Biblio

Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.

Hardware Trojans (HTs) are malicious modifications of the original circuits intended to leak information or cause malfunction. Based on the Side Channel Analysis (SCA) technology, a set of hardware Trojan detection platform is designed for RTL circuits on the basis of HSPICE power consumption simulation. Principal Component Analysis (PCA) algorithm is used to reduce the dimension of power consumption data. An intelligent neural networks (NN) algorithm based on Particle Swarm Optimization (PSO) is introduced to achieve HTs recognition. Experimental results show that the detection accuracy of PSO NN method is much better than traditional BP NN method.

Virtualization based memory isolation has been widely used as a security primitive in many security systems. This paper firstly provides an in-depth analysis of its effectiveness in the multicore setting, a first in the literature. Our study reveals that memory isolation by itself is inadequate for security. Due to the fundamental design choices in hardware, it faces several challenging issues including page table maintenance, address mapping validation and thread identification. As demonstrated by our attacks implemented on XMHF and BitVisor, these issues undermine the security of memory isolation. Next, we propose a new isolation approach that is immune to the aforementioned problems. In our design, the hypervisor constructs a fully isolated micro computing environment (FIMCE) that exposes a minimal attack surface to an untrusted OS on a multicore platform. By virtue of its architectural niche, FIMCE offers stronger assurance and greater versatility than memory isolation. We have built a prototype of FIMCE and measured its performance. To show the benefits of using FIMCE as a building block, we have also implemented several practical applications which cannot be securely realized by using memory isolation alone.