zahraa mohammed ali hatem algelal

Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.

Human face Recognition systems are increasingly gaining more importance and can be utilized throughout many applications like video surveillance, Security, human-computer intelligent interaction, etc. this paper presents performance comparison between three feature extraction techniques for an automatic face recognition system. In the first step, we benefit from wavelet Transforms, Principal Component Analysis (PCA) and combining Wavelet with PCA as feature extracting methods. After feature vectors generation, linear and nonlinear Support Vector Machines (SVM) are usually used for implementing the classification or recognition step. These methods are compared on accuracy in an ORL database for face recognition applications including 400 images of 40 people.

Abstract— In this paper, an exact and proactive technique is created to distinguish Distributed Denial of Service (DDoS) attacks.This is achieved by using an entropy concept to measure abnormal traffic changes according to the phases of the attack. This traffic is then clustered by using a modified DBSCAN algorithm, and the centroids for the resulting clusters are then used as patterns for efficient distance-based classification to detect DDoS attacks. The experimental results showed that the classification based on DBSCAN centroids achieved accuracy of more than (98%). This system is characterized by processing and analyzing high-speed network traffic (by using entropy), discovering and accurately identifying types of DDoS attacks to reduce false alarms, and early detecting DDoS attacks in real time.

Distributed Denial of Service (DDoS) attack, has become one of the major threats to the Internet. It makes a victim to deny providing normal services in the Internet by generate huge useless packets by a large number of agents and can easily exhaust the computing and communication resources of a victim .In this paper we develop method to detect DDoS attacks accurately and proactively. This can be achieved using entropy concept to measure abnormal change in traffic according to the phases of the attack , and then these traffics are clustered using DBSCAN algorithm. The patterns for DDoS traffic is created based on extracted centroid points from each cluster, which are used in testing phase using Distances-based classification . This system is characterized processing and analyzing of high-speed network traffic (based on entropy approach ), discovering and accurately identifying new types of DDoS attack to reduce the false alarms (FA) , detecting this attack in real time and making use of pattern in the train stage to increase detection ratio.

Artificial Intelligence and its role against coronavirius

Publication of a research entitled: Botnet detection using ensemble classifiers of network flow

Participation in an electronic lecture: Challenges and Research Directions in fog computing