EHAVIOR ANALYSIS OF MALWARE IN INSTITUT TEKNOLOGI SEPULUH NOPEMBER SURABAYA,INDONESIA

Ejaz Karim

Abstract


An almost incomprehensible amount of data and information is stored on millions and millions of computers worldwide. To be able to defend against the threat imposed by malware we need to understand both how and why the malware exists. This document presents the nature of malware today and outlines some analytical techniques used by security experts. Furthermore, a process foranalyzing malware samples with the goal of discovering the behavior and techniques used by the samples is presented. An analysis is performed on malware samples, disclosing behavior, location, encryption techniques. So these samples are being analyzed by tools named as Threat Expert and Anubis. The samples will be also analyzed on real system and results will be compared with Anubis  and Threat Expert. This research is expected to describe and explain how malware and other malicious software spread through internet, network and computers. The encryption techniques used by the malware programmers to encrypt malware and to hide these viruses from the users. The damage done by the malicious code to the target machine, network or computers. It should also show the techniques used by the programmers and malware vendor to spread through different platforms. The results are also expected to show that which platforms are being attacked by the malware and why.

Full Text:

PDF

References


Jeremy Paquette, A History of Viruses, March 2008, http://www.securityfocus.com/infocus/1286.

Ed Skoudis and Lenny Zeltser. Malware: Fighting Malicious Code. Prentice Hall, 2003.

Niels Provos, Panayiotis Mavrommatis, Moheeb Abu Rajab, and Fabian Monrose. All Your iFRAMEs Point to Us. Google Technical Report, 2008.

Brian Krebs, Washigton post: Hundreds of thousands of microsoft web servers hacked, April 2008,

http://blog.washingtonpost.com/securityfix/2008/04/hundreds_of_thousands_of_micro_1.html

A framework for behavior-based malware analysis in the cloud Lorenzo Martignoni, Roberto Paleari , and Danilo Bruschi.

An Effective Framework of Behavior Detection-Advanced Static Analysis for Malware Detection by Maya Louk, Hyotaek Lim and HoonJae Lee , Mohammed Atiquzzaman.

Crowdroid: Behavior-Based Malware Detection System for Android by Iker Burguera and Urko Zurutuza and Simin Nadjm-Tehrani.

Automatic Analysis of Malware Behavior using Machine Learning, Konrad Rieck, Philipp Trinius, Carsten Willems, and Thorsten Holz.

Towards Automated Malware Behavioral Analysis and Profiling for Digital Forensic Investigation Purposes, Ahmed F.Shosha, Joshua I. James, Alan Hannaway, Chen-Ching Liu and Pavel Gladyshev.

Ether Malware Analysis via Hardware Virtualization Extensions by Artem Dinaburg, Paul Royal, Monirul Sharif.

AMAL: High-Fidelity, Behavior-based Automated Malware Analysis and Classification by Aziz Mohaisen.

Behavioral Analysis of Android Applications Using Automated Instrumentation by Mohammad Karami, Mohamed Elsabagh, Parnian Najafiborazjani, and Angelos.

Ontology-based Mobile Malware Behavioral Analysis by Hsiu-Sen Chiang, Woei-Jiunn Tsaur.

Automated Classification and Analysis of Internet Malware by Michael Bailey, Jon Oberheide, Jon Andersen, Z. Morley Mao, Farnam Jahanian, and Jose Nazario .

A Comparative Assessment of Malware Classification using Binary Texture Analysis and Dynamic Analysis by Lakshmanan Nataraj University of California, Santa Barbara, USA.

Malware Behavior Feature Extraction Based on Web Information Extraction by Binlin Cheng, Jianming Fu and Ya Liu, Siyang Xiong.

Automatic Behavior Bases Analysis and Classification System for Malware Detection Jaime Devesa, Igor Santos, Xabier Cantero, Yoseba K. Penya and Pablo G. Bringas.

Malbehave: Classifying Malware by Observed Behavior by Connor Gilbert, Bryce CronkiteRatcliff, and Jason Franklin.

Behavioral Analysis on IPv4 Malware on different platforms in IPv6 Network Environment by Zulkiflee M., Azirah S.A., Haniza N., Zakiah A., Shahrin S.

BareBox: Efficient Malware Analysis on Bare-Metal. Dhilung Kirat, Giovanni Vigna, Christopher Kruegel University of California, Santa Barbara.

Behavior-based Spyware Detection Engin Kirda and Christopher Kruegel Secure Systems Lab Technical University Vienna, Greg Banks, Giovanni Vigna, and Richard A. Kemmerer.

Malware behavior analysis Gérard Wagener Radu State Alexandre Dulaunoy.

Behavior Classification based Self-learning Mobile Malware Detection by Dai-Fei Guo, AiFen Sui, Yi-Jie Shi, Jian-Jun Hu, Guan-Zhou Lin and Tao Guo.

RBACS: Rootkit Behavioral Analysis and Classification System by Desmond Lobo, Paul Watters and Xinwen Wu




DOI: https://doi.org/10.22219/sentra.v0i1.1976

Refbacks

  • There are currently no refbacks.


Seketariat

Fakultas Teknik

Universitas Muhammadiyah Malang Kampus III

Jl. Raya Tlogomas 246 Malang, 65144