The Management of Physical Evidence and Chain of Custody (CoC) in Digital Forensic Laboratory Storage

Efendi, Tino Feri

doi:10.29040/seocology.v1i01.3

Cited by 4 publications

(3 citation statements)

References 7 publications

(5 reference statements)

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“…Computer related crimes for example continue to grow, therefore a management information system for physical technology evidence with digital chain of custody documents and case data is needed. In many digital forensic laboratories, this system is deficient, and consequently courtroom outcomes can be affected [ 142 ].…”

Section: Operational Decisionsmentioning

confidence: 99%

Interpol review of forensic management, 2019–2022

McAndrew

Speaker

Houck

2023

Forensic Science International: Synergy

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Section: Operational Decisionsmentioning

confidence: 99%

Interpol review of forensic management, 2019–2022

McAndrew

Speaker

Houck

2023

Forensic Science International: Synergy

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“…Audio merupakan media penting salah satu barang bukti digital, sering kali bahwa audio menjadi peran krusial sebagai barang bukti yang diajukan dipersidangan [1]. Secara umum audio dihasilkan oleh getaran benda atau suatu benda yang berupa sinyal analog dengan amplitude yang berubah secara terus-menerus terhadap waktu dan berkaitan dengan indra pendengan yang merambat melalui udara [2].…”

Section: Pendahuluanunclassified

Perbandingan Tingkat Kemiripan Rekaman Suara Menggunakan Metode Itakura Saito Distance untuk Mendukung Analisa Audio Forensik

Wicaksono

Laksono²,

Hartiyani³

2023

komtekinfo

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Audio mengacu pada suara yang berbentuk sinyal listrik atau digital. Audio digital sering digunakan untuk merekam, menyimpan, dan mengirimkan audio, karena dapat dengan mudah diproses maupun dimanipulasi menggunakan komputer dan perangkat digital lainnya. Audio adalah media penting salah satu barang bukti digital, sering kali bahwa audio ini membawa peran krusial untuk mengungkap adanya kasus kejahatan sehingga diperlukan proses analisis forensik audio. Audio tersebut berisi suara rekaman seseorang yang memiliki karakter pengucapan kosakata yang berbeda-beda, pengucapan yang tidak jelas, dan memiliki banyak noise. Perlu adanya penanganan yang sesuai denganStandart Operational Procedure (SOP) audio forensik. Tahapan dalam melakukan audio forensics pada Digital Forensics Analyst Team Laboratorium Forensik (DFAT) PUSLABFOR terdiri dari 4 tahapan yakni Acquisition, Audio Enhancement, Decoding, dan Voice Recognition . Penelitian ini akan dibahasa meng0enai analisis audio menggunakan metode speech processing yaitu Itakura Saito Distance. Metode ini akan mengetahui tingkat akurasi suara dan argumentasi yang diperoleh dari proses Analisa, khususnya dalam penanganan audio forensik. Akurasi itu sendiri diukur dari nilai kedekatan frekuensi atauspektrum antara suara rekaman asli dengan rekaman suara pembanding. Pengujian dilakukan pada 4 rekaman suara asli ( unknown ) dan 4 rekaman suara pembanding ( known) menggunakan lebih dari 20 kosakata. Analisa menggunakan batas panjang waktu ( frame ) sebesar 100000 dari keseluruhan panjang waktu dari setiap suara yang menunjukaan akurasi perbandingan suara tertinggi 95%.

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“…Another line of research focuses on post-forensics, i.e., after the root cause is identified. The post-forensics system can prosecute the attacker in court by generating causal proof [19,24] and fingerprint the attack to prevent similar attacks in the future [36,53,57].…”

Section: Benefits Of Forensicsmentioning

confidence: 99%

Traceback of Data Poisoning Attacks in Neural Networks

Shan¹,

Bhagoji²,

Zheng³

et al. 2021

Preprint

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In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable complement to existing defenses, by tracing back a successful attack to its root cause, and offering a path forward for mitigation to prevent similar attacks in the future.In this paper, we describe our efforts in developing a forensic traceback tool for poison attacks on deep neural networks. We propose a novel iterative clustering and pruning solution that trims "innocent" training samples, until all that remains is the set of poisoned data responsible for the attack. Our method clusters training samples based on their impact on model parameters, then uses an efficient data unlearning method to prune innocent clusters. We empirically demonstrate the efficacy of our system on three types of dirty-label (backdoor) poison attacks and three types of clean-label poison attacks, across domains of computer vision and malware classification. Our system achieves over 98.4% precision and 96.8% recall across all attacks. We also show that our system is robust against four anti-forensics measures specifically designed to attack it.

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The Management of Physical Evidence and Chain of Custody (CoC) in Digital Forensic Laboratory Storage

Cited by 4 publications

References 7 publications

Interpol review of forensic management, 2019–2022

Interpol review of forensic management, 2019–2022

Perbandingan Tingkat Kemiripan Rekaman Suara Menggunakan Metode Itakura Saito Distance untuk Mendukung Analisa Audio Forensik

Traceback of Data Poisoning Attacks in Neural Networks

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