Mr. Elvan M. Ugurlu Profile

Elvan M. Ugurlu

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Proceedings Article | 24 April 2020 Presentation + Paper

Program profiling based on Markov models and EM emanations

Baki Berkay Yilmaz, Elvan Mert Ugurlu, Frank Werner, Milos Prvulovic, Alenka Zajic

Proceedings Volume 11417, 114170D (2020) https://doi.org/10.1117/12.2560808

KEYWORDS: Profiling, Data modeling, Principal component analysis, Feature extraction, Model-based design, Dimension reduction, Modulation

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As one of the fundamental approaches for code optimization and performance analysis, profiling software activities can provide information on the existence of malware, code execution problems, etc. In this paper, we propose a methodology to profile a system with no overhead. The approach leverages electromagnetic (EM) emanations while executing a program, and exploits its flow diagram by constructing a Markov model. The states of the model are considered as the heavily executed blocks (called hot paths) of the program, and the transition between any two states is possible only if there exists a branching operation which enables execution of corresponding states without any intermediate state. To identify the state of the program, we utilize a supervised learning method. To do so, we first collect signals for each state, extract features, and generate a dictionary. The features are considered as the activated frequencies when the program is executed. The assumption here is that there exists at least one unique frequency component that is only active for one unique state. Moreover, to degrade the e↵ect of interruptions and other signals emanated from other parts of the device, and to obtain signals with high Signal-to-Noise Ratio (SNR), we average the output of Short-Time Fourier Transform (STFT). After extracting features, we apply Principle Component Analysis (PCA) for dimension reduction which helps monitoring systems in real time. Finally, we describe experimental setup and show results to demonstrate that the proposed methodology can detect malware activity with high accuracy.

Proceedings Article | 17 May 2019 Paper

Instruction level program tracking using electromagnetic emanations

Baki Berkay Yilamz, Elvan Mert Ugurlu, Alenka Zajic, Milos Prvulovic

Proceedings Volume 11011, 110110H (2019) https://doi.org/10.1117/12.2522620

KEYWORDS: Signal processing, Clocks, Electromagnetism, Solids, Field programmable gate arrays, Computing systems, Receivers, Signal generators, Software, Inspection

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Monitoring computer system activities on the instruction level provides more resilience to malware attacks because these attacks can be analyzed better by observing the changes on the instruction level. Assuming the source code is available, many training signals can be collected to track the instruction sequence to detect whether a malware is injected or the system works properly. However, training signals have to be collected with high sampling rate to ensure that the significant features of these signals do not vanish. Since the clock frequencies of the current computer systems are extremely high, we need to have a commercial device with high sampling rate, i.e. 10GHz, which either costs remarkably high, or does not exist. To eliminate the deficiencies regarding the insufficient sampling rate, we propose a method to increase the sampling rate with the moderate commercial devices for training symbols. In that respect, we first generate some random instruction sequences which exist in the inspected source code. Then, these sequences are executed in a for-loop, and emanated electromagnetic (EM) signals from the processor are collected by a commercially available device with moderate sampling rate, i.e. sampling rate is much smaller than the clock frequency. Lastly, we apply a mapping of the gathered samples by utilizing modulo of their timings with respect to execution time of overall instruction sequence. As the final step, we provide some experimental results to illustrate that we successfully track the instruction sequence by applying the proposed approach.

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