Results 61 to 70 of about 46,889 (202)
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned [PDF]
, 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
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
, 2019 Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social ...
Biggio, Battista +2 more
core +1 more source
Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF) [PDF]
, 2019 This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection.
Mazlan, Nurul Hidayah
core +1 more source
Security Toolbox for Detecting Novel and Sophisticated Android Malware
, 2015 This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project.
Deering, Tom +4 more
core +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
A Threat to Cyber Resilience : A Malware Rebirthing Botnet [PDF]
, 2011 This paper presents a threat to cyber resilience in the form of a conceptual model of a malware rebirthing botnet which can be used in a variety of scenarios. It can be used to collect existing malware and rebirth it with new functionality and signatures
Brand, Murray +2 more
core +2 more sources
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
, 2017 Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection.
Chandramohan, Mahinthan +3 more
core +1 more source
TTGNet-AMD: Android malware detection based on multi-modal feature fusion [PDF]
PeerJ Computer ScienceThe application of static features for Android malware detection has been extensively studied and developed. Existing methods exhibit limitations in both the completeness and discriminability of feature representation, which affects the enhancement of ...
Jiayin Feng +5 more
doaj +2 more sources
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
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
, 2017 In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA,
Chen, Aokun +7 more
core +1 more source