Towards Reliable Justice Developing A Comprehensive Software Application For Evaluating The Admissibility Of Digital Evidence In The Court Assignment Sample

Introduction : Towards Reliable Justice Developing A Comprehensive Software Application For Evaluating The Admissibility Of Digital Evidence In The Court

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Digital forensic investigation activity is advancement in the court process and science and the advancement of technology support in developing the effectiveness of data collection and analysis activities. Digital media contributes to collecting evidence and completing the investigation process of different cybercrime activities (Lone and Mir, 2019). Electronic evidence includes identifying issues through utilising different digital devices and involves identifying principles that focus on the admissibility of digital evidence (Al-Saeed et al. 2020). Digital evidence includes storing information and transmitting that information to complete trials and other activities in court (Bhat et al. 2021). This supports the court to verify the reliability of evidence and authenticity of the evidence. The utilisation of digital evidence has increased over the last few decades and courts considered the utilisation of e-mails, ATM transaction details, digital photographers, message histories, internet browser histories, system tracks, global positioning, computer memory contents and digital audio and video files (Coat, 2023).

Electronic evidence includes binary data content, storage device and software analysis to read and interpret overall binary data. Python includes a multi-paradigm programming language and can be utilised in different data-centric activities in different court processes (Wu et al. 2020). Different types of features in Python support interpreting, object-oriented script and interactive activities in evidence collection and analysis of act to justify crime and supports the court in completing decision-making activities.

Research background

According to Thaker and Shukla (2020), python includes different characteristics such as interpreted, interactive, object-oriented, convenient to read and write, portable, extendable, GUI programming, scripting language and quality memory management. These characteristics support different purposes of data science as they contain different libraries such as numpy, SciPy, Pandas, Matplotlib and IPython. Python supports completing activities of digital forensics by considering security professionals and does not consider the utilisation of third-party applications. Digital security and fingerprinting applications involve in this programming language support digital forensic activities to develop the admissibility of digital evidence in the court process. This language is used for socket programming and delivers easy methods and functions to complete this programming activity (Asquith and Horsman, 2019). Python-nmap library can complete port scanning functionality by including less number of code lines in it. This programming language also supports IP address geolocation and contributes to exploiting scripts and different framework activities in the digital forensic process.

As opined by Salamh et al. (2019), the forensic tool Autopsy 4.6.0 included exporting flight logs through a drone controller chip to visualise GPS data and utilisation of Python script of webFlightPath, Google and GPS visualiser to complete drone forensic investigation. IT Act of 2000 included revised legislation to develop admissible digital evidence in the court process. Forensic science includes different technologies and algorithms programming languages to complete evidence of cybercrimes and conduct court processes (Santhy and Padmanabhan, 2023). According to Serketzis et al. (2019), digital forensic includes the utilisation of programming language to continue the preservation, acquisition, identification, analysis, documentation and interpretation of digital data to complete root cause analysis. This considers the presentation of different digital sources to reconstruct suspicious events.

WLAN communications include models to collect, store and analysis of data to complete jurisdictions' activities. As stated by Servida and Casey (2019), the data collection process can integrate into the script of Python language to collect data directly from different devices. This programming language includes ismartAlarm events to store data and complete user interaction activity. This programming continues reading scripts of Python and text data and stimulates unstructured events to complete systematic processes.

Figure 1: DFaaS process

(Source: van Beek et al. 2020)

DFaaS platform includes support in the investigation process and completes digital evidence and court processes. This platform includes digital material, digital experts, analysts and case investigators. Operators can investigate data under these software base forensic activities and digital experts can complete data investigation and analysis activities by verifying the case with the relevant actors. This includes a Python scripting library to complete the entire investigation process automatically (van Beek et al. 2020). For example, HANSKEN includes Python scripting library and involves in approx more than 1000 cases to complete investigation and court processes.

According to Yeboah-Ofori and Brown (2020), different cybercrime investigations case utilises digital forensic tools and processes. For example, Joseph E Duncan III included computer-based evidence that delivered information on the planning of crimes. BTK Killer, Sharon Lopatka, Corcoran Group and Robert Durall considered digital forensic activities to complete investigations and solve cybercrime cases. As opined by ANGEL et al. (2023), digital evidence is not formerly in the judicial environment and real cases include casual relationship factors. The utilisation of digital technologies and programming-based investigation processes deliver an analysis of cases with relevant acts to develop decisions in different cases. These digital programming base activities support decreasing time requirements and completing audit processes by verifying different logs and files in the database.

This data-centric investigation and jurisdiction process support developing proof-based decision-making activities while solving any cases in the court. This can support in identifying issues and completing entire jurisdiction activity within a short period and developing solutions for any cases.

Objectives

1. To analyse the case law, standards, and legal frameworks that are currently in place regarding the admissibility of digital evidence.
2. To develop a comprehensive Framework for analyzing the admissibility of digital evidence that includes legal concepts, technical requirements, and evidential criteria.
3. To design and implementing Python scripts for simulation and modelling allowing the exploration of different of admissibility thresholds or criteria and their impact on legal outcomes.
4. To Conduct performance test and validation of the proposed framework and test the authenticity, integrity and maintain chain of custody of digital evidence.
5. To validate the framework using real-world data and establish its effectiveness in assessing authenticity, integrity, and reliability of digital evidence while maintaining chain of custody.
6. To investigate case precedents, standards, and legislative structures to improve digital evidence assessment and acceptance in the judicial system.

Research Gap

According to ANGEL et al. (2023), digital literacy gaps and technological gaps can present in legal professionals to complete entire data integration and jurisdiction activities in the court. This process can operate as per programming and scripts and cannot deliver results in aspects of relationships and other human-based factors. As opined by Servida and Casey (2019), Trace limitation is present in this research and faces the requirements of extending forensic capabilities to develop an evidence-based investigation process. As stated by van Beek et al. (2020), DFaaS is hard to develop the entire process and can consider the large cost to complete the overall process.

Benefit from this research

This research can provide information regarding digital evidence-based activities in the investigation and jurisdiction process. This can show the effectiveness of Python programming language to continue data collection, storing and analysis of those data of different cases with relevant legislations. This can show the legal process that the court can consider to complete investigation and decision-making activities through the utilisation of digital technologies and Python programming language. This can support understanding the process of utilisation of digital evidence through Python to collect, store, integrate, interpret and analysis of data to complete court processes.

References

• Al-Saeed, Y., Edwards, D.J. and Scaysbrook, S., 2020. Automating construction manufacturing procedures using BIM digital objects (BDOs): Case study of knowledge transfer partnership project in UK. Construction Innovation.
• ANGEL, O.E.M., MERCEDES, C.F.Y.M., ELISA, Q.L.A., JOAQUIN, D.P.J., GIOVANNA, C.D.O.D.D. and BEATRIZ, G.Q.G., 2023. DIGITAL EVIDENCE AS A MEANS OF PROOF IN CRIMINAL PROCEEDINGS. Russian Law Journal, 11(5s).
• Asquith, A. and Horsman, G., 2019. Let the robots do it!–Taking a look at Robotic Process Automation and its potential application in digital forensics. Forensic Science International: Reports, 1, p.100007.
• Bhat, W.A., AlZahrani, A. and Wani, M.A., 2021. Can computer forensic tools be trusted in digital investigations?. Science & Justice, 61(2), pp.198-203.
• Coat, 2023. Evidence from a digital device Available at: https://coat.asn.au/wp-content/uploads/2018/11/Evidence-from-a-digital-device-Miiko-Kumar.pdf [Accessed on: 29/06/2023]
• Lone, A.H. and Mir, R.N., 2019. Forensic-chain: Blockchain based digital forensics chain of custody with PoC in Hyperledger Composer. Digital investigation, 28, pp.44-55.
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• Santhy, D.K. and Padmanabhan, A.S., 2023. A Review on the Changing Dimensions of Digital Forensics in Criminal Investigations. SVP National Police Academy Journal, Forthcoming.
• Serketzis, N., Katos, V., Ilioudis, C., Baltatzis, D. and Pangalos, G.J., 2019. Actionable threat intelligence for digital forensics readiness. Information & Computer Security.
• Servida, F. and Casey, E., 2019. IoT forensic challenges and opportunities for digital traces. Digital Investigation, 28, pp.S22-S29.
• Thaker, N. and Shukla, A., 2020. Python as multi paradigm programming language. International Journal of Computer Applications, 177(31), pp.38-42.
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• Yeboah-Ofori, A. and Brown, A.D., 2020. Digital forensics investigation jurisprudence: issues of admissibility of digital evidence. Journal of Forensic, Legal & Investigative Sciences, 6(1), pp.1-8.