There have been a number of previous surveys on Android malware detection using ML. However, each of these only provide a partial review of the literature, typically focusing on a particular group of approaches.
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 ...
Machine learning based intrusion detection framework for detecting security attacks in internet of things Article Open access 04 December 2024 Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm Article Open access 12 May 2025 Introduction...
our classifier outperforms both traditional machine learning (ML) and deep learning (DL) techniques, showcasing its robustness and dependability in identifying malware threats within cloud settings. The results of our study underscore the classifier's potential to serve as a crucial tool for ...
malware detection. So far as we know, DeepMal is the first practical and systematical adversarial learning method, which could directly produce adversarial samples and effectively bypass static malware detectors powered by DL and machine learning (ML) models while preserving attack functionality in the...
(2020) developed the DOOM system using the Opcode feature based on deep reinforcement learning to enhance the intrusion detection system defense mechanism. Wang et al.(2017) proposed a new adversary resistant technique that obstructs attackers from constructing impactful AME by randomly nullifying ...
Machine learning offers the ability to reduce much of the manual effort required with traditional approaches to malware analysis, as well as increased accuracy in malware detection and classification. In the context of malware analysis, a machine learning model is trained on a dataset of existing la...
Additionally, the malware executable files are handled effectively using the Relief Feature Selection technique. Results show a 97.968% of accuracy in windows malware detection. A Deep Learning-based malware detection method that utilizes the combination of a visualization technique and CNN [282] is ...
This repo summarizes the results of the joint effort of the researcher group (George Vyshnya, Denys Frolov and Co). The main purpose of such an effort was to demonstrate that the novel DL network architectures with attention can improve the results of the malware detection by now-classical M...
Malware Detection With CNNs For this new model, we are going to discover how to build a malware classifier with CNNs. But I bet you are wondering how we can do that while CNNs are taking images as inputs. The answer is really simple, the trick here is converting malware into an imag...