Abstract — The popularity of peer-to-peer (P2P) networks makes them an attractive target to the creators of viruses and other malicious code. Recently a number of viruses designed specifically to spread via P2P networks have emerged. Pollution has also become increasingly prevalent as copyright holders inject multiple decoy versions in order to impede item distribution. In this paper we derive deterministic epidemiological models for the propagation of a P2P virus through a P2P network and the dissemination of pollution. We report on discrete simulations that provide some verification that the models remain sufficiently accurate despite variations in individual peer conduct to provide insight into the behaviour of the system. The paper examines the steady-state behaviour and illustrates how the models may be used to estimate in a computationally efficient manner how effective object reputation schemes will be in mitigating the impact of viruses and preventing the spread of pollution. I.

Wireless sensor-actuator networks (SANETs), in which nodes perform actions (actuation) in response to sensor measurements and shared information, have great potential in medical and agricultural applications. In this paper, we focus on the problem of using distributed sensed data to design actuation strategies in order to elicit a desired response from the environment, whilst attempting to minimize the communication in the network. Our methodology is based on batch Q-learning; we describe a distributed approach for learning dyadic regression trees to estimate the Q-functions from collected data. Analysis and simulation indicate that substantial communication savings that can be achieved through distributed learning without significant performance deterioration. The simulations also reveal that the performance of our technique depends strongly on the amount of training data available

Several recent price-based congestion control schemes require relatively accurate path price estimates for successful operation. The proposed addition of the two-bit explicit congestion notification (CCN) field in the IP header provides routers with a mechanism for conveying price information. Recently, two proposals have emerged for probabilistic packet marking at the routers; the proposals allow receivers to estimate path price from the fraction of marked packets. In this paper we introduce an alternative deterministic marking scheme for encoding path price. Under our approach, each router quantizes the price of its outgoing link to a fixed number of bits. We then make use of the IP identification (IPid) field to map data packets to different probe types, and each probe type calculates a partial sum of the path price bits. A router deduces its marking behaviour according to the IPid and the TTL (time to live) field of each packet. We evaluate the performance of our algorithm in terms of its error in representing the end-to-end price, and compare it to probabilistic marking. We show that based on empirical Internet traffic characteristics, our algorithm performs better when estimating path price using small blocks of packets. We also derive the probability distribution of the error for our scheme, and provide a relatively simple bound on its maximum mean-squared error.