Results 41 to 50 of about 2,609 (164)

Malware Classification Using LSTMs

, 2021
Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models.
openaire   +2 more sources

Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
Mobile devices have been spreading at great rate in recent years. Not only smartphone, but also tablets and IoT devices, are gaining an increasingly place in our everyday lives. This is the reason why attackers are developing more and more aggressive techniques with the aim to exfiltrate our sensitive and private information.
Mercaldo F., Saracino A.
openaire   +4 more sources

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, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

Exploring network-based malware classification [PDF]

2011 6th International Conference on Malicious and Unwanted Software, 2011
Over the last years, dynamic and static malware analysis techniques have made significant progress. Majority of the existing analysis systems primarily focus on internal host activity. In spite of the importance of network activity, only a limited set of analysis tools have recently started taking it into account.
Natalia Stakhanova, Mathieu Couture, Ali A. Ghorbani   +2 more
openaire   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

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, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

IEEE Access, 2023
Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim, Munya A. Arasi, Majdi Khalid, Sumayh S. Aljameel, Radwa Marzouk, Heba Mohsen, Ishfaq Yaseen, Sara Saadeldeen Ibrahim   +7 more
doaj   +1 more source

Microsoft Malware Classification Challenge

, 2018
The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour.
Ronen, Royi, Radu, Marian, Feuerstein, Corina, Yom-Tov, Elad, Ahmadi, Mansour   +4 more
openaire   +2 more sources

AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim, Lal Hussain, Mohammed A. Alqarni, Faisal S. Alsubaei, Rasha Jamal Atwah   +4 more
wiley   +1 more source

CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments

Sensors
Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This
Mainak Basak, Myung-Mook Han
doaj   +1 more source

Similarity-Based Malware Classification Using Graph Neural Networks

Applied Sciences, 2022
This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the ...
Yu-Hung Chen, Jiann-Liang Chen, Ren-Feng Deng   +2 more
doaj   +1 more source