Game Theory Model for Intrusion Prevention in Computer Networks
No system is completely secure, but it’s possible to analyze system vulnerabilities, take security measures and thus reduce the likelihood of system intrusions. One tool to reduce this probability of intrusion is to implement intrusion prevention systems, which analyze network traffic to determine anomalous behavior and take security measures with the agents that exhibit these behaviors. This document proposes a game theory model based in an evaluation of works related in the area that could be the basis of an algorithm for the prevention of intrusions in networks.
A Hybrid Intrusion Detection by Game Theory Approaches in MANET
In general, mobile ad hoc networks (MANET) are formed dynamically by an autonomous system of mobile nodes that are connected via wireless links without using an existing network infrastructure or centralized administration. The hosts establish infrastructure and cooperate to forward data in a multi-hop fashion. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions. In this paper, we proposed a method for prevention internal intruder and detection external intruder by using game theory in mobile ad hoc network. One optimal solution for reducing the resource consumption of detection external intruder is to elect a cluster head for each cluster to provide intrusion service to other nodes in the its cluster, we call this mode, normal mode. Normal mode is only suitable when the probability of attack is low. Once the probability of attack is high, victim nodes should launch their own IDS to detect and thwart intrusions and we call perfect mode. In this paper cluster head should not be malicious or selfish node and must detect external intrusion in its cluster with enough resource and honest behavior. Our hybrid method has three phases: the first phase building trust relationship between nodes and estimation trust value for each node to prevent internal intrusion. In the second phase we propose an optimal method for cluster head election by using trust value; and in the third phase, finding the threshold value for notifying the victim node to launch its IDS once the probability of attack exceeds that value. In first and third phase we apply Bayesian game. Our hybrid method due to using game theory, trust value and honest cluster head election algorithm can effectively improve the network security, performance and reduce resource consumption.
ImageCLEF 2017: ImageCLEF Tuberculosis Task - the SGEast Submission
In this paper, we describe our methodologies in an attempt to improve the diagnosis accuracy of drug resistant tuberculosis and also of identifying the type of tuberculosis present in the patient, as per the requirements of the ImageCLEF tuberculosis task of ImageCLEF 2017. Firstly, we employed the concept of Convolutional Neural Networks (CNN), which can be used to identify useful features in the Computerized Tomography (CT) scans that were provided in the competition and perform the classification based on them. Secondly, Recurrent Neural Networks (RNN) were used on top of CNNs by utilizing CNNs as a feature extractor and the RNNs as a classifier. In order for our model to produce acceptable results, proper preprocessing was performed prior to providing the input data to the models for training, such as image slicing and data augmentation. Our methods were able to reach rank 4 and 5 for the subtask involving drug-resistant tuberculosis and rank 1 and 2 for the other subtask of identifying the tuberculosis type, according to the evaluation performed by ImageCLEF.