Side-channel attacks exploit the physical leakages from hardware components, such as power consumption, to break secure cryptographic algorithms and retrieve their secret key. Evaluating implementations of cryptographic algorithms against such analysis is crucial but traditional frameworks require expensive external devices like oscilloscopes, making the process expensive and time-consuming. Recent advancements in on-chip sensors offer a cost-effective, fully on-chip SCA framework, eliminating the need for external devices. In this paper, we propose Raven, an on-chip SCA framework with hardware implementations of Test Vector Leakage Assessment (TVLA), Correlation Power Analysis (CPA), and Deep Learningbased Leakage Assessment (DL-LA), for run-time evaluation of cryptographic implementations. RAVEN leverages on-chip sensors to efficiently assess side-channel security, without requiring any external measurement devices or any customized evaluation platform. Our proposed hardware implementations of TVLA, CPA, and DL-LA are lightweight and the entire architecture including the sensors can fit within the lightweight and low-cost AMD-Xilinx PYNQ FPGA platform. The proposed framework is verified on an FPGA implementation of AES-128 and the corresponding result of TVLA, CPA, and DL-LA closely matches with these algorithm's software implementation while requiring significantly less time and storage.

There has been a significant spike in cyber attacks with serious economic, security, and privacy concerns recently. The effectiveness of older NIDS (Network Intrusion Detection Systems) has been diminishing as cyber attacks become more sophisticated and complex. Majority of research focuses on detecting and classifying attacks, with limited work attempting to forecast the number of cyber attack. Recent works propose using statistical, machine learning, or deep learning models to forecast cyber attacks, but they do not take advantage of the correlation that exists among different devices present in a network. We proposed a model, PANGA that employs a combination of Principal Neighborhood Aggregation(PNA), Gated Recurrent Unit(GRU), and attention layers to effectively capture and leverage spatiotemporal dependencies. It achieves a mean square error of 0.027 and a coefficient of determination of 0.87 on well known CIDDS-001 dataset. Additionally, we performed perturbation analysis by adding gaussian noises to the test data to validate the robustness of the model. The performance of the proposed model remains largely unaffected as long as the standard deviation of the noise was kept below 0.25.

Sensors aesthetically embedded in accessoriessuch as jewelry, piercings or contact lenses arebeing proposed recently. These symbiotic wearable wirelesssensors are envisioned to operate on scarce harvestedenergy resources from the human body. In addition tothe hardware and software constraints arising from theform-factor and low energy operations, there are safetyrequirements such as avoidance of physical injury. Thedesign implications of these requirements are non-intuitiveand may involve estimation of human physiological dynamics. The physical impact of a sensor operation canbe controlled by appropriate design of multiple sensorcomponents such as processor, radio, and optimization ofdata algorithm. For example, the risk of thermal injury totissue can be reduced by limiting the sensing frequency, the computation power, and the radio duty cycle of bodyworn sensor. Hence, it is a challenging task to trace backa cause of a physical impact to hardware and softwaredesign decisions in a sensor. This paper proposes a novelnon-linear optimization framework to consider safety andsustainability requirements that depend on the humanphysiology and derive system level design parameters of asensor. We demonstrate our methodology using three casestudies: a) continuously monitoring ECG sensor sustainedby body heat, b) thermally safe network of implantedsensors, and c) infusion pump control algorithm to avoidhypo-glycemia.

Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to the BSN and senses physiological signals through processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram of one person gets coupled to the electroencephalogram of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attack on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN.

Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to the BSN and senses physiological signals through processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram of one person gets coupled to the electroencephalogram of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attack on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN.

Implementing requirements verified body worn medical sensors and smart phones, acting as base stations, in Body Sensor Networks (BSNs), is of extreme importance for development of reliable pervasive health monitoring systems (PHMS). Models of BSNs have been used to analyze designs with respect to requirements such as energy consumption, lifetime, and network reliability under dynamic context changes due to user mobility. This paper proposes Health-Dev that takes a high level specification of requirements verified BSN design and automatically generates both the sensor and smart phone code. Case studies related to energy efficiency and mobility aware network reliability show whether the resulting implementation satisfies the requirements set forth in the design phase.