Mobile eye-tracking in external environments remains challenging, despite recent advances in eye-tracking software and hardware engineering. Many current methods fail to deal with the vast range of outdoor lighting conditions and the speed at which these can change. This confines experiments to artificial environments where conditions must be tightly controlled. Additionally, the emergence of low-cost eye tracking devices calls for the development of analysis tools that enable non-technical researchers to process the output of their images. We have developed a fast and accurate method (known as “SET”) that is suitable even for natural environments with uncontrolled, dynamic and even extreme lighting conditions. We compared the performance of SET with that of two open-source alternatives by processing two collections of eye images: images of natural outdoor scenes with extreme lighting variations (“Natural”); and images of less challenging indoor scenes (“CASIA-Iris-Thousand”). We show that SET excelled in outdoor conditions and was faster, without significant loss of accuracy, indoors. SET offers a low cost eye-tracking solution, delivering high performance even in challenging outdoor environments. It is offered through an open-source MATLAB toolkit as well as a dynamic-link library (“DLL”), which can be imported into many programming languages including C# and Visual Basic in Windows OS (www.eyegoeyetracker.co.uk).
In order to attack to a network, an attacker first must find vulnerability points of the target network. This task is done through scanning. There are many methods of scan detection. Most of these methods are based on thresholding. Setting a proper threshold value is crucial and depends on many parameters such as network structure and time window. In this study we proposed a new scan detection method based on genetic algorithm (GA). This method has two phases. In the first phase we separate normal traffic from suspicious traffic and send only suspicious traffic to the second phase. This way the overhead of the process in the second phase is decreased considerably. In the second phase we aim to detect attacks with respect to two optimum parameters of threshold and memory. We compared our method with snort. Results showed that our method achieves better performance in both hit rate and false alarm rate.
Due to increasing number of network attacks, it is highly crucial to equip networks with an intrusion detection system (IDS). These systems must be able to deal with today's high speed and large scale networks. In this paper we propose a distributed IDS that performs both data capturing and data analyzing in a distributed fashion. This distributed mechanism enables our system to effectively operate within large scale and high traffic rate networks. We developed a grouping mechanism which divides computers in the network into subsets of computers with a leader and a few members. Subsequently, using a data sharing mechanism we were able to detect distributed attacks. Our data sharing mechanism added an overhead on the network traffic which is negligible compared to the overall network traffic. We simulated our method in NS2 simulation environment. Then we compared our proposed system with a centralized IDS in terms of detection rate, memory usage and packet loss rate. Results showed that our system's performance was better despite of some extra load imposed by distribution of data processing.
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