The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts’ identification process, leverages novel multi-view features, and is equipped with a well-defined workflow. HAWK is evaluated with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times, higher recall and similar false positive rate compared to the in-use anti-cheat, and the ability to capture cheaters who evaded official inspections.

Modern software development necessitates efficient version-oriented collaboration among developers. While Git is the most popular version control system, it generates unsatisfactory version merging results due to textual-based workflow, leading to potentially unexpected results in the merged version of the project. Although numerous merging tools have been proposed for improving merge results, developers remain struggling to resolve the conflicts and fix incorrectly modified code without clues. We present WizardMerge , an auxiliary tool that leverages merging results from Git to retrieve code block dependency on text and LLVM Intermediate Representation level and provide suggestions for developers to resolve errors introduced by textual merging. Rather than directly resolving these errors, the suggestions provide pigeonholed code blocks with their relevance and prioritized order within each category. To this end, developers can address the specific locations of these issues without manually analyzing their dependencies, thereby reducing the time and effort spent on conflict resolution tasks. Through the evaluation, we subjected WizardMerge to testing on 227 conflicts within five large-scale projects. The outcomes demonstrate that WizardMerge diminishes conflict merging time costs, achieving a 23.85% reduction. Beyond addressing conflicts, WizardMerge provides useful code block classifications and resolving orders for over 70% of the code blocks potentially affected by the conflicts. Notably, WizardMerge exhibits the capability to identify conflict-unrelated code blocks that require manual intervention yet are harmfully applied by Git during the merging.

Existing kernel storage stacks for NVMe SSDs struggle to address performance interference between I/O requests from tenants with different SLAs, leading to the multi-tenancy issue. Addressing this requires separating their I/O requests within the NVMe I/O queues (NQs). However, our analysis reveals that the static CPU core-NQ bindings of current storage stacks restrict their flexibility to achieve this goal.

Hybrid testing, combining fuzz testing and concolic execution, has emerged as an effective technique for bug discovery. However, concolic execution becomes the performance bottleneck when applied to real-world software. Despite numerous approaches to optimize seed scheduling, symbolic simulation, and constraint solving, concolic execution remains inefficient and ineffective due to two limitations. First, the concolic executor and fuzzer do not synchronize the testing state in real time, leading to the generation of numerous duplicate inputs in both concolic execution and the fuzzer. Second, the concolic executor overlooks the independence of constraint solving and solves constraints sequentially, which introduces significant slowdown. In this paper, we first conduct a study to identify these limitations in existing hybrid testing systems. We then propose a novel design for hybrid fuzzing,CherryPicker, where the fuzzer and concolic executor share testing states, and concolic execution runs in parallel mode. Finally, we evaluate our system using the LAVA-M benchmark and real-world software and compare it to state-of-the-art systems. The results demonstrate thatCherryPickeroutperforms current systems in terms of efficiency and effectiveness, delivering improved runtime performance, generating more intriguing inputs, and activating more code. Notably,CherryPickerexclusively uncovers six previously unknown bugs during the evaluation, which have been reported to developers, all of which have been confirmed with three CVEs assigned.