Secure Evidence Collection and Storage for Cloud Accountability Audits

Forensic Readiness and eDiscovery

Advances in Digital Crime, Forensics, and Cyber Terrorism - Handbook of Research on Digital Crime, Cyberspace Security, and Information Assurance ◽

10.4018/978-1-4666-6324-4.ch012 ◽

2015 ◽

pp. 178-192

Author(s):

Dauda Sule

Keyword(s):

Information Security ◽

Digital Evidence ◽

Efficient Manner ◽

Digital Crime ◽

Evidence Collection ◽

Court Proceedings ◽

Assess Quality ◽

And Storage

In a bid to discover, uncover, and stamp out digital crime while ensuring information security and assurance, there is a need to investigate the crime once it has taken place. This will help trace the criminals and also secure an organization against future attacks. Forensic readiness entails that an organization be at alert in terms of digital evidence collection and storage – that is, collecting and storing such evidence constantly in a forensically sound manner, not just when the need for such evidence arises. In the event litigation arises or is anticipated, digital evidence may need to be reviewed by the opposing parties prior to court proceedings to assess quality of the evidence; this is eDiscovery. This chapter explores eDiscovery and forensic readiness. Digital evidence for eDiscovery needs to be forensically sound and provided in a timely and efficient manner - forensic readiness helps to ensure this. This chapter seeks to establish how forensic readiness is relevant to the eDiscovery process.

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Forensic Readiness for Enhanced eDiscovery

Handbook of Research on Cyber Crime and Information Privacy - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-5728-0.ch013 ◽

2021 ◽

pp. 236-255

Author(s):

Dauda Sule

Keyword(s):

Information Security ◽

Digital Evidence ◽

Timely Manner ◽

Previous Edition ◽

Digital Crime ◽

Evidence Collection ◽

Court Proceedings ◽

Assess Quality ◽

And Storage

To discover, uncover, and stamp out digital crime while ensuring information security and assurance, there is a need to investigate the crime once it has occurred. This will help trace the criminals and also secure an organization against future attacks. Forensic readiness entails organizations being at alert as per digital evidence collection and storage – that is collecting and storing such evidence constantly in a forensically sound manner, not just when the need for such evidence arises. In the event litigation arises or is anticipated, digital evidence may need to be reviewed by the opposing parties prior to court proceedings to assess quality of the evidence; this is eDiscovery. Digital evidence for eDiscovery needs to be forensically sound and provided in an efficient timely manner – forensic readiness helps to ensure this. This chapter is an update on the chapter on “Forensic Readiness and eDiscovery” in the previous edition and still seeks to establish how forensic readiness is relevant to the eDiscovery process, taking into consideration current developments in the field.

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An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Scientific Programming ◽

10.1155/2021/7294206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Yunus Khan ◽

Sunita Verma

Keyword(s):

Cuckoo Search ◽

Optimization Method ◽

Structure Design ◽

Network Simulator ◽

Cloud Environment ◽

Computational Overhead ◽

Search Optimization ◽

Secret Keys ◽

Evidence Collection ◽

And Storage

Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).

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An Overview of Digital Image Processing and Recognition (Invited Paper)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100055837 ◽

1982 ◽

Vol 40 ◽

pp. 700-702

Author(s):

R. C. Gonzalez

Keyword(s):

Image Processing ◽

New York ◽

Digital Image Processing ◽

Digital Image ◽

Practical Implementation ◽

Submarine Cable ◽

Vigorous Growth ◽

Digitized Pictures ◽

Processing Techniques ◽

And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

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Control of Secretory Production in the Isopod Hepatopancreas

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100094085 ◽

1972 ◽

Vol 30 ◽

pp. 166-167

Author(s):

John W. Roberts ◽

E. R. Witkus

Keyword(s):

B Cells ◽

Morphological Data ◽

Secretory Product ◽

Secretory Function ◽

Secretory Material ◽

Blind Ending ◽

Secretory Production ◽

Regenerative Cells ◽

Immature Cells ◽

And Storage

The isopod hepatopancreas, as exemplified by Oniscus ascellus. is comprised of four blind-ending diverticula. The regenerative cells at the tip of each diverticula differentiate into either club-shaped B-cells, which serve a secretory function, or into conoid S-cells, which serve in the absorption and storage of nutrients.The glandular B-cells begin producing secretory material with the development of rough endoplasmic reticulum during their process of maturation from the undifferentiated regenerative cells. Cytochemical and morphological data indicate that the hepatopancreas sequentially produces two types of secretory material within the large club-shaped cells. The production of the carbohydrate-like secretory product in immature cells seems to be phased out as the production of the osmiophilic secretion was phased in as the cell matured.

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Quantitative Analysis Of X-Ray Images From Geological Materials

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010013482x ◽

1990 ◽

Vol 48 (2) ◽

pp. 244-245

Author(s):

J. M. Paque ◽

R. Browning ◽

P. L. King ◽

P. Pianetta

Keyword(s):

Quantitative Analysis ◽

Bulk Composition ◽

Principal Component ◽

Analytical Description ◽

Bulk Sample ◽

Geological Samples ◽

X Ray ◽

Software And Hardware ◽

And Storage ◽

Insight Into

Geological samples typically contain many minerals (phases) with multiple element compositions. A complete analytical description should give the number of phases present, the volume occupied by each phase in the bulk sample, the average and range of composition of each phase, and the bulk composition of the sample. A practical approach to providing such a complete description is from quantitative analysis of multi-elemental x-ray images.With the advances in recent years in the speed and storage capabilities of laboratory computers, large quantities of data can be efficiently manipulated. Commercial software and hardware presently available allow simultaneous collection of multiple x-ray images from a sample (up to 16 for the Kevex Delta system). Thus, high resolution x-ray images of the majority of the detectable elements in a sample can be collected. The use of statistical techniques, including principal component analysis (PCA), can provide insight into mineral phase composition and the distribution of minerals within a sample.

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