Static program analysis of embedded executable assembly code

Venkitaraman, Ramakrishnan; Gupta, Gopal

doi:10.1145/1023833.1023857

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…The code-semantics-based [12]: the code-semantics-based analyzes the meaning of the instructions. In addition, it obtains the flow chart and the functional block diagram of malware, and to determine whether the program is malicious, it analyzes the functions and intentions of the program.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection

2019

KSII TIIS

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This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets 1 . We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% 1 3655 with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

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Section: Related Workmentioning

confidence: 99%

Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection

2019

KSII TIIS

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“…After certain epochs of training, the cost function will change very little when updates using Eq. (11). Constrastive divergence (CD) is developed by Geoffrey Hinton for training RBM model.…”

Section: Image Texture Analysis Based On Deep Belief Networkmentioning

confidence: 99%

“…In order to have a fair comparison, the correct classification rate is performed as the following equation (11).…”

Section: Preds=dbnpredict(testx)mentioning

confidence: 99%

“…For characterizing Android applications' behaviors, static analysis takes into consideration some static information including permissions, deployment of components, intent message passing and Application Programming Interface (API) calls. There are many static analysis methods available for the examination of malware, such as the signature based [10], semantics-based [11], heuristic scanning technique [12] et al…”

Section: Introductionmentioning

confidence: 99%

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Android malicious code Classification using Deep Belief Network

Luo¹,

Tian²,

Long³

et al. 2018

KSII TIIS

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This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method-using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

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“…propose in [29] its use to support model checking of Intel MCS-51 microcontroller code by providing information that can be statically extracted from the source assembly code. Venkitamaran and Gupta [30] also analyze assembly code to automatically check whether code standards defined for a family of DSP processors have been followed by third party software developers.…”

Section: Static Analysis and Related Workmentioning

confidence: 99%

An evaluation of free/open source static analysis tools applied to embedded software

Torri

Fachini

Steinfeld

et al. 2010

2010 11th Latin American Test Workshop

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Static analysis can be a valuable strategy to improve the quality of embedded software at a lower development cost. In this paper, we have surveyed ten different free/open source tools that perform static software analysis and evaluated their use in embedded software. Experimental results show that the studied tools present widely different results, and most of them are not ready to be applied to embedded systems. Furthermore, we discuss possible directions to improve the use of static analysis tools in the embedded domain.

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Static program analysis of embedded executable assembly code

Cited by 10 publications

References 27 publications

Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection

Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection

Android malicious code Classification using Deep Belief Network

An evaluation of free/open source static analysis tools applied to embedded software

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