Recovering Residual Forensic Data from Smartphone Interactions with Cloud Storage Providers

There is a growing demand for cloud storage services such as Dropbox, Box, Syncplicity and SugarSync. These public cloud storage services can store gigabytes of corporate and personal data in remote data centres around the world, which can then be synchronized to multiple devices. This creates an environment which is potentially conducive to security incidents, data breaches and other malicious activities. The forensic investigation of public cloud environments presents a number of new challenges for the digital forensics community. However, it is anticipated that end-devices such as smartphones, will retain data from these cloud storage services. This research investigates how forensic tools that are currently available to practitioners can be used to provide a practical solution for the problems related to investigating cloud storage environments. The research contribution is threefold. First, the findings from this research support the idea that end-devices which have been used to access cloud storage services can be used to provide a partial view of the evidence stored in the cloud service. Second, the research provides a comparison of the number of files which can be recovered from different versions of cloud storage applications. In doing so, it also supports the idea that amalgamating the files recovered from more than one device can result in the recovery of a more complete dataset. Third, the chapter contributes to the documentation and evidentiary discussion of the artefacts created from specific cloud storage applications and different versions of these applications on iOS and Android smartphones

Introduction to Cyber-of-Things: Cyber-crimes and Cyber-Security Minitrack

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Digital forensics investigative framework for control rooms in critical infrastructure

In this paper a cyber-forensic framework with a detailed guideline for protecting control systems is developed to improve the forensic capability for big data in critical infrastructures. The main objective of creating a cyber-forensic plan is to cover the essentials of monitoring, troubleshooting, data reconstruction, recovery, and the safety of classified information. The problem to be addressed in control rooms is the diversity and quantity of data, and for investigators, bringing together the different skill groups for managing data and device diversity. This research embraces establishing of a new digital forensic model for critical infrastructures that supports digital forensic investigators with the necessary information for conducting an advanced forensic investigation in Critical Infrastructures. The framework for investigation is presented here and elaborated. The extended work applies the framework to industry case studies and is not reported here

Insight from a Containerized Kubernetes Workload Introspection

Developments in virtual containers, especially in the cloud infrastructure, have led to diversification of jobs that containers are being used to support, particularly in the big data and machine learning spaces. The diversification has been powered by the adoption of orchestration systems that marshal fleets of containers to accomplish complex programming tasks. The additional components in the vertical technology stack, plus the continued horizontal scaling have led to questions regarding how to forensically analyze complicated technology stacks. This paper proposed a solution through the use of introspection. An exploratory case study has been conducted on a bare-metal cloud that utilizes Kubernetes, the introspection tool Prometheus, and Apache Spark. The contribution of this research is two-fold. First, it provides empirical support that introspection tools can acquire forensically viable data from different levels of a technology stack. Second, it provides the ground work for comparisons between different virtual container platforms

Detecting Repackaged Android Applications Using Perceptual Hashing

The last decade has shown a steady rate of Android device dominance in market share and the emergence of hundreds of thousands of apps available to the public. Because of the ease of reverse engineering Android applications, repackaged malicious apps that clone existing code have become a severe problem in the marketplace. This research proposes a novel repackaged detection system based on perceptual hashes of vetted Android apps and their associated dynamic user interface (UI) behavior. Results show that an average hash approach produces 88% accuracy (indicating low false negative and false positive rates) in a sample set of 4878 Android apps, including 2151 repackaged apps. The approach is the first dynamic method proposed in the research community using image-based hashing techniques with reasonable performance to other known dynamic approaches and the possibility for practical implementation at scale for new applications entering the Android market

Knock! Knock! Who Is There? Investigating Data Leakage from a Medical Internet of Things Hijacking Attack

The amalgamation of Medical Internet of Things (MIoT) devices into everyday life is influencing the landscape of modern medicine. The implementation of these devices potentially alleviates the pressures and physical demands of healthcare systems through the remote monitoring of patients. However, there are concerns that the emergence of MIoT ecosystems is introducing an assortment of security and privacy challenges. While previous research has shown that multiple vulnerabilities exist within MIoT devices, minimal research investigates potential data leakage from MIoT devices through hijacking attacks. The research contribution of this paper is twofold. First, it provides a proof of concept that certain MIoT devices and their accompanying smartphone applications are vulnerable to hijacking attacks. Second, it highlights the effectiveness of using digital forensics tools as a lens to identify patient and medical device information on a hijacker’s smartphone

Insight from a Docker Container Introspection

Large-scale adoption of virtual containers has stimulated concerns by practitioners and academics about the viability of data acquisition and reliability due to the decreasing window to gather relevant data points. These concerns prompted the idea that introspection tools, which are able to acquire data from a system as it is running, can be utilized as both an early warning system to protect that system and as a data capture system that collects data that would be valuable from a digital forensic perspective. An exploratory case study was conducted utilizing a Docker engine and Prometheus as the introspection tool. The research contribution of this research is two-fold. First, it provides empirical support for the idea that introspection tools can be utilized to ascertain differences between pristine and infected containers. Second, it provides the ground work for future research conducting an analysis of large-scale containerized applications in a virtual cloud

A Bleeding Digital Heart: Identifying Residual Data Generation from Smartphone Applications Interacting with Medical Devices

The integration of medical devices in everyday life prompts the idea that these devices will increasingly have evidential value in civil and criminal proceedings. However, the investigation of these devices presents new challenges for the digital forensics community. Previous research has shown that mobile devices provide investigators with a wealth of information. Hence, mobile devices that are used within medical environments potentially provide an avenue for investigating and analyzing digital evidence from such devices. The research contribution of this paper is twofold. First, it provides an empirical analysis of the viability of using information from smartphone applications developed to complement a medical device, as digital evidence. Second, it includes documentation on the artifacts that are potentially useful in a digital forensics investigation of smartphone applications that interact with medical devices