Algorithms for self-healing networks

Many modern networks are reconfigurable, in the sense that the topology of the network can be changed by the nodes in the network. For example, peer-to-peer, wireless and ad-hoc networks are reconfigurable. More generally, many social networks, such as a company\u27s organizational chart; infrastructure networks, such as an airline\u27s transportation network; and biological networks, such as the human brain, are also reconfigurable. Modern reconfigurable networks have a complexity unprecedented in the history of engineering, resembling more a dynamic and evolving living animal rather than a structure of steel designed from a blueprint. Unfortunately, our mathematical and algorithmic tools have not yet developed enough to handle this complexity and fully exploit the flexibility of these networks. We believe that it is no longer possible to build networks that are scalable and never have node failures. Instead, these networks should be able to admit small, and, maybe, periodic failures and still recover like skin heals from a cut. This process, where the network can recover itself by maintaining key invariants in response to attack by a powerful adversary is what we call self-healing. Here, we present several fast and provably good distributed algorithms for self-healing in reconfigurable dynamic networks. Each of these algorithms have different properties, a different set of gaurantees and limitations. We also discuss future directions and theoretical questions we would like to answer

Report on BCTCS 2016: The 32nd British Colloquium for Theoretical Computer Science 22–24 March 2016, Queen’s University Belfast

Report on BCTCS 2016: The 32nd British Colloquium for Theoretical Computer Science 22–24 March 2016, Queen’s University Belfas

On the termination of flooding

Flooding is among the simplest and most fundamental of all graph/network algorithms. Consider a (distributed network in the form of a) finite undirected graph G with a distinguished node v that begins flooding by sending copies of the same message to all its neighbours and the neighbours, in the next round, forward the message to all and only the neighbours they did not receive the message from in that round and so on. We assume that nodes do not keep a record of the flooding event, thus, raising the possibility that messages may circulate infinitely even on a finite graph. We call this history-less process amnesiac flooding (to distinguish from a classic distributed implementation of flooding that maintains a history of received messages to ensure a node never sends the same message again). Flooding will terminate when no node in G sends a message in a round, and, thus, subsequent rounds. As far as we know, the question of termination for amnesiac flooding has not been settled - rather, non-termination is implicitly assumed.In this paper, we show that surprisingly synchronous amnesiac flooding always terminates on any arbitrary finite graph and derive exact termination times which differ sharply in bipartite and non-bipartite graphs. In particular, synchronous flooding terminates in e rounds, where e is the eccentricity of the source node, if and only if G is bipartite, and, otherwise, in j rounds where e For comparison, we also show that, for asynchronous networks, however, an adaptive adversary can force the process to be non-terminating.</div

Intrusion Detection in Critical SD-IoT Ecosystem

The Internet of Things (IoT) connects physical objects with intelligent decision-making support to exchange information and enable various critical applications. The IoT enables billions of devices to connect to the Internet, thereby collecting and exchanging real-time data for intelligent services. The complexity of IoT management makes it difficult to deploy and manage services dynamically. Thus, in recent times, Software Defined Network (SDN) has been widely adopted in IoT service management to provide dynamic and adaptive capabilities to the traditional IoT ecosystem. This has resulted in the evolution of a new paradigm known as Software-defined IoT (SD-IoT). Although there are several benefits of SD-IoT, it also opens new frontiers for attackers to introduce attacks and intrusions. Specifically, it becomes challenging working in a critical IoT environment where any delay or disruption caused by an intruder can be life-threatening or can cause significant destruction. However, given the flexibility of SDN, it is easier to deploy different intrusion detection systems that can detect attacks or anomalies promptly. Thus, in this paper, we have deployed a hybrid architecture that allows monitoring, analysis, and detection of attacks and anomalies in the SD-IoT ecosystem. In this work, we have considered three scenarios, a) denial of services, b) distributed denial of service, and c) packet fragmentation. The work is validated using simulated experiments performed using SNORT deployed on the Mininet platform for three scenarios

Algorithms for self-healing networks

Many modern networks are reconfigurable, in the sense that the topology of the network can be changed by the nodes in the network. For example, peer-to-peer, wireless and ad-hoc networks are reconfigurable. More generally, many social networks, such as a company's organizational chart; infrastructure networks, such as an airline's transportation network; and biological networks, such as the human brain, are also reconfigurable. Modern reconfigurable networks have a complexity unprecedented in the history of engineering, resembling more a dynamic and evolving living animal rather than a structure of steel designed from a blueprint. Unfortunately, our mathematical and algorithmic tools have not yet developed enough to handle this complexity and fully exploit the flexibility of these networks. We believe that it is no longer possible to build networks that are scalable and never have node failures. Instead, these networks should be able to admit small, and, maybe, periodic failures and still recover like skin heals from a cut. This process, where the network can recover itself by maintaining key invariants in response to attack by a powerful adversary is what we call self-healing. Here, we present several fast and provably good distributed algorithms for self-healing in reconfigurable dynamic networks. Each of these algorithms have different properties, a different set of gaurantees and limitations. We also discuss future directions and theoretical questions we would like to answer.Computer ScienceDoctoralUniversity of New Mexico. Dept. of Computer ScienceSaia, JaredHayes, ThomasMoore, CrisBerger-Wolf, Tany