Support Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
Zolotukhin, M., & Hämäläinen, T. (2013). Support Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware. In IEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big Data (pp. 211-216). IEEE. IEEE Global Telecommunications Conference. https://doi.org/10.1109/GLOCOMW.2013.6824988
Julkaistu sarjassa
IEEE Global Telecommunications ConferencePäivämäärä
2013Tekijänoikeudet
© Copyright 2014 IEEE. Article's final and definitive form has been published by IEEE.
Abstract.
—In the modern world, a rapid growth of mali-
cious software production has become one of the most signifi-
cant threats to the network security. Unfortunately, wides
pread
signature-based anti-malware strategies can not help to de
tect
malware unseen previously nor deal with code obfuscation te
ch-
niques employed by malware designers. In our study, the prob
lem
of malware detection and classification is solved by applyin
g a
data-mining-based approach that relies on supervised mach
ine-
learning. Executable files are presented in the form of byte a
nd
opcode sequences and n-gram models are employed to extract
essential features from these sequences. Feature vectors o
btained
are classified with the help of support vector classifiers int
egrated
with a genetic algorithm used to select the most essential fe
atures,
and a game-theory approach is applied to combine the classifi
ers
together. The proposed algorithm, ZSGSVM, is tested by usin
g a
set of byte and opcode sequences obtained from a set containi
ng
executable files of benign software and malware. As a result,
almost all malicious files are detected while the number of fa
lse
alarms remains very low.
...
Julkaisija
IEEEEmojulkaisun ISBN
978-1-4799-2851-4Konferenssi
IEEE Globecom Workshops : International Workshop on Security and Privacy in Big DataKuuluu julkaisuun
IEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big DataISSN Hae Julkaisufoorumista
1930-529XAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/23787836
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Aberrant brain functional networks in type 2 diabetes mellitus : A graph theoretical and support-vector machine approach
Lin, Lin; Zhang, Jindi; Liu, Yutong; Hao, Xinyu; Shen, Jing; Yu, Yang; Xu, Huashuai; Cong, Fengyu; Li, Huanjie; Wu, Jianlin (Frontiers Media SA, 2022)Objective: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and ... -
DNS Tunneling Detection Techniques – Classification, and Theoretical Comparison in Case of a Real APT Campaign
Nuojua, Viivi; David, Gil; Hämäläinen, Timo (Springer International Publishing, 2017)Domain Name System (DNS) plays an important role as a translation protocol in everyday use of the Internet. The purpose of DNS is to translate domain names into IP addresses and vice versa. However, its simple architecture can ... -
A Network-Based Framework for Mobile Threat Detection
Kumar, Sanjay; Viinikainen, Ari; Hämäläinen, Timo (IEEE, 2018)Mobile malware attacks increased three folds in the past few years and continued to expand with the growing number of mobile users. Adversary uses a variety of evasion techniques to avoid detection by traditional systems, ... -
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets
Vahdani Amoli, Payam (University of Jyväskylä, 2015)Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast ... -
Anomaly Detection and Classification of Household Electricity Data : A Time Window and Multilayer Hierarchical Network Approach
Zhao, Qiang; Chang, Zheng; Min, Geyong (Institute of Electrical and Electronics Engineers (IEEE), 2022)With the increasing popularity of the smart grid, huge volumes of data are gathered from numerous sensors. How to classify, store, and analyze massive datasets to facilitate the development of the smart grid has recently ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.