Simulation based network protocol testing requires that one model reality correctly. Mis-modelling can render results invalid. Hence, one must determine the context in which a solution is to be implemented for the results to be meaningful. In this work, we investigate whether the security augmentation technique DCFM (denial contradictions with fictitious nodes) for OLSR based mobile ad-hoc networks (MANET) is valid for multiple contexts. We accomplish this by running the node isolation attack and DCFM defense on a variety of mobility models, comparing the results. We show that for most models the technique performs well, while for a specific scenario (e.g. street based) it fails. Additionally we describe the infrastructure that was built to support the street based simulations.

Although quite popular for the protection for ad-hoc networks (MANETs, IoT, VANETs, etc.), detection & mitigation techniques only function after the attack has commenced. Prevention, however, attempts at thwarting an attack before it is executed. Both techniques can be realized either by the collective collaboration of network nodes (i.e., adding security messages to protocols) or by internal deduction of attack state. In this paper, we propose a method for minimizing the gray-hole DoS attack. Our solution assumes no explicit node collaboration, with each node using only internal knowledge gained by routine routing information. The technique was evaluated using five different threat models (different attacker capabilities), allowing for a better understanding of the attack surface and its prevention. Our simulation results show a decrease of up to 51 percent in previously dropped packet, greatly minimizing gray-hole attack effectiveness.

Black-holes, gray-holes and, wormholes, are devastating to the correct operation of any network. These attacks (among others) are based on the premise that packets will travel through compromised nodes, and methods exist to coax routing into these traps. Detection of these attacks are mainly centered around finding the subversion in action. In networks, bottleneck nodes -- those that sit on many potential routes between sender and receiver -- are an optimal location for compromise. Finding naturally occurring path bottlenecks, however, does not entitle network subversion, and as such are more difficult to detect. The dynamic nature of mobile ad-hoc networks (manets) causes ubiquitous routing algorithms to be even more susceptible to this class of attacks. Finding perceived bottlenecks in an olsr based manet, is able to capture between 50%-75% of data. In this paper we propose a method of subtly expanding perceived bottlenecks into complete bottlenecks, raising capture rate up to 99%; albeit, at high cost. We further tune the method to reduce cost, and measure the corresponding capture rate.

With the main focus of research in routing protocols for Mobile Ad-Hoc Networks (MANET) geared towards routing efficiency, the resulting protocols tend to be vulnerable to various attacks. Over the years, emphasis has also been placed on improving the security of these networks. Different solutions have been proposed for different types of attacks, however, these solutions often compromise routing efficiency or network overload. One major DOS attack against the Optimized Link State Routing protocol (OLSR) known as the node isolation attack occurs when topological knowledge of the network is exploited by an attacker who is able to isolate the victim from the rest of the network and subsequently deny communication services to the victim. In this paper, we suggest a novel solution to defend the OLSR protocol from node isolation attack by employing the same tactics used by the attack itself. Through extensive experimentation, we demonstrate that 1) the proposed protection prevents more than 95 percent of attacks, and 2) the overhead required drastically decreases as the network size increases until it is non-discernable. Last, we suggest that this type of solution can be extended to other similar DOS attacks on OLSR.

We show how automatically extracted citations in historical corpora can be used to measure the direct and indirect influence of authors on each other. These measures can in turn be used to determine an author's overall prominence in the corpus and to identify distinct schools of thought. We apply our methods to two major historical corpora. Using scholarly consensus as a gold standard, we demonstrate empirically the superiority of indirect influence over direct influence as a basis for various measures of authorial impact.