DESIGN A SECURITY FIREWALL POLICY TO FILTER INCOMING TRAFFIC IN PACKET SWITCHED NETWORKS USING CLASSIFICATION METHODS

Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires to overcome and filter a range of special attacks or issues in network. In this paper, we present a machine learning based algorithm that filter Denial of Service (DoS) attacks in networks. This filtering algorithm has been designed by using a classification algorithm based on principal component and correlation based filters. We show good quality and performance of our algorithm experimentally by executing our algorithm on a several packet flow data sets.Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires to overcome and filter a range of special attacks or issues in network. In this paper, we present a machine learning based algorithm that filter Denial of Service (DoS) attacks in networks. This filtering algorithm has been designed by using a classification algorithm based on principal component and correlation based filters. We show good quality and performance of our algorithm experimentally by executing our algorithm on a several packet flow data sets

Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity