wang, Haoyu; Bai, Haowen; Ju, Ying; Swindlehurst, A Lee (October 2023, Proc. 57th Asilomar Conference on Signals, Systems, and Computers)

   The broadcasting nature of wireless signals may result in the task offloading process of mobile edge computing (MEC) suffering serious information leakage. As a novel technology, physical layer security (PLS) combined with reconfigurable intelligent surfaces (RIS) can enhance transmission quality and security. This paper investigates the MEC service delay problem in RIS-aided vehicular networks under malicious eavesdropping. Due to the lack of an explicit formulation for the optimization problem, we propose a deep deterministic policy gradient (DDPG)-based communication scheme to optimize the secure MEC service. It aims to minimize the maximum MEC service time while reducing eavesdropping threats by jointly designing the RIS phase shift matrix and computing resource allocation in real-time. Simulation results demonstrate that 1) the DDPG-based scheme can help the base station make reasonable actions to realize secure MEC service in dynamic MEC vehicular networks; 2) deploying RIS can dramatically reduce eavesdropping threats and improve the overall MEC service quality. 

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4. Towards security-as-a-service in multi-access edge

   https://doi.org/10.1145/3318216.3363335  

   Tourani, Reza; Bos, Austin; Misra, Satyajayant; Esposito, Flavio (January 2019, SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing)

   The prevailing network security measures are often implemented on proprietary appliances that are deployed at fixed network locations with constant capacity. Such a rigid deployment is sometimes necessary, but undermines the flexibility of security services in meeting the demands of emerging applications, such as augmented/virtual reality, autonomous driving, and 5G for industry 4.0, which are provoked by the evolution of connected and smart devices, their heterogeneity, and integration with cloud and edge computing infrastructures. To loosen these rigid security deployments, in this paper, we propose a data-centric SECurity-as-a-Service (SECaaS) framework for elastic deployment and provisioning of security services at the Multi-Access Edge Computing (MEC) infrastructure. In particular, we discuss three security services that are suitable for edge deployment: (i) an intrusion detection and prevention system (IDPS), (ii) an access control enforcement system (ACE), and (iii) a communication anonymization service (CA). We benchmark the common security microservices along with the design and implementation of a proof of concept communication anonymization application. 

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5. User Profiling Attack Using Windows Registry Data

   https://doi.org/10.1109/UEMCON59035.2023.10315968  

   Amoruso, Edward L.; Zou, Cliff C.; Leinecker, Richard (October 2023, 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON))

   The Windows registry stores a glut of information containing settings and data utilized by the Microsoft operating system (OS) and other applications. For example, information such as user credentials, installed programs, recently used applications and documents, accessed resources such as local, remote, and removable devices can all be found in this database. More revealingly, the registry also has time and date stamps that can help build a timeline of user activities. The Windows registry can be easily queried by either malicious or benign applications. This is possible through the Windows Application Program Interface (API) and other OS built-in utilities. In this paper, we develop and demonstrate a program able to collect and infer a user’s rich activities by accessing the Windows registry alone. This information, also referred to as the user’s digital footprint, can be used to devise an exploit or create a privacy threat. Our custom developed application will demonstrate how a user’s digital footprint can be acquired by a malicious application from a Windows registry, without alerting security software. In addition, this information can be exported to a set of comma delimited files, making it easy to import them into other analysis applications. 

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