Results 51 to 60 of about 29,821 (199)
Classification of malware based on string and function feature selection
, 2010 Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a ...
Batten, Lynn +3 more
core +1 more source
Energy Science &Engineering, EarlyView.By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali +6 more
wiley +1 more source
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
A Classification System for Visualized Malware Based on Multiple Autoencoder Models
IEEE Access, 2021 In this paper, we propose a classification system that uses multiple autoencoder models for identifying malware images. It is crucial to accurately classify malware before we can deploy appropriate countermeasures to prevent them from spreading.
Jongkwan Lee, Jongdeog Lee
doaj +1 more source
Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles
Software: Practice and Experience, EarlyView.ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom +2 more
wiley +1 more source
An Efficient Boosting-Based Windows Malware Family Classification System Using Multi-Features Fusion
Applied Sciences, 2023 In previous years, cybercriminals have utilized various strategies to evade identification, including obfuscation, confusion, and polymorphism technology, resulting in an exponential increase in the amount of malware that poses a serious threat to ...
Zhiguo Chen, Xuanyu Ren
doaj +1 more source
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
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 +2 more
openaire +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 +4 more
openaire +2 more sources