Hence, conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT. In this paper, we proposed the nove
Deep learning for effective android malware detection using api call graph embeddings Soft Computing, 24 (2) (2020), pp. 1027-1043 CrossrefView in ScopusGoogle Scholar 18 Suleiman Y Yerima, Sakir Sezer, and Igor Muttik. Android malware detection using parallel machine learning classifiers. In ...
With the growth of Android OS-based smartphones, it has become a prime target for mobile malware attacks. Minimal alterations in malware samples can easily evade traditional detection methods such as signature-based detection. In contrast, artificial intelligence (AI) and machine learning (ML)-...
In this paper, a novel hybrid deep learning model called DeepVisDroid has been suggested for detecting android malware samples based on hybridizing image-based features with deep learning techniques. To this end, four grayscale image datasets have been constructed by converting some files from the ...
This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in th
The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learnin...
In this work, we propose ACAMA that can identify malware and can classify malware into specific categories by utilizing APIs used to implement malicious functions. ACAMA generates deep learning models based on APIs of Android malware with the CNN algorithm. To evaluate the performance of ACAMA, ...
In this paper DL-AMDet, a deep learning architecture is proposed to detect Android malware applications based on its static and dynamic features. DL-AMDet consists of two main detection models the first one uses CNN-BiLSTM deep learning method for detecting malware using static analysis. The ...
However, the combination of both approaches could substantially enhance detection systems. In this paper we present an hybrid approach to address the task of malware classification by fusing multiple types of features defined by experts and features learned through deep learning from raw data. In ...
(2020). Dl-droid: Deep learning based android malware detection using real devices. Computers & Security, 89, 101663. Article Google Scholar Shukla, S., Kolhe, G., Sai Manoj, P. D. & Rafatirad, S. (2019). Rnn-based classifier to detect stealthy malware using localized features and ...