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 ...
Santoro A, Bartunov S, Botvinick M et al (2016) One-shot learning with memory-augmented neural networks. In: Proc. of the international conference on machine learning (ICML). Pp 1–13 Security H (2020) Malware and ransomware attack volume down due to more targeted attacks. In: Help Net...
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...
As a result, the stacked deep learning-based intrusion detection approach performs better than some cutting-edge shallow methods, such as the standalone deep learning models, naive Bayes, random forests, nearest neighbor, oneR, AdaBoost, and support vector machine. The research in Jmila and Hou...
the study evaluates public network-based data intrusion detection systems. It explores the application of deep learning techniques for IDS, assessing their performance based on criteria such as accuracy, recall, f1 score, false alarm rate, and detection rate. Another obstacle encountered within the re...
Currently, the premier objectives in malware detection are to identify the types of malware and raise the rates of malware detection. Techniques such as: machine learning, deep learning, big data, and cloud computing are all useful in this process. Recently, image-based or visualization-based mal...
This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In...
Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and ...
In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries ...
Detection methods include traditional techniques like signature-based, heuristic-based, and behavior-based detection, as well as advanced techniques like Machine Learning (ML) and Deep Learning (DL). Cloud-based detection offers greater computational power and more extensive databases (Aslan and Samet,...