Internet topology at the network layer consists of routers and subnets, i.e., point-to-point or multi-access connections. Network measurement studies have focused on router level maps and derived characteristics of routers such as mean degree, degree distribution, clustering coefficient and betweenness. Considering the fact that subnets are also important building blocks of the Internet topology, this paper introduces a complementary view of network topologies named subnet level maps. Subnet level network topology maps represent subnets as vertices and depict routers as links connecting the vertices/subnets. Additionally, we introduce a tool, called exploreNET, for subnet discovery. Although ExploreNET is based on the same principals as our recent work traceNET [21], it differs from traceNET in its utilization in various domains. Particularly, it allows us discover the underlying subnet level topology map of a network rather than the map dictated by routing dynamics. Finally, we present an evaluation of exploreNET by using it to discover and analyze various subnet characteristics including degree distribution, capacity distribution and utilization for six geographically disperse public Internet Service Providers (ISPs).

Abstract-Telesurgical Robot Systems (TRSs) address mission critical operations emerging in extreme fields such as battlefields, underwater, and disaster territories. The lack of wirelined communication infrastructure in such fields makes the use of wireless technologies including satellite and ad-hoc networks inevitable. TRSs over wireless environments pose unique challenges such as preserving a certain reliability threshold, adhering some maximum tolerable delay, and providing various security measures depending on the nature of the operation and communication environment. In this study we present a novel approach that uses information coding to integrate both light-weight privacy and adaptive reliability in a single protocol called Secure and Statistically Reliable UDP (SSR-UDP). We prove that the offered security is equivalent to the existing AES-based long key crypto systems, yet, with significantly less computational overhead. Additionally, we demonstrate that the proposed scheme can meet high reliability and delay requirements of TRS applications in highly lossy environments while optimizing the bandwidth use. Our proposed SSR-UDP protocol can also be utilized in other similar cyber-physical wireless application domains.

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Attacks launched over the Internet often degrade or disrupt the quality of online services. Various Intrusion Detection Systems (IDSs), with or without prevention capabilities, have been proposed to defend networks or hosts against such attacks. While most of these IDSs extract features from the packet headers to detect any irregularities in the network traffic, some others use payloads alongside the headers. In this study, we propose a payload-based intrusion detection scheme, PayloadEmbeddings, using byte embeddings of the payloads of network packets. We employ a shallow neural network to generate vector representations for bytes and their corresponding payloads. Our feature extraction technique is coupled with the k-Nearest Neighbours (kNN) algorithm for the classification of packets as intrusive or non-intrusive.In our experiments, we evaluated 34 publicly available datasets, and used ten distinct payload-based, labeled intrusion detection datasets to train and evaluate our approach. Our empirical results show that PayloadEmbeddings reaches between 75% and 99% accuracy across all datasets. Finally, we compare our approach to other state-of-the-art and traditional intrusion detection techniques. Our findings suggest that PayloadEmbeddings demonstrates significant advantages over the other techniques on most of the datasets.

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