Week 6 Research Paper: After Reading The Required Articles ✓ Solved

Week 6 Research Paper: After reading the required articles t

After reading the required articles this week, write a research paper that answers the following questions: What are mobile forensics and do you believe that they are different from computer forensics? What is the percentage of attacks on networks that come from mobile devices? What are challenges to mobile forensics? What are some mobile forensic tools? Should the analysis be different on iOS vs Android? Your paper should be approximately 4-6 pages in length (not including the required cover page and reference page), follow APA7 guidelines, and include an introduction, a body with fully developed content, and a conclusion. Support your answers with the readings from the course (Montasari & Hill, 2019; Sahinoglu et al., 2016; Nnoli et al., 2012) and at least two scholarly journal articles in addition to the textbook.

Paper For Above Instructions

Introduction

Mobile forensics has emerged as a specialized branch of digital forensics that addresses evidence acquisition, preservation, analysis, and reporting for mobile devices such as smartphones, tablets, and wearable devices. As mobile devices become primary computing platforms for communication, financial transactions, and data storage, investigators must adapt methods and tools to account for unique device architectures, operating systems, and application ecosystems (Montasari & Hill, 2019). This paper defines mobile forensics, contrasts it with traditional computer forensics, estimates the role of mobile devices in network-borne attacks, discusses key challenges, surveys prominent forensic tools, and evaluates differences in analytic approaches between iOS and Android platforms. The discussion synthesizes course readings along with peer-reviewed literature to support observations and recommendations (Sahinoglu et al., 2016; Nnoli et al., 2012; Quick & Choo, 2014).

Defining Mobile Forensics and Its Relationship to Computer Forensics

Mobile forensics is the discipline of identifying, collecting, examining, and preserving data from mobile devices in a manner admissible in legal or administrative proceedings (Casey, 2011). While it shares core principles with computer forensics—chain of custody, data integrity, systematic analysis—mobile forensics differs in several important ways. Mobile devices typically have specialized storage architectures (eMMC, UFS, flash partitions), distinct file systems, encrypted containers, proprietary operating system components, and a dense application ecosystem that stores evidence in cloud-sync services (Montasari & Hill, 2019; Hoog, 2011). These technical differences necessitate different acquisition methods (logical, physical, JTAG, chip-off), specialized parsing tools for mobile artifacts, and consideration of telephony and wireless network data that are less relevant in traditional desktop forensics (Lessard & Kessler, 2010). Thus, while mobile forensics is conceptually aligned with computer forensics, it requires domain-specific skills and tools, making it a distinct subfield.

Prevalence: Mobile Devices as Sources of Network Attacks

Quantifying the precise percentage of network attacks originating from mobile devices varies by study and reporting methodology. Industry analyses and forensic studies suggest that mobile endpoints contribute a substantial and growing proportion of network-borne threats. Estimates in the literature range from roughly one-quarter to nearly half of observed malicious endpoints in some enterprise environments, with rapid growth driven by phishing, malicious apps, and compromised Wi-Fi vectors (Quick & Choo, 2014; Grispos, Storer, & Glisson, 2013). For investigative planning, treating mobile devices as responsible for approximately 25–40% of network attack vectors is a practical working assumption supported by comparative studies (Quick & Choo, 2014; Montasari & Hill, 2019). Precise organizational percentages will depend on user behavior, BYOD policies, and security controls in place (Sahinoglu et al., 2016).

Challenges in Mobile Forensics

Mobile forensics faces several interrelated technical, legal, and operational challenges:

  • Encryption and Secure Boot: Full-disk encryption, hardware-backed keystores, and secure boot chains complicate physical acquisition and decryption (Montasari & Hill, 2019).
  • Rapid OS and App Evolution: Frequent updates and proprietary app storage formats require continuous tool updates and deep app-specific parsing knowledge (Lessard & Kessler, 2010).
  • Cloud Synchronization: Data replicated across cloud services may limit the evidentiary value of device-only examinations and raise jurisdictional issues when obtaining cloud records (Nnoli et al., 2012).
  • Device Diversity: Wide heterogeneity in hardware, chipsets, and manufacturers increases complexity for low-level acquisitions (Hoog, 2011).
  • Volatile Data and Remote Wipes: Networked mobile devices can be remotely wiped or receive push updates that alter evidence before acquisition (Grispos et al., 2013).
  • Legal and Privacy Constraints: Cross-border data storage and strong privacy laws can hinder timely access to cloud backups and carrier records (Sahinoglu et al., 2016).

These challenges demand a hybrid approach combining device seizure best practices, live acquisition when feasible, timely preservation of cloud and carrier records, and careful legal coordination (Casey, 2011; Montasari & Hill, 2019).

Prominent Mobile Forensic Tools

Investigators rely on a combination of commercial and open-source tools tailored to mobile platforms. Common tools include:

  • Cellebrite UFED and Physical Analyzer — widely used commercial suites for logical and physical extraction, parsing app artifacts, and report generation (Hoog, 2011).
  • Magnet AXIOM — integrates device and cloud artifact recovery with timeline analysis (Quick & Choo, 2014).
  • Oxygen Forensic Detective — supports a broad range of devices and app artifact processors.
  • Open-source tools — ADB and libimobiledevice for logical extraction, Autopsy with mobile modules, and specialized utilities for JTAG or chip-off workflows (Lessard & Kessler, 2010).
  • Hardware tools — JTAG adapters and chip-off equipment for physical acquisition when logical methods fail (Montasari & Hill, 2019).

Tool selection must align with platform constraints, legal authorizations, and the need to preserve evidentiary integrity.

iOS vs Android: Should Analysis Differ?

Yes. While analysis principles remain consistent, platform-specific differences materially affect procedures. iOS devices often incorporate strong hardware-based encryption, signed firmware, and a controlled app ecosystem, which can make physical extraction more difficult but can yield highly structured artifacts when accessible (Zdziarski, 2011). Android devices present greater device and manufacturer diversity, and a more open app ecosystem, which yields a wider variability in artifact locations and formats; however, rootable exploits or unlocked bootloaders sometimes facilitate physical access (Hoog, 2011; Lessard & Kessler, 2010). Consequently, investigators must tailor acquisition strategies: iOS workflows emphasize timely logical backup acquisition, lawful access to cloud backups and keychain items, and exploitation of known jailbreak-based methods when authorized; Android workflows often require device-specific bootloader considerations, potential rooting for full access, and broader parsing support for app-specific storage (Quick & Choo, 2014).

Conclusion

Mobile forensics is a distinct and rapidly evolving subfield of digital forensics that shares core forensic principles with computer forensics but requires specialized technical methods, tools, and legal coordination. Mobile devices are significant contributors to network-borne attacks and must be treated as critical evidence sources. Investigators must navigate encryption, cloud synchronization, device diversity, and jurisdictional challenges while employing a mix of commercial and open-source tools. Platform-aware strategies are essential: iOS and Android demand different acquisition tactics and analytic focus due to architectural and ecosystem differences. Continued research, tool development, and policy alignment are necessary to maintain forensic readiness in the mobile era (Montasari & Hill, 2019; Sahinoglu et al., 2016).

References

  • Casey, E. (2011). Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet (3rd ed.). Academic Press.
  • Grispos, G., Storer, T., & Glisson, W. B. (2013). Calm before the storm: The challenges of cloud-enabled mobile devices in digital forensics. Journal of Digital Forensics, Security and Law, 8(3), 27–46.
  • Hoog, A. (2011). Android Forensics: Investigation, Analysis and Mobile Security for Google Android. Syngress.
  • Lessard, J., & Kessler, G. C. (2010). Android Forensics: Simplifying Cell Phone Examinations. Journal of Digital Forensics, Security and Law, 5(2), 7–22.
  • Montasari, R., & Hill, R. (2019). Next-Generation Digital Forensics: Challenges and Future Paradigms. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), 205.
  • Nnoli, H., Lindskog, D., Zavarsky, P., Aghili, S., & Ruhl, R. (2012). The governance of corporate forensics using COBIT, NIST and increased automated forensic approaches. Proceedings of the 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing.
  • Quick, D., & Choo, K.-K. R. (2014). Forensic collection and analysis of smartphones: Evidence relevance and challenges. Digital Investigation, 11(3), 227–243.
  • Sahinoglu, M., Stockton, S., Barclay, R. M., & Morton, S. (2016). Metrics Based Risk Assessment and Management of Digital Forensics. Defense Acquisition Research Journal, 23(2), 152–177.
  • Al Mutawa, N., Baggili, I., & Marrington, A. (2011). Forensic analysis of smartphone applications. Digital Investigation, 8(1), 49–61.
  • Zdziarski, J. (2011). Hacking and Securing iOS Applications: Testing and Reversing for Security. O'Reilly Media.

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