IDENTIFYING VOLATILE DATA FROM MULTIPLE MEMORY DUMPS IN LIVE FORENSICS

被引:0
|
作者
Law, Frank [1 ]
Chan, Patrick [1 ]
Yiu, Siu-Ming [1 ]
Tang, Benjamin [1 ]
Lai, Pierre [1 ]
Chow, Kam-Pui [1 ]
Ieong, Ricci [1 ]
Kwan, Michael [1 ]
Hon, Wing-Kai [2 ]
Hui, Lucas [1 ]
机构
[1] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
[2] Natl Tsing Hua Univ, Hsinchu, Taiwan
来源
关键词
Live forensics; volatile data; memory analysis;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the core components of live forensics is to collect and analyze volatile memory data. Since the dynamic analysis of memory is not possible, most live forensic approaches focus on analyzing a single snapshot of a memory dump. Analyzing a single memory dump raises questions about evidence reliability; consequently, a natural extension is to study data from multiple memory dumps. Also important is the need to differentiate static data from dynamic data in the memory dumps; this enables investigators to link evidence based on memory structures and to determine if the evidence is found in a consistent area or a dynamic memory buffer, providing greater confidence in the reliability of the evidence. This paper proposes an indexing data structure for analyzing pages from multiple memory dumps in order to identify static and dynamic pages.
引用
收藏
页码:185 / +
页数:3
相关论文
共 50 条
  • [41] Forensics and Anti-Forensics of a NAND Flash Memory: From a Copy-Back Program Perspective
    Ahn, Na Young
    Lee, Dong Hoon
    IEEE ACCESS, 2021, 9 : 14130 - 14137
  • [42] Live Data Mining Concerning Social Networking Forensics Based on a Facebook Session Through Aggregation of Social Data
    Chu, Hai-Cheng
    Deng, Der-Jiunn
    Park, Jong Hyuk
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2011, 29 (07) : 1368 - 1376
  • [43] FATKit: A framework for the extraction and analysis of digital forensic data from volatile system memory
    Petroni, Nick L., Jr.
    Walters, Aaron
    Fraser, Timothy
    Arbaugh, William A.
    DIGITAL INVESTIGATION, 2006, 3 (04) : 197 - 210
  • [44] Non-Volatile Memory versus Big Data
    Kuo, Tei-Wei
    2016 INTERNATIONAL SYMPOSIUM ON VLSI DESIGN, AUTOMATION AND TEST (VLSI-DAT), 2016,
  • [45] Non-Volatile Memory Technology for Data Age
    Ishimaru, Kazunari
    2018 14TH IEEE INTERNATIONAL CONFERENCE ON SOLID-STATE AND INTEGRATED CIRCUIT TECHNOLOGY (ICSICT), 2018, : 1215 - 1218
  • [46] Data Management on Non-Volatile Memory: A Perspective
    Philipp Götze
    Alexander van Renen
    Lucas Lersch
    Viktor Leis
    Ismail Oukid
    Datenbank-Spektrum, 2018, 18 (3) : 171 - 182
  • [47] A study on vulnerability of the WICKR login system in windows from a live forensics perspective
    Kim, Giyoon
    Kang, Soojin
    Hur, Uk
    Kim, Jongsung
    COMPUTERS & SECURITY, 2024, 139
  • [48] Deriving Cse-specific Live Forensics Investigation Procedures from FORZA
    Ieong, Ricci
    Leung, Hc
    APPLIED COMPUTING 2007, VOL 1 AND 2, 2007, : 175 - 180
  • [49] Development of a deep stacked ensemble with process based volatile memory forensics for platform independent malware detection and classification
    Naeem, Hamad
    Dong, Shi
    Falana, Olorunjube James
    Ullah, Farhan
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 223
  • [50] Identifying multiple cluster structures in a data matrix
    Soffritti, G
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2003, 32 (04) : 1151 - 1177