Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

被引:0
|
作者
Abiodun A. Solanke
Maria Angela Biasiotti
机构
[1] University of Bologna,CIRSFID Alma
[2] Institute of Legal Informatics and Judicial Systems (IGSG-CNR),AI
来源
关键词
Digital forensics; AI; Machine learning; Evaluation; Standardization; Optimization; Evidence mining;
D O I
暂无
中图分类号
学科分类号
摘要
The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.
引用
收藏
页码:143 / 161
页数:18
相关论文
共 50 条
  • [1] Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
    Solanke, Abiodun A.
    Biasiotti, Maria Angela
    KUNSTLICHE INTELLIGENZ, 2022, 36 (02): : 143 - 161
  • [2] Standardizing the construction of a digital forensics laboratory
    Chen, PS
    Tsai, LMF
    Chen, YC
    Yee, G
    FIRST INTERNATIONAL WORKSHOP ON SYSTEMATIC APPROACHES TO DIGITAL FORENSIC ENGINEERING, PROCEEDINGS, 2005, : 40 - 47
  • [3] Data Mining Techniques to Facilitate Digital Forensics Investigations
    Lopez, Erik Miranda
    Kim, Yoon Ho
    Park, Jong Hyuk
    ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING, 2017, 421 : 376 - 379
  • [4] AI-Enhanced Digital Forensics: Automated Techniques for Efficient Investigation and Evidence Collection
    Kadage, Anushka Deepak
    Nayak, Banoth Meghya
    Hingmire, Vishal Sharad
    Wanjale, Kirti
    Bogiri, Nagaraju
    Mandale, Prashant L.
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (01) : 211 - 229
  • [5] Evaluating Automated Facial Age Estimation Techniques for Digital Forensics
    Anda, Felix
    Lillis, David
    Nhien-An Le-Khac
    Scanlon, Mark
    2018 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2018), 2018, : 129 - 139
  • [6] Data mining approach for digital forensics task with deep learning techniques
    Barik, Lalbihari
    INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, 2020, 7 (05): : 56 - 65
  • [7] STandardizing digital evidence storage
    Commun ACM, 2006, 2 (67-68):
  • [8] Standardizing digital evidence storage
    Adelstein, F
    Carrier, B
    Casey, E
    Garfinkel, SL
    Hosmer, C
    Kornblum, J
    Lyle, J
    Rogers, M
    Turner, P
    COMMUNICATIONS OF THE ACM, 2006, 49 (02) : 67 - 68
  • [9] An Ontology for Digital Security and Digital Forensics Investigative Techniques
    Ellison, Dagney
    Venter, Hein
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY (ICCWS 2016), 2016, : 119 - 127
  • [10] An ontology for digital security and digital forensics investigative techniques
    University of Pretoria, Pretoria, South Africa
    Proc. Int. Conf. Cyber Warf. Secur., ICCWS, 1600, (119-127):