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The proposed method bears two distinctive features.

The key idea is to construct the cross-resolution cross-network context for each object. In this paper, we propose a unified framework MrMine to learn the representations of objects from multiple networks at three complementary resolutions (i.e., network, subgraph and node) simultaneously. Consequentially, it leads to sub-optimal performance or even becomes inapplicable for some downstream mining tasks (e.g., role classification, network alignment. A fundamental limitation with such methods is that the intrinsic relationship across different networks (e.g., two networks share same or similar subgraphs) and that across different resolutions (e.g., the node-subgraph membership) are ignored, resulting in more » disparate embeddings. Most of the existing methods focus on a single network and/or a single resolution, which generate embeddings of different network objects (node/subgraph/network) from different networks separately. Network embedding has become the cornerstone of a variety of mining tasks, such as classification, link prediction, clustering, anomaly detection and many more, thanks to its superior ability to encode the intrinsic network characteristics in a compact low-dimensional space. 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.

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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.

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 resulting graph is then fed as an input into our semantic analysis algorithms to determine objects’ origin, context, and scope in the network.

OAGen utilizes Points-to analysis to reconstruct a runtime’s object allocation network. We introduce OAGen, a post-execution and app-agnostic more » 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. Thus, in this research, we present a solution to close the gap between application-specific and application-generic techniques. 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. These techniques analyze and recover objects and artifacts from process memory space that are of critical importance in investigations. 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. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.

Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. To efficiently classify nodes (e.g., apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats.

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