Program profiling based on Markov models and EM emanations

Yilmaz, Baki Berkay; Ugurlu, Elvan Mert; Werner, Frank T.; Prvulovic, Milos; Zajić, Alenka

doi:10.1117/12.2560808

Cited by 7 publications

(2 citation statements)

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“…This conditional code execution is crucial in many programming scenarios such as controlling the program flow and error-handling. Therefore, the detection of selection statements is highly important to track the program behavior in various methods developed against cyber-attacks and performance problems such as program profiling [3]- [5], tracking [6], [7] and malware detection [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Kerpicci,

Prvulovic,

Zajić

2023

IEEE Access

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This paper proposes a dissimilarity detection algorithm that can find different regions between similar signals. In particular, we address the detection problem of branching statements in program control flow using electromagnetic (EM) side-channels, where we have shown that such statements can be detected in emanating side-channels. Based on the findings, we have proposed a generalized approach for dissimilarity detection that can be efficiently applied to various real-world applications. In the proposed method, symbolic representation of the signals is used for efficient processing, where each signal frame is transformed into a string. The codebook of observed patterns is constructed with the reference signal. Then, the sequence of strings that is obtained from the main signal is compared with the codebook to find the newly observed patterns. Finally, the presented method outputs the samples of dissimilar regions in the signal compared to the reference. In the experiments, various EM side-channel signals are collected from different devices and different control flow examples to show the applicability and efficiency of the proposed method and the results show that dissimilarities can be detected with >98% accuracy. INDEX TERMSDissimilarity detection, electromagnetic side-channel, pattern matching, program control flow, time series representation.

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Section: Introductionmentioning

confidence: 99%

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Kerpicci,

Prvulovic,

Zajić

2023

IEEE Access

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“…Profiling can generally be divided into static (offline) and dynamic (during the execution). Dynamic profiling can use instrumentation, hardware counters [2], electromagnetic emanations [3], and program pre-runs, possibly in an emulator. Dynamic profiling has the following disadvantages: it by definition requires code to run, negatively affects program execution performance, requires representative data, and is time consuming.…”

Section: Introductionmentioning

confidence: 99%

Method for Profile-Guided Optimization of Android Applications Using Random Forest

et al. 2022

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When choosing a smartphone, many users are guided by the performance of smartphones and the speed of applications. Because it is difficult to measure the application speed directly, the speed of the application startup is considered and used for comparison. Android runtime (ART) uses several technologies to speed up applications. The first approach is the just-in-time (JIT) compilation of frequently used methods at runtime. The second approach is to compile entire application code ahead of time (AOT). The performance profile is a way to strike a balance between JIT and AOT. The runtime optimization features were introduced in Android Nougat in form of profile-guided optimization (PGO). By aggregating data from a multiplicity of users and devices in Play Store, ART profiling significantly speeds up this process and makes its outcome available to all users alike. We propose a machine learning based method called SPMLGen for generating application profiles used in the optimization. We avoid time delays caused by the need to collect information in advance to perform optimization and ensure user privacy. With profiles generated by SPMLGen, we obtain approximately the same application launch time as with profiles from Play Store. Measurements were taken on Samsung Galaxy S22 and A52 devices with Android 12 firmware and several dozen Samsung applications.

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Using Analog Side Channels for Program Profiling

Zajic,

Prvulovic

2023

Understanding Analog Side Channels Using Cryptography Algorithms

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Program profiling based on Markov models and EM emanations

Cited by 7 publications

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Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Efficient Dissimilarity Detection in Time Series With Application to Side-Channel Analysis

Method for Profile-Guided Optimization of Android Applications Using Random Forest

Using Analog Side Channels for Program Profiling

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