This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by ðÞ regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel ð Þ regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold  such that for a given set of K groups, when the average rate of traffic entering the group end hosts  >  the ratio of the worst-case multicast delay bound of the proposed ð Þ regulator over the traditional ð Þ regulator is Oð 1 KnÞ for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations.

Fast and accurate generation of worm signatures is essential to contain zero-day worms at the Internet scale. Recent work has shown that signature generation can be automated by analyzing the repetition of worm substrings (that is, fingerprints) and their address dispersion. However, at the early stage of a worm outbreak, individual edge networks are often short of enough worm exploits for generating accurate signatures. This paper presents both theoretical and experimental results on a collaborative worm signature generation system (WormShield) that employs distributed fingerprint filtering and aggregation over multiple edge networks. By analyzing real-life Internet traces, we discovered that fingerprints in background traffic exhibit a Zipf-like distribution.

Due to their very nature, wireless sensor networks are probably the category of wireless networks most vulnerable to "radio channel jamming”-based Denial-of-Service (DoS) attacks. An adversary can easily mask the events that the sensor network should detect by stealthily jamming an appropriate subset of the nodes; in this way, he prevents them from reporting what they are sensing to the network operator. Therefore, even if an event is sensed by one or several nodes (and the sensor network is otherwise fully connected), the network operator cannot be informed on time. We show how the sensor nodes can exploit channel diversity in order to create wormholes that lead out of the jammed region, through which an alarm can be transmitted to the network operator. We propose three solutions: The first is based on wired pairs of sensors, the second relies on frequency hopping, and the third is based on a novel concept called uncoordinated channel hopping. We develop appropriate mathematical models to study the proposed solutions.

This paper introduces WiSE, a sender-side, transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP’s multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or to radio channel errors. At the same time, the available bandwidth of the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth. If the current path is severely congested and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path using SCTP’s flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to TCP an  the standard SCTP implementation.

Modeling mobility and user behavior is of fundamental importance in testing the performance of protocols for wireless data
networks. Although several models have been proposed in the literature, none of them can at the same time capture important features
such as geographical mobility, user-generated traffic, and the wireless technology at hand. When collectively considered, these three
aspects determine the user-perceived quality-of-service (QoS) level, which, in turn, might have an influence on the mobility of those
users (we call them QoS-driven users) who do not display constrained mobility patterns, but they can decide to move to less congested
areas of the network in case their perceived QoS level becomes unacceptable.