This study employs a comprehensive approach, combining advanced machine learning and deep learning (ML & DL) techniques, to enhance ransomware detection. Utilizing LSTM networks for deep learning and some methods such as Random Forest (RF), XGBoost, and LightGBM for machine learning-based ...
Recently, long short-term memory- (LSTM-) based deep learning models have been introduced to detect DGA domains in real time using only domain names without feature extraction or additional information. In this paper, we propose an efficient DGA domain detection method based on bidirectional LSTM ...
Network-based intrusion detection system Deep learning Feature augmentation 1. Introduction The volume of illicit network traffic continues to grown dramatically, with the number of high-profile attacks including DDoS, botnet, and ransomware rising by over 45% annually [1], and the losses incurred ex...
In [42], the authors encompassed an in-depth analysis of current IoT security studies. The authors give particular attention to examining intrusion detection systems, emphasizing those that utilize deep learning techniques. Furthermore, they contribute a comprehensive classification system, aligning specifi...
Therefore, the current mainstream research direction is to detect encrypted malicious traffic without decryption based on different machine learning algorithms. The detection and identification of encrypted traffic based on non-decryption methods mainly rely on machine learning technology. Benefiting from the...
Other dependencies between other types of document elements can be either defined by rule-based mechanisms, learned through a machine learning technique, such as that used in sequence labeling to identify text data objects and non-text data objects, or those used in dependency parsing, or the ...
Deep learning LSTM based ransomware detection. In Proceedings of the 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 26–27 October 2017; pp. 442–446. [Google Scholar] Masum, M.; Faruk, M.J.H.; Shahriar, H.; Qian, K.; Lo, D.; Adnan,...
The survey in [64] uses LSTM for botnet detection in IoT. To manage authentication, access control, and intermediate attacks in IoT, the survey uses a deep learning-based deep Q-network to maintain security and privacy. The data in the healthcare field are increasing rapidly; therefore, it ...
Furthermore, CapsNet does not require transfer learning. Therefore, it is simple to train the model from scratch for Android malware detection. A robust work [36] suggested combining the developed RNN-LSTM classifier with the NAdam optimization technique. As a result, the accuracy of the ...
This section reviews the existing literature related to the proposed research. Specifically, studies that employ deep-learning- and machine-learning-based methods in the fields of IoT security and malware detection will be examined. The datasets used the performance metrics of the methods, and the ...