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1. TENET: Memory Safe and Fault Tolerant Persistent Transactional Memory}

   Madhava Krishnan Ramanathan; Diyu Zhou; Wook-Hee Kim; Sudarsun Kannan; Sanidhya Kashyap; Changwoo Min (February 2023, 21st USENIX Conference on File and Storage Technologies)

   Ashvin Goel; Dalit Naor (Ed.)

   Byte-addressable non-volatile memory (NVM) allows programs to directly access storage using memory interface without going through the expensive conventional storage stack. However, direct access to NVM makes the NVM data vulnerable to software bugs and hardware errors. This issue is critical because, unlike DRAM, corrupted data can persist forever, even after the system restart. Albeit the plethora of research on NVM programs and systems, there is little focus on protecting NVM data from software bugs and hardware errors. In this paper, we propose TENET, a new NVM programming framework, which guarantees memory safety and fault tolerance to protect NVM data against software bugs and hardware errors. TENET provides the popular persistent transactional memory (PTM) programming model. TENET leverages the concurrency guarantees (i.e., ACID properties) of PTM to provide performant and cost-efficient memory safety and fault tolerance. Our evaluations show that TENET offers an enhanced protection scope at a modest performance overhead and storage cost as compared to other PTMs with partial or no memory safety and fault tolerance support. 

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2. Co-designing Community-based Sharing of Smarthome Devices for the Purpose of Co-monitoring In-home Emergencies

   https://doi.org/10.1145/3544548.3581239  

   Alghamdi, Leena; Akter, Mamtaj; Kropczynski, Jess; Wisniewski, Pamela J.; Lipford, Heather (April 2023, ACM)

   We conducted 26 co-design interviews with 50 smarthome device owners to understand the perceived benefits, drawbacks, and design considerations for developing a smarthome system that facilitates co-monitoring with emergency contacts who live outside of one’s home. Participants felt that such a system would help ensure their personal safety, safeguard from material loss, and give them peace of mind by ensuring quick response and verifying potential threats. However, they also expressed concerns regarding privacy, overburdening others, and other potential threats, such as unauthorized access and security breaches. To alleviate these concerns, participants designed flexible and granular access control and fail-safe back-up features. Our study reveals why peer-based co-monitoring of smarthomes for emergencies may be beneficial but also difficult to implement. Based on the insights gained from our study, we provide recommendations for designing technologies that facilitate such co-monitoring while mitigating its risks. 

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3. Detecting Injection Attacks in ADS-B Devices Using RNN-Based Models

   https://doi.org/10.1109/ICNS60906.2024.10550816  

   Khoei, Tala Talaei; Ould_Slimane, Hadjar; Shamaileh, Khair Al; Kumar_Devabhaktuni, Vijaya; Kaabouch, Naima (April 2024, IEEE)

   The Automatic Dependent Surveillance Broadcast (ADS-B) system is a critical communication and surveillance technology used in the Next Generation (NextGen) project as it improves the accuracy and efficiency of air navigation. These systems allow air traffic controllers to have more precise and real-time information on the location and movement of aircraft, leading to increased safety and improved efficiency in the airspace. While ADS-B has been made mandatory for all aircraft in the Federal Aviation Administration (FAA) monitored airspace, its lack of security measures leaves it vulnerable to cybersecurity threats. Particularly, ADS-B signals are susceptible to false data injection attacks due to the lack of authentication and integrity measures, which poses a serious threat to the safety of the National Airspace System (NAS). Many studies have attempted to address these vulnerabilities; however, machine learning and deep learning approaches have gained significant interest due to their ability to enhance security without modifying the existing infrastructure. This paper investigates the use of Recurrent Neural Networks for detecting injection attacks in ADS-B data, leveraging the time-dependent nature of the data. The paper reviews previous studies that used different machine learning and deep learning techniques and presents the potential benefits of using RNN algorithms to improve ADS-B security. 

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4. An Approach Towards the Security Management for Sensitive Medical Data in the IoMT Ecosystem

   https://doi.org/10.1145/3565287.3623388  

   Chatterjee, Pushpita; Das, Debashis; Banerjee, Sourav; Ghosh, Uttam; Mpembele, Armando B; Rogers, Tamara (October 2023, ACM)

   The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant plat- form that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data. 

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5. DeepShuffle: A Lightweight Defense Framework against Adversarial Fault Injection Attacks on Deep Neural Networks in Multi-Tenant Cloud-FPGA

   Luo, Yukui; Rakin, Adnan Siraj; Fan, Deliang; Xu, Xiaolin (May 2024, 2024 IEEE Symposium on Security and Privacy (SP))

   FPGA virtualization has garnered significant industry and academic interests as it aims to enable multi-tenant cloud systems that can accommodate multiple users' circuits on a single FPGA. Although this approach greatly enhances the efficiency of hardware resource utilization, it also introduces new security concerns. As a representative study, one state-of-the-art (SOTA) adversarial fault injection attack, named Deep-Dup, exemplifies the vulnerabilities of off-chip data communication within the multi-tenant cloud-FPGA system. Deep-Dup attacks successfully demonstrate the complete failure of a wide range of Deep Neural Networks (DNNs) in a black-box setup, by only injecting fault to extremely small amounts of sensitive weight data transmissions, which are identified through a powerful differential evolution searching algorithm. Such emerging adversarial fault injection attack reveals the urgency of effective defense methodology to protect DNN applications on the multi-tenant cloud-FPGA system. This paper, for the first time, presents a novel moving-target-defense (MTD) oriented defense framework DeepShuffle, which could effectively protect DNNs on multi-tenant cloud-FPGA against the SOTA Deep-Dup attack, through a novel lightweight model parameter shuffling methodology. DeepShuffle effectively counters the Deep-Dup attack by altering the weight transmission sequence, which effectively prevents adversaries from identifying security-critical model parameters from the repeatability of weight transmission during each inference round. Importantly, DeepShuffle represents a training-free DNN defense methodology, which makes constructive use of the typologies of DNN architectures to achieve being lightweight. Moreover, the deployment of DeepShuffle neither requires any hardware modification nor suffers from any performance degradation. We evaluate DeepShuffle on the SOTA open-source FPGA-DNN accelerator, Vertical Tensor Accelerator (VTA), which represents the practice of real-world FPGA-DNN system developers. We then evaluate the performance overhead of DeepShuffle and find it only consumes an additional ~3% of the inference time compared to the unprotected baseline. DeepShuffle improves the robustness of various SOTA DNN architectures like VGG, ResNet, etc. against Deep-Dup by orders. It effectively reduces the efficacy of evolution searching-based adversarial fault injection attack close to random fault injection attack, e.g., on VGG-11, even after increasing the attacker's effort by 2.3x, our defense shows a ~93% improvement in accuracy, compared to the unprotected baseline. 

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