Biblio
FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.
This paper focuses on optimizing the sigmoid filter for detecting Low-Rate DoS attacks. Though sigmoid filter could help for detecting the attacker, it could severely affect the network efficiency. Unlike high rate attacks, Low-Rate DoS attacks such as ``Shrew'' and ``New Shrew'' are hard to detect. Attackers choose a malicious low-rate bandwidth to exploit the TCP's congestion control window algorithm and the re-transition timeout mechanism. We simulated the attacker traffic by editing using NS3. The Sigmoid filter was used to create a threshold bandwidth filter at the router that allowed a specific bandwidth, so when traffic that exceeded the threshold occurred, it would be dropped, or it would be redirected to a honey-pot server, instead. We simulated the Sigmoid filter using MATLAB and took the attacker's and legitimate user's traffic generated by NS-3 as the input for the Sigmoid filter in the MATLAB. We run the experiment three times with different threshold values correlated to the TCP packet size. We found the probability to detect the attacker traffic as follows: the first was 25%, the second 50% and the third 60%. However, we observed a drop in legitimate user traffic with the following probabilities, respectively: 75%, 50%, and 85%.