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Understanding Linux Malware
2018
2018 IEEE Symposium on Security and Privacy (SP)
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To the best of our knowledge, there is currently no comprehensive study attempting to characterize, analyze, and understand Linux malware. ...
However, the recent surge in adoption of embedded devices and the IoT revolution are rapidly changing the malware landscape. ...
However, no comprehensive study has been conducted to characterize, analyze, and understand the characteristics of Linux-based malware. ...
doi:10.1109/sp.2018.00054
dblp:conf/sp/CozziGFB18
fatcat:ygxsry7ohjgahdamsblixdx6ru
A Survey on Malware and Malware Detection Systems
2013
International Journal of Computer Applications
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Over the last decades, there were lots of studies made on malware and their countermeasures. The most recent reports emphasize that the invention of malicious software is rapidly increasing. ...
In this paper, a detailed review has been conducted on the current situation of malware infection and the work done to improve anti-malware or malware detection systems. ...
Examples do include Trend Micro's Rootkit Buster and rkhunter tool to scan for rootkits on an Ubuntu Linux computer. ...
doi:10.5120/11480-7108
fatcat:wnsmrns6wnhddgurij7s3zmhti
SoK: Cryptojacking Malware
[article]
2021
arXiv
pre-print
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Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. ...
critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful ...
National Science Foundation (NSF) (Awards: NSF-CAREER CNS-1453647, NSF-1663051, NSF-CNS-1718116, NSF-CNS-1703454), and ONR under the "In Situ Malware" project, and CyberFlorida Capacity Building Program ...
arXiv:2103.03851v2
fatcat:nz5wblhw5jd7nju64hewsik3sy
Malware Visualization Techniques
2020
International Journal of Applied Mathematics Electronics and Computers
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The analytical study is based mainly on the PSs to achieve the goals. ...
This paper aims to provide insights into the malware visualization techniques and its applications, most common malware types and the extracted features that used to identify the malware are demonstrated ...
recently visualize malware behaviors [46] [47] [48] . ...
doi:10.18100/ijamec.526813
fatcat:54lixfrqxrdrlnnqmbtfpvmdve
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
2020
Zenodo
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The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. ...
This survey paper gives an overview of the malware detection and analysis techniques and tools. ...
REMnux is focused on Ubuntu and integrates several resources into one for quickly analyzing malware based on Windows and Linux. The cornerstone of the project is the Ubuntu based REMnux Linux system. ...
doi:10.5281/zenodo.3738919
fatcat:ks6ral6rqndpbguru4fntb464e
Dissecting Android Malware: Characterization and Evolution
2012
2012 IEEE Symposium on Security and Privacy
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In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. ...
The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. ...
doi:10.1109/sp.2012.16
dblp:conf/sp/ZhouJ12
fatcat:6mjdjwcegvf3na7sxldesdt5wu
A Survey on Cross-Architectural IoT Malware Threat Hunting
2021
IEEE Access
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Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. ...
In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. ...
Recent trends discussed in the introduction section, indicate that Ransomware attacks will continue to grow towards attacking small to medium scale business that is more vulnerable, and public organizations ...
doi:10.1109/access.2021.3091427
fatcat:tsfno6qdirhbdasj3fzrqqqzm4
Deep Learning for Android Malware Defenses: a Systematic Literature Review
[article]
2022
arXiv
pre-print
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This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses. ...
However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual ...
works from different aspects and provided recommendations based on findings to support further research in this domain. • We provided a trend analysis to identify potential future trends for the research ...
arXiv:2103.05292v2
fatcat:qruddq4gknfq7jx5wyrk5qu2eu
Measuring similarity of malware behavior
2009
2009 IEEE 34th Conference on Local Computer Networks
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Based on our results we identify a most appropriate distance measure for grouping malware samples based on similar behavior. ...
We focus on behavioral features of malware and compare and experimentally evaluate different distance measures for malware behavior. ...
the time command of Linux systems. ...
doi:10.1109/lcn.2009.5355037
dblp:conf/lcn/ApelBM09
fatcat:doqqvab6erfahonpzgiugo7jge
PMDS: Permission-Based Malware Detection System
[chapter]
2014
Lecture Notes in Computer Science
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Based on analysis of 2950 samples of benign and malicious Android applications, we propose a novel Android malware detection technique called Permission-based Malware Detection Systems (PMDS). ...
By design, PMDS has the potential to detect previously unknown, and zero-day or next-generation malware. ...
Our work was supported in part by grants from Emory University, and grantof-excellence #120032011 from the Icelandic Research Fund. ...
doi:10.1007/978-3-319-13841-1_19
fatcat:ygepommeszcetfqqxsy44wk7ci
A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices
2019
Security and Communication Networks
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We proposed a framework to classify malware in IoT devices by using MIPS-based system behavior (system call—syscall) obtained from our F-Sandbox passive process and machine learning techniques. ...
IoT devices use the MIPS architecture with a large proportion running on embedded Linux operating systems, but the automatic analysis of IoT malware has not been resolved. ...
In theory, the study has shown many characteristics of the malware type in MIPS ELF, finding the most suitable methods and parameters for detecting MIPS ELF malware based on machine learning methods. ...
doi:10.1155/2019/4073940
fatcat:llefjvtxc5be3h4yc5vemkfcga
A Study of Android Malware Detection Techniques and Machine Learning
2016
Midwest Artificial Intelligence and Cognitive Science Conference
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So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. ...
Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. ...
We aim to give a brief approach on counteracting the update attack with the survey on recent trends on Malware detection. ...
dblp:conf/maics/BaskaranR16
fatcat:xcpoc5f63nehjpoelaglakisga
Mining Patterns of Sequential Malicious APIs to Detect Malware
2018
International journal of network security and its applications
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Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples. ...
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. ...
A recent study [14] employs the deep neural network to acquire the representative and distinguishing API call patterns of malware families. ...
doi:10.5121/ijnsa.2018.10401
fatcat:x5mrl6enzrd2raql3fol3x5fei
N-gram Opcode Analysis for Android Malware Detection
[article]
2016
arXiv
pre-print
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Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. ...
Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. ...
We also some provide empirical findings that correlate with trends observed in the overall performance of the n-gram opcodes on the experimental dataset. ...
arXiv:1612.01445v1
fatcat:wphxwztxr5hexjygbmz232wgaa
The MalSource Dataset: Quantifying Complexity and Code Reuse in Malware Development
[article]
2018
arXiv
pre-print
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During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. ...
We detect a significant number of code clones across malware families and report which features and functionalities are more commonly shared. ...
We did find up to 210 relevant code clones (larger than 5 lines) in gcc, the Linux kernel, Git, and clamAV. ...
arXiv:1811.06888v1
fatcat:3vjjtk2eqngvfgro2btqepvwai
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