Parallel‐CNN network for malware detection

Bakhshinejad, Nazanin; Hamzeh, Ali

doi:10.1049/iet-ifs.2019.0159

Cited by 18 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By the decompilation of classes.dex, API calls, used permissions or string features like network addresses can be extracted from disassembled java codes [76,122,209] or smali codes [57,68,113,172]. On the other hand, the source files, including APK [19,108,164,194], classes.dex [8,69,137,152], resources files [20,80], and smali codes [9,91,107,190,206], can be inputted directly as text features or bytecode features. Except the features from source files, the application information in app markets, metadata pertaining to Android application, could also be used as features [42,46,77,151].…”

Section: Static Analysismentioning

confidence: 99%

“…Binary code: Apart from the disassembled codes, the raw data, binary code in APK file, can also be used as inputted features, and it is a promising solution to obfuscation techniques, like redundant API injections. Researchers convert the whole binary code or specific bytes of raw APK files into a vector of 8-bit unsigned integers [19,20,62,108,164,194].…”

Section: Static Analysismentioning

confidence: 99%

“…In addition, some researchers employ discrete representation approaches like Bag of Words (BOW), Term Frequency-Inverse Document Frequency (TF-IDF) and N-Gram to select words [11,127,132,159,163,165,188,193], but these methods still suffer from the problem of data sparsity and high dimensionality [173]. Therefore, many primary studies further investigate the effectiveness of pre-trained word embedding models, such as Continuous Word2vec [19,25,46,76,171,202,212] and GloVe [76], to learn the non-contextual representation from the extracted semantic features of Android APKs. Except representing words as dense vectors, there are some embedding approaches based on paragraph, sentence or document [134], and for example, three primary studies [89,151,202] employ Doc2vec [86] learn document-level embedding.…”

Section: Hybrid Analysismentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Android Malware Defenses: a Systematic Literature Review

Liu,

Tantithamthavorn,

et al. 2021

Preprint

View full text Add to dashboard Cite

Malicious applications (especially in the Android platform) are a serious threat to developers and end-users. Many research efforts have hence been devoted to developing effective approaches to defend Android malware. However, with the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, android malware defenses based on manual rules or traditional machine learning may not be effective due to limited apriori knowledge. In recent years, a dominant research field of deep learning (DL) with the powerful feature abstraction ability has demonstrated a compelling and promising performance in various fields, like Nature Language processing and image processing. To this end, employing deep learning techniques to thwart the attack of Android malware has recently gained considerable research attention. Yet, there exists no systematic literature review that focuses on deep learning approaches for Android Malware defenses. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 104 studies were identified over the period 2014-2020. The results of our investigation show that even though most of these studies still mainly consider DL-based on Android malware detection, 35 primary studies (33.7%) design the defenses approaches based on other scenarios. This review also describes research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.CCS Concepts: • Security and privacy → Malware and its mitigation; Software security engineering;• General and reference → Surveys and overviews.

show abstract

Section: Static Analysismentioning

confidence: 99%

Section: Static Analysismentioning

confidence: 99%

Section: Hybrid Analysismentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Android Malware Defenses: a Systematic Literature Review

Liu,

Tantithamthavorn,

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, deep learning based methods have recently been used for malware detection on network. In one of such studies, Bakhshinejad and Hamzeh [27 ] proposed a method for malware detection using parallel architecture of CNN.…”

Section: Related Workmentioning

confidence: 99%

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

Dandıl

2020

IET Inf. secur

View full text Add to dashboard Cite

“…Malware and counterfeit components can decrease performance or cause performance instability. There is a plethora of security measures that can be adopted to prevent malicious programs from being downloaded and remove them when they have been downloaded [2,3]. There are also many novel ways of detecting counterfeit hardware [4,5].…”

Section: Introductionmentioning

confidence: 99%

CPU Hardware Classification and Performance Prediction using Neural Networks and Statistical Learning

Foots¹,

Pal²,

Datta³

et al. 2020

IJAIA

View full text Add to dashboard Cite

We propose a set of methods to classify vendors based on estimated CPU performance and predict CPU performance based on hardware components. For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor to create classification zones. These zones can be used to identify vendors who manufacture hardware that satisfy a given performance requirement. We use multi-layered neural networks for performance prediction, which account for nonlinearity in performance data. Various neural network architectures are analysed in comparison to linear, quadratic, and cubic regression. Experiments show that neural networks obtain low error and high correlation between predicted and published performance values, while cubic regression produces higher correlation than neural networks when more data is used for training than testing. An analysis of how the neural network architecture affects prediction is also performed. The proposed methods can be used to identify suitable hardware replacements.

show abstract

Parallel‐CNN network for malware detection

Cited by 18 publications

References 25 publications

Deep Learning for Android Malware Defenses: a Systematic Literature Review

Deep Learning for Android Malware Defenses: a Systematic Literature Review

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

CPU Hardware Classification and Performance Prediction using Neural Networks and Statistical Learning

Contact Info

Product

Resources

About