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1. Potentiometric Sensor System with Self-Calibration for Long-Term, In Situ Measurements

   https://doi.org/10.3390/chemosensors11010048  

   Zhang, Zhehao; Boselli, Elena; Papautsky, Ian (January 2023, Chemosensors)

   We built an integrated solid-contact ion-selective electrode (SCISE) system with the functionality of self-calibration. A multiplexed SCISE sensor (K+ and NO3− vs. Ag/AgCl) was fabricated on printed-circuit board (PCB) substrates and was subsequently embedded into a microfluidic flow cell for self-calibration and flow-through analysis. A PCB circuit that includes modules for both sensor readout and fluid control was developed. The sensors showed a fast and near-Nernstian response (56.6 for the K+ electrode and −57.4 mV/dec for the NO3− electrode) and maintained their performance for at least three weeks. The sensors also showed a highly reproducible response in an automated two-point calibration, demonstrating the potential for in situ monitoring. Lastly, the sensor system was successfully applied to measure mineral nutrients in plant sap samples. 

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2. ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection

   https://doi.org/10.46586/tches.v2023.i1.301-325  

   Mosavirik, Tahoura; Schaumont, Patrick; Tajik, Shahin (January 2023, IACR Transactions on Cryptographic Hardware and Embedded Systems)

   Physical attacks can compromise the security of cryptographic devices. Depending on the attack’s requirements, adversaries might need to (i) place probes in the proximity of the integrated circuits (ICs) package, (ii) create physical connections between their probes/wires and the system’s PCB, or (iii) physically tamper with the PCB’s components, chip’s package, or substitute the entire PCB to prepare the device for the attack. While tamper-proof enclosures prevent and detect physical access to the system, their high manufacturing cost and incompatibility with legacy systems make them unattractive for many low-cost scenarios. In this paper, inspired by methods known from the field of power integrity analysis, we demonstrate how the impedance characterization of the system’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect various classes of tamper events. We explain how these embedded network analyzers, without any modifications to the system, can be deployed on FPGAs to extract the frequency response of the PDN. The analysis of these frequency responses reveals different classes of tamper events from board to chip level. To validate our claims, we run an embedded network analyzer on FPGAs of a family of commercial development kits and perform extensive measurements for various classes of PCB and IC package tampering required for conducting different side-channel or fault attacks. Using the Wasserstein Distance as a statistical metric, we further show that we can confidently detect tamper events. Our results, interestingly, show that even environment-level tampering activities, such as the proximity of contactless EM probes to the IC package or slightly polished IC package, can be detected using on-chip impedance sensing. 

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3. Model inversion attacks against collaborative inference

   https://doi.org/10.1145/3359789.3359824  

   He, Zecheng; Zhang, Tianwei; Lee, Ruby B. (December 2019, 35th Annual Computer Security Applications Conference (ACSAC))

   null (Ed.)

   The prevalence of deep learning has drawn attention to the privacy protection of sensitive data. Various privacy threats have been presented, where an adversary can steal model owners' private data. Meanwhile, countermeasures have also been introduced to achieve privacy-preserving deep learning. However, most studies only focused on data privacy during training, and ignored privacy during inference. In this paper, we devise a new set of attacks to compromise the inference data privacy in collaborative deep learning systems. Specifically, when a deep neural network and the corresponding inference task are split and distributed to different participants, one malicious participant can accurately recover an arbitrary input fed into this system, even if he has no access to other participants' data or computations, or to prediction APIs to query this system. We evaluate our attacks under different settings, models and datasets, to show their effectiveness and generalization. We also study the characteristics of deep learning models that make them susceptible to such inference privacy threats. This provides insights and guidelines to develop more privacy-preserving collaborative systems and algorithms. 

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4. Adversarial Anomaly Detection Using Centroid-Based Clustering

   https://doi.org/10.1109/IRI.2018.00009  

   Anindya, Imrul Chowdhury; Kantarcioglu, Murat (July 2018, 2018 IEEE International Conference on Information Reuse and Integration (IRI))

   As cyber attacks are growing with an unprecedented rate in the recent years, organizations are seeking an efficient and scalable solution towards a holistic protection system. As the adversaries are becoming more skilled and organized, traditional rule based detection systems have been proved to be quite ineffective against the continuously evolving cyber attacks. Consequently, security researchers are focusing on applying machine learning techniques and big data analytics to defend against cyber attacks. Over the recent years, several anomaly detection systems have been claimed to be quite successful against the sophisticated cyber attacks including the previously unseen zero-day attacks. But often, these systems do not consider the adversary's adaptive attacking behavior for bypassing the detection procedure. As a result, deploying these systems in active real-world scenarios fails to provide significant benefits in the presence of intelligent adversaries that are carefully manipulating the attack vectors. In this work, we analyze the adversarial impact on anomaly detection models that are built upon centroid-based clustering from game-theoretic aspect and propose adversarial anomaly detection technique for these models. The experimental results show that our game-theoretic anomaly detection models can withstand attacks more effectively compared to the traditional models. 

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5. An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics

   https://doi.org/10.1145/3148055.3148077  

   Villalobos, J. J.; Rodero, Ivan; Parashar, Manish (January 2017, BDCAT '17 Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies)

   A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack. 

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