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AutoProfile: Towards Automated Profile Generation for Memory Analysis

Despite a considerable number of approaches that have been proposed to protect computer systems, cyber-criminal activities are on the rise and forensic analysis of compromised machines and seized devices is becoming essential in computer security. This article focuses on memory forensics, a branch of digital forensics that extract artifacts from the volatile memory. In particular, this article looks at a key ingredient required by memory forensics frameworks: a precise model of the OS kernel under analysis, also known as profile . By using the information stored in the profile, memory forensics tools are able to bridge the semantic gap and interpret raw bytes to extract evidences from a memory dump. A big problem with profile-based solutions is that custom profiles must be created for each and every system under analysis. This is especially problematic for Linux systems, because profiles are not generic : they are strictly tied to a specific kernel version and to the configuration used to build the kernel. Failing to create a valid profile means that an analyst cannot unleash the true power of memory forensics and is limited to primitive carving strategies. For this reason, in this article we present a novel approach that combines source code and binary analysis techniques to automatically generate a profile from a memory dump, without relying on any non-public information. Our experiments show that this is a viable solution and that profiles reconstructed by our framework can be used to run many plugins, which are essential for a successful forensics investigation.

Cyber Security and Digital Forensics

An insight into digital forensics: history, frameworks, types and tools, digital forensics, digital forensics as a service: analysis for forensic knowledge, roadmap of digital forensics investigation process with discovery of tools, wake up digital forensics' community and help combating ransomware, privacy of web browsers: a challenge in digital forensics, the analysis and implication of data deduplication in digital forensics, digital forensics investigation on xiaomi smart router using sni iso/iec 27037:2014 and nist sp 800-86 framework, export citation format, share document.

Research on the Key Technology of Secure Computer Forensics

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Research on Computer Forensics Technology Based on Data Recovery

Ruibo Duan 1 and Xiong Zhang 2

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1648 , Information technology Citation Ruibo Duan and Xiong Zhang 2020 J. Phys.: Conf. Ser. 1648 032025 DOI 10.1088/1742-6596/1648/3/032025

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1 Yunnan College of Foreign Affairs & Foreign Language, China, 651700

2 Songming County Public Security Bureau, China, 651700

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With the rapid development of information technology, fundamental changes have taken place in the way people work. However, computer crime has also become the main type of cases in the Internet era. Therefore, computer forensics technology has become an important research content of computer crime evidence collection. Firstly, this paper analyzes the relationship between computer forensics and data recovery. Then, this paper analyzes the steps of computer forensics. Finally, this paper analyzes the application of anti-forensics technology and computer forensics technology.

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Implications of Forensic Investigation in Dark Web

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• First Online: 27 September 2023
• Cite this conference paper

• Ngaira Mandela 13 ,
• Amir Aboubakar Shaker Mahmoud 13 &
• Animesh Agrawal 13  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1893))

Included in the following conference series:

• International Conference on Communication, Networks and Computing

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The dark web has become a critical area of concern for law enforcement agencies and cybersecurity experts due to its association with illegal activities such as drug trafficking, money laundering, and cybercrime. Dark web forensics involves the collection, analysis, and preservation of digital evidence from the dark web, which presents several challenges such as anonymity, encryption, and the use of cryptocurrencies. This paper presents a comprehensive review of the current state of research on dark web forensics, with an emphasis on the methods, tools, and challenges associated with this field. The continuous improvement of darknet forensics technology has important practical significance for law enforcement agencies to combat darknet crimes.

• Dark Web Forensics
• Digital Forensics
• Cryptocurrencies

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National Forensic Sciences University, Gandhinagar, India

Ngaira Mandela, Amir Aboubakar Shaker Mahmoud & Animesh Agrawal

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Correspondence to Ngaira Mandela .

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ITM University, Gwalior, India

Ranjeet Singh Tomar

IIIT-Allahabad, Allahabad, Uttar Pradesh, India

Shekhar Verma

IIIT Lucknow, Lucknow, India

Brijesh Kumar Chaurasia

IIIT-Allahabad, Allahabad, India

Vrijendra Singh

Deakin University, Burwood, VIC, Australia

Jemal H. Abawajy

ITM University, Gwalior, Madhya Pradesh, India

Shyam Akashe

National Chung Cheng University, Chiayi, Taiwan

Pao-Ann Hsiung

Aarhus University, Herning, Denmark

Ramjee Prasad

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Mandela, N., Mahmoud, A.A.S., Agrawal, A. (2023). Implications of Forensic Investigation in Dark Web. In: Tomar, R.S., et al. Communication, Networks and Computing. CNC 2022. Communications in Computer and Information Science, vol 1893. Springer, Cham. https://doi.org/10.1007/978-3-031-43140-1_10

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DOI : https://doi.org/10.1007/978-3-031-43140-1_10

Published : 27 September 2023

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Computer Science > Computation and Language

Title: mapping the increasing use of llms in scientific papers.

Abstract: Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

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