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Existing Android vulnerability detection tools overwhelm teams with thousands of low-signal warnings yet uncover few true positives. Analysts spend days triaging these results, creating a bottleneck in the security pipeline. Meanwhile,…
Contemporary mobile applications (apps) are designed to track, use, and share users' data, often without their consent, which results in potential privacy and transparency issues. To investigate whether mobile apps have always been…
The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party markets. Additionally, the vital role…
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them,…
Provenance graph-based intrusion detection systems are deployed on hosts to defend against increasingly severe Advanced Persistent Threat. Using Graph Neural Networks to detect these threats has become a research focus and has demonstrated…
Despite its widespread use in Android apps, reflection poses graving problems for static security analysis. Currently, string inference is applied to handle reflection, resulting in significantly missed security vulnerabilities. In this…
A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to…
The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls,…
The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate…
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms…
We analyze the prandom pseudo random number generator (PRNG) in use in the Linux kernel (which is the kernel of the Linux operating system, as well as of Android) and demonstrate that this PRNG is weak. The prandom PRNG is in use by many…
Software projects are dependent on many third-party libraries, therefore high-risk vulnerabilities can propagate through the dependency chain to downstream projects. Owing to the subjective nature of patch management, software vendors…
Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to build structural patterns in social graphs which can be used to re-identify legitimate users on…
Self-stabilizing algorithms are an important because of their robustness and guaranteed convergence. Starting from any arbitrary state, a self-stabilizing algorithm is guaranteed to converge to a legitimate state.Those algorithms are not…
Background. Evidence suggests that mobile applications are not thoroughly tested as their desktop counterparts. In particular GUI testing is generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e.…
Expecting the shipment of 1 billion Android devices in 2017, cyber criminals have naturally extended their vicious activities towards Google's mobile operating system. With an estimated number of 700 new Android applications released every…
Android apps are built on APIs that abstract core Android system functionalities. These APIs are officially documented in multiple files distributed with the Android source code or SDK, which we collectively refer to as Android API Lists…
In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks. However, when an upstream data owner publishes graph data, there are often many privacy concerns,…
The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual…
Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review (SLR) examines…