Results 51 to 60 of about 97,198 (223)
Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection
International Journal of Computational Intelligence Systems, 2018 Nowadays, security of the computer systems has become a major concern of security experts. In spite of many antivirus and malware detection systems, the number of malware incidents are increasing day by day.
Hongwei Zhao +3 more
doaj +1 more source
Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm
IEEE Access, 2020 Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems.
S. Abijah Roseline +3 more
doaj +1 more source
Mission Aware Cyber‐Physical Security
Systems Engineering, EarlyView.ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis +3 more
wiley +1 more source
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
, 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
Malware vs Anti-Malware Battle - Gotta Evade ‘em All! [PDF]
2020 IEEE Symposium on Visualization for Cyber Security (VizSec), 2020 The landscape of malware development is ever-changing, creating a constant catch-up contest between the defenders and the adversaries. One of the methodologies that has the potential to pose a significant threat to systems is malware evasion. This is where malware tries to determine whether it is run in a controlled environment, such as a sandbox ...
Chaffey, E., Sgandurra, D.
openaire +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
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
Semantics-aware malware detection [PDF]
2005 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
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
Applied Sciences, 2020 Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj +1 more source