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Data privacy, a critical human right, is gaining importance as new technologies are developed, and the old ones evolve. In mobile platforms such as Android, data privacy regulations require developers to communicate data access requests using privacy policy statements (PPS). This case study cross-examines the PPS in popular social media (SM) apps---Facebook and Twitter---for features of language ambiguity, sensitive data requests, and whether the statements tally with the data requests made in the Manifest file. Subsequently, we conduct a comparative analysis between the PPS of these two apps to examine trends that may constitute a threat to user data privacy. 

As data privacy continues to be a crucial human-right concern as recognized by the UN, regulatory agencies have demanded developers obtain user permission before accessing user-sensitive data. Mainly through the use of privacy policies statements, developers fulfill their legal requirements to keep users abreast of the requests for their data. In addition, platforms such as Android enforces explicit permission request using the permission model. Nonetheless, recent research has shown that service providers hardly make full disclosure when requesting data in these statements. Neither is the current permission model designed to provide adequate informed consent. Often users have no clear understanding of the reason and scope of usage of the data request. This paper proposes an unambiguous, informed consent process that provides developers with a standardized method for declaring Intent. Our proposed Intent-aware permission architecture extends the current Android permission model with a precise mechanism for full disclosure of purpose and scope limitation. The design of which is based on an ontology study of data requests purposes. The overarching objective of this model is to ensure end-users are adequately informed before making decisions on their data. Additionally, this model has the potential to improve trust between end-users and developers. 

Memory Forensics is one of the most important emerging areas in computer forensics. In memory forensics, analysis of userland memory is a technique that analyses per-process runtime data structures and extracts significant evidence for application-specific investigations. In this research, our focus is to examine the critical challenges faced by process memory acquisition that can impact object and data recovery. Particularly, this research work seeks to address the issues of consistency and reliability in userland memory forensics on Android. In real-world investigations, memory acquisition tools record the information when the device is running. In such scenarios, each application’s memory content may be in flux due to updates that are in progress, garbage collection activities, changes in process states, etc. In this paper we focus on various runtime activities such as garbage collection and process states and the impact they have on object recovery in userland memory forensics. The outcome of the research objective is to assess the reliability of Android userland memory forensic tools by providing new research directions for efficiently developing a metric study to measure the reliability. We evaluated our research objective by analysing memory dumps acquired from 30 apps in different Process Acquisition Modes. The Process Acquisition Mode (PAM) is the memory dump of a process that is extracted while external runtime factors are triggered. Our research identified an inconsistency in the number of objects recovered from analysing the process memory dumps with runtime factors included. Particularly, the evaluation results revealed differences in the count of objects recovered in different acquisition modes. We utilized Euclidean distance and covariance as the metrics for our study. These two metrics enabled the authors to identify how the change in the number of recovered objects in PAM impact forensic analysis. Our conclusion revealed that runtime factors could on average result in about 20% data loss, thus revealing these factors can have an obvious impact on object recovery. 

As IT/OT convergence continues to evolve, the traditionally isolated ICS/OT systems are increasingly exposed to a myriad of online and offline threats. Although IIoT enhances the reachability in ICS, im- proved data analytics, ensuring ease of access and decision making, it unwittingly opens the ICS environment to attackers. The design of IIoT introduces multiple entry points to an isolated system, which is used to protect itself via air-gapping and risk avoidance strategies. This study explores a comprehensive mapping of threats and risks for IT/OT convergence. Additionally, we propose IIoT-ARAS - an automated risk assessment system based on OCTAVE Allegro and ISO/IEC 27030 methodologies. The design of IIoT-ARAS is aimed to be agentless, with minimum interruptions to the OT environment. Furthermore, the system performs automated regular asset inventory checks, threshold optimization, probability computation, risk evaluations, and contingency plan configuration. 

Traditionally, Android malware is analyzed using static or dynamic analysis. Although static techniques are often fast; however, they cannot be applied to classify obfuscated samples or malware with a dynamic payload. In comparison, the dynamic approach can examine obfuscated variants but often incurs significant runtime overhead when collecting every important malware behavioral data. This paper conducts an exploratory analysis of memory forensics as an alternative technique for extracting feature vectors for an Android malware classifier. We utilized the reconstructed per-process object allocation network to identify distinguishable patterns in malware and benign application. Our evaluation results indicate the network structural features in the malware category are unique compared to the benign dataset, and thus features extracted from the remnant of in-memory allocated objects can be utilized for robust Android malware classification algorithm. 

Over the last decade, userland memory forensics techniques and algorithms have gained popularity among practitioners, as they have proven to be useful in real forensics and cybercrime investigations. These techniques analyze and recover objects and artifacts from process memory space that are of critical importance in investigations. Nonetheless, the major drawback of existing techniques is that they cannot determine the origin and context within which the recovered object exists without prior knowledge of the application logic. Thus, in this research, we present a solution to close the gap between application-specific and application-generic techniques. We introduce OAGen, a post-execution and app-agnostic semantic analysis approach designed to help investigators establish concrete evidence by identifying the provenance and relationships between in-memory objects in a process memory image. OAGen utilizes Points-to analysis to reconstruct a runtime’s object allocation network. The resulting graph is then fed as an input into our semantic analysis algorithms to determine objects’ origin, context, and scope in the network. The results of our experiments exhibit OAGen’s ability to effectively create an allocation network even for memory-intensive applications with thousands of objects, like Facebook. The performance evaluation of our approach across fourteen different Android apps shows OAGen can efficiently search and decode nodes, and identify their references with a modest throughput rate. Further practical application of OAGen demonstrated in two case studies shows that our approach can aid investigators in the recovery of deleted messages and the detection of malware functionality in post-execution program analysis. 

There is a growing need for post-mortem analysis in forensics investigations involving mobile devices, particularly when application-specific behaviors must be analyzed. This is especially true for architectures such as Android, where traditional kernel-level memory analysis frameworks such as Volatility face serious challenges recovering and providing context for user-space artifacts. In this research work, we developed an app-agnostic userland memory analysis technique that targets the new Android Runtime (ART). Leveraging its latest memory allocation algorithms, called region-based memory management, we develop a system called DroidScraper that recovers vital runtime data structures for applications by enumerating and reconstructing allocated objects from a process memory image. The result of our evaluation shows DroidScraper can recover and decode nearly 90% of all live objects in all allocated memory regions.