Results 41 to 50 of about 3,469,712 (175)
A Malware Detection Approach Using Autoencoder in Deep Learning
IEEE Access, 2022 Today, in the field of malware detection, the expanding limitations of traditional detection methods and the increasing accuracy of detection methods designed on the basis of artificial intelligence algorithms are driving research findings in this area ...
Xiaofei Xing +4 more
doaj +1 more source
VAE‐Based SPC for Botnet Detection in IoT Systems
Quality and Reliability Engineering International, EarlyView.ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has created new security risks, particularly botnet infections. This paper proposes an unsupervised monitoring framework that combines a β$\beta$‐variational autoencoder (VAE) with multivariate statistical process control (SPC) for botnet detection in IoT networks.
Jorge Proaño +3 more
wiley +1 more source
Semantics-Aware Malware Detection
, 2018 A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware.
Sanjit A Seshia (5358551) +4 more
core +2 more sources
Normal‐Mode‐Aware Augmented Kernel One‐Class SVM for Heterogeneous Normality Modeling
IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.Despite the rapid advances in anomaly detection, many existing methods assume that normal data are homogeneous and can be modeled by a single one‐class boundary. However, in many practical scenarios, normal data are heterogeneous and consist of multiple latent modes, making conventional latent‐space kernels insufficient to characterize normality.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Devious chatbots - interactive malware with a plot [PDF]
, 2009 Many social robots in the forms of conversation agents or Chatbots have been put to practical use in recent years. Their typical roles are online help or acting as a cyber agent representing an organisation.
Wong, K.W., Fung, C.C., Pan, J.Y.
core
Research on Application of Attention-CNN in Malware Detection
Jisuanji kexue yu tansuo, 2021 The attack of malware has become one of the most major threats to the Internet. What??s more, the existing malware data are huge and have multiple features. In order to extract the characteristics better and master the behaviors of malware, Attention-CNN
MA Dan, WAN Liang, CHENG Qiqin, SUN Zhiqiang
doaj +1 more source
Graph neural network‐based attack prediction for communication‐based train control systems
CAAI Transactions on Intelligence Technology, EarlyView.Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao +3 more
wiley +1 more source
Boutique Malware – Custom made for e-business [PDF]
, 2010 Malware are typically known through extensive publicity in the media when incidents such as infection by Conficker on the computers around the globe. Such Malware infects all who are vulnerable to its bite.
Fung, C.C., Pan, J.Y.
core
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source