Computer game is a billion-dollar industry and is booming. Testing games has been recognized as a difficult task, which mainly relies on manual playing and scripting based testing. With the advances in technologies, computer games have become increasingly more interactive and complex, thus play-testing using human participants alone has become unfeasible. In recent days, play-testing of games via autonomous agents has shown great promise by accelerating and simplifying this process. Reinforcement Learning solutions have the potential of complementing current scripted and automated solutions by learning directly from playing the game without the need of human intervention. This paper presented an approach based on reinforcement learning for automated testing of 3D games. We make use of the notion of curiosity as a motivating factor to encourage an RL agent to explore its environment. The results from our exploratory study are promising and we have preliminary evidence that reinforcement learning can be adopted for automated testing of 3D games.

The Session Initiation Protocol (SIP) is at the root of many sessions-based applications such as VoIP and media streaming that are used by a growing number of users and organizations. The increase of the availability and use of such applications calls for careful attention to the possibility of transferring malformed, incorrect, or malicious SIP messages as they can cause problems ranging from relatively innocuous disturbances to full blown attacks and frauds. To this end, SIP messages are analyzed to be classified as “good” or “bad” depending on whether this structure and content are deemed acceptable or not. This paper presents a classifier of SIP messages based on a two stage filter. The first stage uses a straightforward lexical analyzer to detect and remove all messages that are lexically incorrect with reference to the grammar that is defined by the protocol standard. The second stage uses a machine learning approach based on a Support Vector Machine (SVM) to analyze the structure of the remaining syntactically correct messages in order to detect semantic anomalies which are deemed a strong indication of a possibly malicious message. The SVM “learns” the structure of the “good” and “bad” SIP messages through an initial training phase and the SVM thus configured correctly classifies messages produced by a synthetic generator and also “real” SIP messages that have been collected from the communication network at our institution. The preliminary results of such classification look very promising and are presented in the final section of this paper.

Voice and multimedia communications are rapidly migrating from traditional networks to TCP/IP networks (Internet), where services are provisioned by SIP (Session Initiation Protocol). This paper proposes an on-line filter that examines the stream of incoming SIP messages and classifies them as good or bad. The classification is carried out in two stages: first a lexical analysis is performed to weed out those messages that do not belong to the language generated by the grammar defined by the SIP standard. After this first stage, a second filtering occurs which identifies messages that somehow differ - in structure or contents - from messages that were previously classified as good. While the first filter stage is straightforward, as the classification is crisp (either a messages belongs to the language or it does not), the second stage requires a more delicate handling, as it is not a sharp decision whether a message is semantically meaningful or not. The approach we followed for this step is based on using past experience on previously classified messages, i.e. a "learn-by-example" approach, which led to a classifier based on Support-Vector-Machines (SVM) to perform the required analysis of each incoming SIP message. The paper describes the overall architecture of the two-stage filter and then explores several points of the configuration-space for the SVM to determine a good configuration setting that will perform well when used to classify a large sample of SIP messages obtained from real traffic collected on a VoIP installation at our institution. Finally, the performance of the classification on additional messages collected from the same source is presented.

Mobile Ad hoc Networks (MANETs) consist of a large number of relatively low-powered mobile nodes communicating in a network using radio signals. Clustering is one of the techniques used to manage data exchange amongst interacting nodes. Each group of nodes has one or more elected Cluster head(s), where all Cluster heads are interconnected for forming a communication backbone to transmit data. Moreover, Cluster heads should be capable of sustaining communication with limited energy sources for longer period of time. Misbehaving nodes and cluster heads can drain energy rapidly and reduce the total life span of the network. In this context, selection of best cluster heads with trusted information becomes critical for the overall performance. In this paper, we propose Cluster head(s) selection algorithm based on an efficient trust model. This algorithm aims to elect trustworthy stable cluster head(s) that can provide secure communication via cooperative nodes. Simulations were conducted to evaluate trusted Cluster head(s) in terms of clusters stability, longevity and throughput.

The inherent freedom in self-organized mobile ad-hoc networks (MANETs) introduces challenges for trust management; particularly when nodes do not have any prior knowledge of each other. Furthermore in MANETs, the nodes themselves should be responsible for their own security. We propose a novel approach for trust management in MANETs that is based on the nodes' own responsibility of building their trust level and node-level trust monitoring. The main contribution of this work is in the introduction of a Node based Trust Management (NTM) scheme in MANET based on the assumption that individual nodes are themselves responsible for their own trust level. We explore and develop the mathematical framework of trust in NTM. Finally, in this context, we demonstrate our scheme with notations, algorithms, analytical model and prove of its correctness.