A toolkit for detecting and analyzing malicious software

被引:21
|
作者
Weber, M [1 ]
Schmid, M [1 ]
Schatz, M [1 ]
Geyer, D [1 ]
机构
[1] Cigital Inc, Dulles, VA 20166 USA
关键词
D O I
10.1109/CSAC.2002.1176314
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper we present PEAT The Portable Executable Analysis Toolkit. It is a software prototype designed to provide a selection of tools that an analyst may use in order to examine structural aspects of a Windows Portable Executable (PE) file, with the goal of determining whether malicious code has been inserted into an application after compilation. These tools rely on structural features of executables that are likely to indicate the presence of inserted malicious code. The underlying premise is that typical application programs are compiled into one binary, homogeneous from beginning to end with respect to certain structural features; any disruption of this homogeneity is a strong indicator that the binary has been tampered with. For example, it could now harbor a virus or a Trojan horse program. We present our investigation into structural feature analysis, the development of these ideas into the PEAT prototype, and results that illustrate PEAT's practical effectiveness.
引用
收藏
页码:423 / 431
页数:9
相关论文
共 50 条
  • [1] Analyzing and Detecting Malicious Flash Advertisements
    Ford, Sean
    Cova, Marco
    Kruegel, Christopher
    Vigna, Giovanni
    [J]. 25TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, 2009, : 363 - 372
  • [2] MalReG: Detecting and Analyzing Malicious Retweeter Groups
    Gupta, Sonu
    Kumaraguru, Ponnurangam
    Chakraborty, Tanmoy
    [J]. PROCEEDINGS OF THE 6TH ACM IKDD CODS AND 24TH COMAD, 2019, : 61 - 69
  • [3] Detecting Malicious Domains using the Splunk Machine Learning Toolkit
    Cersosimo, Michelle
    Lara, Adrian
    [J]. PROCEEDINGS OF THE IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2022, 2022,
  • [4] Analyzing Variability in Automation Software with the Variability Analysis Toolkit
    Schlie, Alexander
    Rosiak, Kamil
    Urbaniak, Oliver
    Schaefer, Ina
    Vogel-Heuser, Birgit
    [J]. 23RD INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE(SPLC 2019), VOL B, 2019, : 191 - 198
  • [5] Detecting malicious software by monitoring anomalous windows registry accesses
    Apap, R
    Honig, A
    Hershkop, S
    Eskin, E
    Stolfo, S
    [J]. RECENT ADVANCES IN INTRUSION DETECTION, PROCEEDINGS, 2002, 2516 : 36 - 53
  • [6] Detecting Malicious Websites in Depth through Analyzing Topics and Web-pages
    Wen, Senhao
    Zhao, Zhiyuan
    Yan, Hanbing
    [J]. ICCSP 2018: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON CRYPTOGRAPHY, SECURITY AND PRIVACY, 2018, : 128 - 133
  • [7] AntiMSA: A framework for detecting malicious software agents in online multiplayer games
    Oros, Bogdan-Ioan
    Bacu, Victor Ioan
    [J]. 2022 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, ICCP, 2022, : 283 - 288
  • [8] Machine Learning-Based System for Detecting Unseen Malicious Software
    Bisio, Federica
    Gastaldo, Paolo
    Meda, Claudia
    Nasta, Stefano
    Zunino, Rodolfo
    [J]. APPLICATIONS IN ELECTRONICS PERVADING INDUSTRY, ENVIRONMENT AND SOCIETY, APPLEPIES 2014, 2016, 351 : 9 - 15
  • [9] Detecting Malicious Switches for a Secure Software-defined Tactile Internet
    Yuan, Bin
    Lin, Chen
    Zou, Deqing
    Yang, Laurence Tianruo
    Jin, Hai
    [J]. ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 2021, 21 (04)
  • [10] Detection of malicious software by analyzing the behavioral artifacts using machine learning algorithms
    Singh, Jagsir
    Singh, Jaswinder
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2020, 121