The proliferation of ubiquitous computing requires energy-efficient as well as secure operation of modern processors. Side channel attacks are becoming a critical threat to security and privacy of devices embedded in modern computing infrastructures. Unintended information leakage via physical signatures such as power consumption, electromagnetic emission (EM) and execution time have emerged as a key security consideration for SoCs. Also, information published on purpose at user privilege level accessible through software interfaces results in software only attacks. In this paper, we used a supervised learning based approach for inferring applications executing on android platform based on features extracted from EM side-channel emissions and software exposed dynamic voltage frequency scaling(DVFS) states. We highlight the importance of machine learning based approach in utilizing these multi-dimensional features on a complex SoC, against profiling-based approaches. We also show that learning the instantaneous frequency states polled from onboard frequency driver (cpufreq) is adequate to identify a known application and flag potentially malicious unknown application. The experimental results on benchmarking applications running on ARMv8 processor in Snapdragon 820 board demonstrates early detection of these apps, and atleast 85 accuracy in detecting unknown applications. Overall, the highlight is to utilize a low-complexity path to application inference attacks through learning instantaneous frequency states pattern of CPU core.
Many of the past efforts have focused on EM based side-channel analysis. Nazari et. al. and Callan et. al. presented EM-based acquisition and analysis flow to detect code change injected by an adversary @cite_7 @cite_4 . ML-models were used to learn features extracted from power side channel emissions to create a model for detecting malware in medical devices @cite_10 . Similarly, a security monitor for control flow integrity of programs executing on industrial PLC has been demonstrated using LSTM network based on features derived from EM-emissions @cite_11 .
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Medical devices based on embedded systems are ubiquitous in clinical settings. Increasingly, they connect to networks and run off-the-shelf operating systems vulnerable to malware. But strict validation requirements make it prohibitively difficult or costly to use anti-virus software or automated operating system updates on these systems. Our add-on monitoring system, WattsUpDoc, uses a traditionally undesirable side channel of power consumption to enable run-time malware detection. In our experiments, WattsUpDoc detected previously known malware with at least 94 accuracy and previously unknown malware with at least 85 accuracy on several embedded devices--detection rates similar to those of conventional malware-detection systems on PCs. WattsUpDoc detects malware without requiring hardware or software modifications or network communication. This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations that are emitted as a side effect of execution on the monitored system, and it relies on spikes in the EM spectrum that are produced as a result of periodic (e.g. loop) activity in the monitored execution. During training, EDDIE characterizes normal execution behavior in terms of peaks in the EM spectrum that are observed at various points in the program execution, but it does not need any characterization of the malware or other code that might later be injected. During monitoring, EDDIE identifies peaks in the observed EM spectrum, and compares these peaks to those learned during training. Since EDDIE requires no resources on the monitored machine and no changes to the monitored software, it is especially well suited for security monitoring of embedded and IoT devices. We evaluate EDDIE on a real IoT system and in a cycle-accurate simulator, and find that even relatively brief injected bursts of activity (a few milliseconds) are detected by EDDIE with high accuracy, and that it also accurately detects when even a few instructions are injected into an existing loop within the application. This paper presents a new metric, which we call Signal Available to Attacker (SAVAT), that measures the side channel signal created by a specific single-instruction difference in program execution, i.e. The amount of signal made available to a potential attacker who wishes to decide whether the program has executed instruction event A or instruction event B. We also devise a practical methodology for measuring SAVAT in real systems using only user-level access permissions and common measurement equipment. Finally, we perform a case study where we measure electromagnetic (EM) emanations SAVAT among 11 different instructions for three different laptop systems. Our findings from these experiments confirm key intuitive expectations, e.g. That SAVAT between on-chip instructions and off-chip memory accesses tends to be higher than between two on-chip instructions. However, we find that particular instructions, such as integer divide, have much higher SAVAT than other instructions in the same general category (integer arithmetic), and that last-level-cache hits and misses have similar (high) SAVAT. Overall, we confirm that our new metric and methodology can help discover the most vulnerable aspects of a processor architecture or a program, and thus inform decision-making about how to best manage the overall side channel vulnerability of a processor, a program, or a system. Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors. Zeus monitors for control flow integrity of the PLC program execution. Zeus monitors the communications between the human machine interface and the PLC, and captures the control logic binary uploads to the PLC. Zeus exercises its feasible execution paths, and fingerprints their emissions using an external electromagnetic sensor. Zeus trains a neural network for legitimate PLC executions, and uses it at runtime to identify the control flow based on PLC's electromagnetic emissions. We implemented Zeus on a commercial Allen Bradley PLC, which is widely used in industry, and evaluated it on real-world control program executions. Zeus was able to distinguish between different legitimate and malicious executions with 98.9 accuracy and with zero overhead on PLC execution by design.