Android utilizes Ahead-of-Time (AOT) compilation technology to precompile applications and stores the compiled code in OAT files, thereby improving app performance. When the Android system is updated, the old OAT files become invalidated. Applications will fall back to interpreted execution, resulting in degraded performance. To accommodate the frequent updates to the Android system that commonly occur on a monthly basis for most smartphone manufacturers, apps must be frequently recompiled into OAT files to promptly restore optimal app performance. However, recompiling applications is a resource-consuming process that cannot be completed quickly. Users have to endure issues such as device overheating and lag, which are caused by performance degradation after system updates.This paper evaluated popular Android apps across different system updates and made an important observation: up to 99% of the compiled code can be reused across different system updates, rendering most existing recompilation efforts unnecessary. Based on this observation, this paper proposes a method to accelerate app recompilation across Android system updates by reusing the old OAT files. We evaluated the proposed method with eight popular apps, on ten open-source Android system pairs and one closed-source Android system pair provided by a smartphone manufacturer. These Android system pairs have the same Android Runtime (ART) version and execute AOT compilation in both speed and speed-profile modes. Experimental results show that the proposed method reuses approximately 95% of compiled methods, achieving average speedups of 2.12× in CPU time and 1.39× in wall-clock time in speed-profile mode. In speed mode, the proposed method reuses about 99% of compiled methods, achieving average speedups of 5.15× in CPU time and 2.80× in wall-clock time, respectively. The proposed method not only accelerates app recompilation but also generates OAT files identical to those generated by native AOT compilation, without introducing security issues. Therefore, it holds significant promise for real-world deployment and has the potential to enhance user experience by speeding up the generation of new OAT files for applications.

Due to the inevitable information loss during IR lowering, compile-time metadata collection can provide more precise auxiliary information than binary analysis to achieve reliable fine-grained randomization. However, existing schemes build on deep modifications of compilers, making it challenging to provide consistent randomization protection for different high-level languages. Additionally, they are inadequate for securing widely used smartphones and embedded devices, since only ×86-64 applications are currently supported. In this paper, we present MLARandom, a compiler-assisted function-level randomization scheme designed for Multi-Language ARM64 applications. MLARandom employs a lightweight compilation standardization strategy that allows for uniform information collection at the assembly level, regardless of the high-level language or compiler used. Further, it combines ARM64 architecture specifications and collected relocation types to accurately repair all ARM64 pointers after randomization. Our experimental results show that MLARandom can equally randomize modules developed in different languages (e.g., C/C++, Rust, Fortran, Cangjie) with negligible runtime overhead (0.51%), to effectively counter against traditional Code Reuse Attacks as well as advanced Cross-Language Attacks. Although randomization approaches based on reassembly can achieve similar goals, our empirical evaluation highlights the imprecise pointer identification as a major obstacle to their practical deployment.

Various fine-grained randomization schemes have been designed to increase the entropy of process space, while none of them can rise from an academic exercise to industrial deployment like Address Space Layout Randomization (ASLR). One of the critical reasons is the incorrectness of randomization caused by the mismatch between their pointer collection capabilities and the high accuracy requirements of the pointer patching task. In this article, we present PointerScope, an accurate compile-time pointer collection scheme deriving from a group of novel observations. The success of PointerScope relies on the complete tracing of the pointer generation process, including the compilation chain from compiler to static linker and the interface specification between them. From this view, PointerScope identifies four types of pointer-related static linker behaviors and clarifies five types of inherent addressing modes in the x86-64 architecture. The vague understanding of them causes the Compiler-assisted Code Randomization (CCR) to incorrectly collect pointers and patch them to the wrong values after randomization. Further, we measure the pointer collection capability of augmented binary analysis, the experimental results show that they can mitigate challenges from the traditional binary analysis by the given premises, but additional heuristics still need to be designed to support the fine-grained randomization.

In this paper, we study the secret-key capacity of the secure cooperative network using the collaborative amplify-andforward (AF) relays to form a beamforming system through the physical layer. The closed-form expression of secret-key capacity is firstly given to prove the the secret-key capacity is not only achievable but can be designed and optimized by beamforming vector formed by AF relays. Furthermore, three beamforming solutions are proposed to maximize the secret-key capacity under the total relay power constraint. The first solution, maximum secret-key capacity (MSKC) beamforming solution, is aimed towards two-level optimization problems and performed by using the semidefinite relaxation (SDR) technique. This solution is the closest to the optimal solution of original problem, but its complexity is very high because of the SDPs. To decrease the complexity, the signals at all eavesdroppers are forced to be zero, producing the second solution named zero-forcing (ZF) beamforming solution. However, the ZF beamforming solution does not work when the number of eavesdroppers is larger than that of relays. The third solution, lower bound maximum secretkey capacity (LB-MSKC) beamforming solution, is designed to make up the loss. It simplifies the original problem to a one-level optimization problem by relaxing the eavesdroppers, and it can still work well when the number of eavesdroppers is larger than that of relays. Simulation results are presented to illustrate the proposed solutions.

Vehicle-to-Vehicle (V2V) Safety Communication is committed to reduce traffic accidents by information exchange between vehicles. This paper studies the V2V safety communication based on cellular systems, especially LTE, where vehicles send Cooperative Awareness Messages (CAM) periodically to Base Stations (BSs) and the BSs transmit the CAMs to target vehicles through appropriate strategies. For the case of vehicle to multi-vehicle communication(usually cross cell), Multimedia Broadcast multicast service Single Frequency Network(MBSFN) can be adopted to achieve better performance compared with unicast because every message is only transmitted once reaching all target vehicles which reduce network load. A novel MBSFN scheme is designed in this paper. Simulation results show that our proposed MBSFN scheme can support more cars than the existing scheme under the same latency and QoS constraints.