Analysis of ResNet and GoogleNet models for malware detection

Khan, Riaz Ullah; Zhang, Xiaosong; Kumar, Rajesh

doi:10.1007/s11416-018-0324-z

Cited by 180 publications

(94 citation statements)

References 5 publications

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“…So far, many supervised machine learning algorithms are used to classify the data. Khan et al [49,50] analysed ResNet and GoogleNet models for malware detection using image processing technique. Kumar et al [51,52] used the Convolutional Neural Network CNN model for malicious code detection based on pattern recognition and permission-induced risk for Android IoT Devices, respectively.…”

Section: Comparison With Other Machine Learning Classifiersmentioning

confidence: 99%

An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

et al. 2019

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In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

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Section: Comparison With Other Machine Learning Classifiersmentioning

confidence: 99%

An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

et al. 2019

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“…There are numerous popular CNN topologies, such as AlexNet [33], GoogleNet, and ResNet [34]. These models perform well in detail extraction, and additional training of these models for new images can save time and produce satisfying results [35].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads

Qin

Chen

Lin

2020

IJERPH

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Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in “near scene” and “middle scene”, and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.

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“…Khan et al [9] used two famous deep learning models, ResNet [6] and GoogLeNet [10], for malware detection without modification. In their experiments, ResNet has better performance than GoogLeNet.…”

Section: Copyright C 2020 the Institute Of Electronics Information Amentioning

confidence: 99%

Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

Cho

Kim

Shin

et al. 2020

IEICE Trans. Inf. & Syst.

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Minkyoung CHO †a) , Member, Jik-Soo KIM †b) , Jongho SHIN †c) , and Incheol SHIN † †d) , Nonmembers SUMMARY We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).

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Analysis of ResNet and GoogleNet models for malware detection

Cited by 180 publications

References 5 publications

An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers

Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads

Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

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