Results 81 to 90 of about 134,256 (265)

Android Malware Clustering through Malicious Payload Mining

open access: yes, 2017
Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party ...
I Santos   +7 more
core   +1 more source

Semantics-aware malware detection [PDF]

open access: yes2005 IEEE Symposium on Security and Privacy (S&P'05), 2005
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. Malware detectors that use a pattern-matching approach (such as commercial virus scanners) are susceptible to obfuscations used by hackers.
Christodorescu, Mihai   +4 more
openaire   +1 more source

Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned [PDF]

open access: yes, 2018
Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform.
Stringhini, Gianluca   +1 more
core  

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

open access: yes, 2018
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the ...
G Tandon   +4 more
core   +1 more source

Multifamily malware models

open access: yesJournal of Computer Virology and Hacking Techniques, 2020
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the
Basole, Samanvitha   +2 more
openaire   +3 more sources

Graph neural network‐based attack prediction for communication‐based train control systems

open access: yesCAAI 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

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

open access: yesCAAI 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

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

open access: yesCAAI 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   +4 more
wiley   +1 more source

Dynamic Analysis of Executables to Detect and Characterize Malware

open access: yes, 2018
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating ...
Aimone, James B.   +6 more
core   +1 more source

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