The introduced approaches have been evaluated over a series of experiments using two large real Twitter datasets and demonstrate valuable advantages over other existing techniques targeting the identification of malicious users in social media.doi:10.1016/j.asoc.2021.107360Loukas IliasIoanna RoussakiApplied Soft Computing
Detecting Malware refers to the process of identifying and analyzing malicious software within digital systems using techniques such as signature-based, heuristic-based, and Deep Learning-based methods to ensure security and prevent harm to the digital world. AI generated definition based on: Computer ...
To spot the presence of an application/icon hiding malicious content, end nodes and the server collaborate to find the optimal Deep Neural Network (DNN)-based model via a federated approach. Such strategies are mainly used to avoid moving raw data from end nodes to the server, take advantage...
We used the Word2vec embedding of the PowerShell language presented in the previous section to train deep learning models capable of detecting malicious PowerShell scripts. The classification model is trained and validated using a large dataset of PowerShell scripts that are labeled “c...
containing real, up-to-date network traffic from malicious and benign android applications. The goal of this project is to employ deep learning techniques, in conjunction with the CICMalAnal2017 dataset, to accurately identify the intent of a given application through collected network traffic data....
In this paper, a hybrid convolutional neural network (CNN) and long-short term memory (LSTM)-based deep learning model (CNN–LSTM) is proposed for detecting shilling attack in recommender systems. This deep learning model utilizes the transformed network architecture for exploiting the deep-level ...
The Trend Micro Deep Discovery™ solution has an email inspection layer that can protect enterprises by detecting malicious attachments and URLs. Deep Discovery can detect the remote scripts even if they are not being downloaded to the physical endpoint. It is ...
The Trend Micro Deep Discovery™ solution has an email inspection layer that can protect enterprises by detecting malicious attachments and URLs. Deep Discovery can detect the remote scripts even if they are not being downloaded to the physical endpoint. It is ...
In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators (URLs). Three categories of features, both ML and Deep Learning (DL) algorithms and a ranking schema are included in the proposed framework. We apply frequency and ...
In the first stage, the required benign and malicious data were gathered. Subsequently, labeled data was fed in the DL module, which was composed of an embedding layer followed by a 1-dimensional CNN that was then forwarded as input to an LSTM recurrent neural network. The trained model was...