Abstract: Industrial Control Systems (ICS) such as Supervisory Control And Data Acquisition (SCADA) can be seen in many different critical infrastructures, from nuclear management to utility, medical equipment, power, waste and engine management on ships and planes. The role SCADA plays in critical infrastructure has resulted in a call to secure them. Many lives depend on it for daily activities and the attack vectors are becoming more sophisticated. Hence, the security of ICS is vital as malfunction of it might result in huge risk. This paper describes how the application of Prey Predator (PP) approach in flocks of birds could enhance the detection of malicious activities on ICS. The PP approach explains how these animals in groups or flocks detect predators by following some simple rules. They are not necessarily very intelligent animals but their approach in solving complex issues such as detection through corporation, coordination and communication worth emulating. This paper will emulate flocking behavior seen in birds in detecting predators. The PP approach will adopt six nearest bird approach in detecting any predator. Their local and global bests are based on the individual detection as well as group detection. The PP algorithm was designed following MapReduce methodology that follows a Split Detection Convergence (SDC) approach.
Abstract: In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments.
Abstract: Most of researches for conventional simulations were
studied focusing on flocks with a single species. While there exist the
flocking behaviors with a single species in nature, the flocking
behaviors are frequently observed with multi-species. This paper
studies on the flocking simulation for heterogeneous agents. In order
to simulate the flocks for heterogeneous agents, the conventional
method uses the identifier of flock, while the proposed method defines
the feature vector of agent and uses the similarity between agents by
comparing with those feature vectors. Based on the similarity, the
paper proposed the attractive force and repulsive force and then
executed the simulation by applying two forces. The results of
simulation showed that flock formation with heterogeneous agents is
very natural in both cases. In addition, it showed that unlike the
existing method, the proposed method can not only control the density
of the flocks, but also be possible for two different groups of agents to
flock close to each other if they have a high similarity.