AndroAnalyzer: android malicious software detection based on deep learning

Arslan, Recep Sinan

doi:10.7717/peerj-cs.533

Cited by 16 publications

(15 citation statements)

References 35 publications

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“…is method involves disassembly of source code and analyzing it to check the presence of malware without executing the source code and depend only on malware abstraction characteristics and application byte code. Mostly, reengineering is applied [41][42][43].…”

Section: Static Dynamicmentioning

confidence: 99%

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A Modified ResNeXt for Android Malware Identification and Classification

Albahar

Elsayed

Jurcut

2022

Computational Intelligence and Neuroscience

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It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware’s features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on the convolutional neural network (CNN) on the visualized malware samples to automatically obtain the discriminatory features that separate normal from malicious data. Using DL techniques not only avoids the domain expert costs but also eliminates the frequent need for the feature engineering methods. Furthermore, we evaluated the effectiveness of the modified ResNeXt model in the classification process by testing a total of fifteen different combinations of the Android malware image sections on the Drebin dataset. In this study, we only use grayscale malware images from a modified ResNeXt to analyze the malware samples. The experimental results show that the modified ResNeXt successfully achieved an accuracy of 98.25% using Android certificates only. Furthermore, we undertook extensive trials on the dataset in order to confirm the efficacy of our methodology, and we compared our approach with several existing methods. Finally, this article reveals the evaluation of different models and a much more precise option for malware identification.

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Section: Static Dynamicmentioning

confidence: 99%

“…Collects temporal instructions. Applications are in format APK or archive in a zip package [41][42][43].…”

Section: Advantagementioning

confidence: 99%

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A Modified ResNeXt for Android Malware Identification and Classification

Albahar

Elsayed

Jurcut

2022

Computational Intelligence and Neuroscience

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“…At last, the URL is classified and detected by SoftMax function with global feature. In the study conducted earlier [16], an algorithm was suggested which employs AndroAnalyzer, a model that applies both deep learning systems and static analysis. In this study, the analysis was conducted on original datasets comprising 7,622 applications.…”

Section: Introductionmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

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Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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“…There are a lot of groups and categories of Android malware APKs, such as worms, botnet, rootkits, ransomware, and Trojans [ 3 ]. These Android malware attacks can exploit metamorphic and polymorphic procedures to obfuscate traditional malware recognition and detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Android malware analysis in a nutshell

2022

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This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The results reveal that some factors might significantly impact the performance of the malware analysis solution. For example, from a security perspective, the accuracy, F1-score, precision, and recall are improved by 131.29%, 236.44%, 192%, and 131.29%, respectively, when changing one factor and fixing all other factors under study. Similar results are observed in the case of complexity assessment, including testing time, CPU usage, storage size, and pre-processing speed, proving the importance of the proposed android malware analysis model.

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AndroAnalyzer: android malicious software detection based on deep learning

Cited by 16 publications

References 35 publications

A Modified ResNeXt for Android Malware Identification and Classification

A Modified ResNeXt for Android Malware Identification and Classification

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Android malware analysis in a nutshell

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