Computing trajectory similarity is a fundamental task in trajectory analysis. Traditional heuristic methods suffer from quadratic computational complexity, which limits their scalability to large datasets. Recently, Trajectory Representation Learning (TRL) has been extensively studied to address this limitation. However, most existing TRL algorithms face two key challenges. First, they prioritize spatial similarity while neglecting the intricate spatio-temporal dynamics of trajectories, particularly temporal regularities. Second, these methods are often constrained by predefined single spatial or temporal scales, which can significantly impact performance, since the measurement of trajectory similarity depends on spatial and temporal resolution. To address these issues, we propose MSST, a Multi-Scale Self-supervised Trajectory Representation Learning framework. MSST simultaneously processes spatial and temporal information by generating 3D spatial-temporal tokens, thereby capturing spatio-temporal characteristics of trajectories more effectively. Further, MSST explore the multi-scale characteristics of trajectories. Finally, self-supervised contrastive learning is employed to enhance the consistency between the trajectory representations from different views. Experimental results on three real-world datasets for similarity trajectory computation provide insight into the design properties of our approach and demonstrate the superiority of our approach over existing TRL methods. MSST significantly surpasses all state-of-the-art competitors in terms of effectiveness, efficiency, and robustness. We explore the multi-scale characteristics of trajectories. To the best of our knowledge, this is the first effort in the TRL literature. Compared to previous TRL research, the proposed method can balance the noise and the details of trajectories, enabling a more comprehensive analysis by accounting for the variability inherent in trajectory data across different scales.

Mobile positioning plays an essential role in smart city services. This paper proposes a fingerprint positioning framework based on massive Minimization of Drive Test (MDT) data to provide accurate and efficient city-scale positioning without additional equipment and measures. First, a multi-level fingerprint construction method is proposed using the Timing Advance (TA), Reference Signal Receiving Power (RSRP), and Reference Signal Receiving Quality (RSRQ) of the serving cell and neighboring cell. Then, an adaptive online fingerprint matching method is employed to extract and match online data fingerprints. Experiments show that the median positioning error is 29.97 meters with city-scale MDT data. It outperforms the reported accuracy of the state-of-the-art fingerprint positioning method.

In this paper we explore the problem of collecting malicious smartphone advertisements. Most smartphone app contains advertisements and also suffers from vulnerable advertisement libraries. Malicious advertisements exploit the ad library vulnerability and attack victim smartphones. Similar to the traditional honeypots, we need an effective way to capture malicious ads. In this paper, we provide our approach named AdHoneyDroid. We build a crawler to gather apps on the android marketplaces and manually collect ad libraries and their vulnerabilities. Then AdHoneyDroid executes the apps and detects malicious advertisements. In our approach, we adopt the idea of API sandbox and TaintDroid to detect the attack event. We store the malicious advertisements in a database for future analysis. Malicious ads can help security analysts have a better understanding of current mobile attacks and also disclose the attack payloads.

Network operators need to have a clear visibility into the applications running in their network. This is critical for both security and network management. Recent years have seen an exponential growth in the number of smart phone apps which has complicated this task. Traditional methods of traffic classification are no longer sufficient as the majority of this smart phone app traffic is carried over HTTP/HTTPS. Keeping up with the new applications that come up everyday is very challenging and time-consuming. We present a novel technique for automatically generating network profiles for identifying Android apps in the HTTP traffic. A network profile consists of fingerprints, i.e., unique characteristics of network behavior, that can be used to identify an app. To profile an Android app, we run the app automatically in an emulator and collect the network traces. We have developed a novel UI fuzzing technique for running the app such that different execution paths are exercised, which is necessary to build a comprehensive network profile. We have also developed a light-weight technique, for extracting fingerprints, that is based on identifying invariants in the generated traces. We used our technique to generate network profiles for thousands of apps. Using our network profiles we were able to detect the presence of these apps in real-world network traffic logs from a cellular provider.

Response-computable authentication (RCA) is a two-party authentication model widely adopted by authentication systems, where an authentication system independently computes the expected user response and authenticates a user if the actual user response matches the expected value. Such authentication systems have long been threatened by malicious developers who can plant backdoors to bypass normal authentication, which is often seen in insider-related incidents. A malicious developer can plant backdoors by hiding logic in source code, by planting delicate vulnerabilities, or even by using weak cryptographic algorithms. Because of the common usage of cryptographic techniques and code protection in authentication modules, it is very difficult to detect and eliminate backdoors from login systems. In this paper, we propose a framework for RCA systems to ensure that the authentication process is not affected by backdoors. Our approach decomposes the authentication module into components. Components with simple logic are verified by code analysis for correctness, components with cryptographic/ obfuscated logic are sand boxed and verified through testing. The key component of our approach is NaPu, a native sandbox to ensure pure functions, which protects the complex and backdoor-prone part of a login module. We also use a testing-based process to either detect backdoors in the sand boxed component or verify that the component has no backdoors that can be used practically. We demonstrated the effectiveness of our approach in real-world applications by porting and verifying several popular login modules into this framework.