Biblio

In the area of cloud security, detection of DDoS attack is a challenging task such that legitimate users use the cloud resources properly. So in this paper, detection and classification of the attacking packets and normal packets are done by using various machine learning classifiers. We have selected the most relevant features from NSL KDD dataset using five (Information gain, gain ratio, chi-squared, ReliefF, and symmetrical uncertainty) commonly used feature selection methods. Now from the entire selected feature set, the most important features are selected by applying our hybrid feature selection method. Since all the anomalous instances of the dataset do not belong to DDoS category so we have separated only the DDoS packets from the dataset using the selected features. Finally, the dataset has been prepared and named as KDD DDoS dataset by considering the selected DDoS packets and normal packets. This KDD DDoS dataset has been discretized using discretize tool in weka for getting better performance. Finally, this discretize dataset has been applied on some commonly used (Naive Bayes, Bayes Net, Decision Table, J48 and Random Forest) classifiers for determining the detection rate of the classifiers. 10 fold cross validation has been used here for measuring the robustness of the system. To measure the efficiency of our hybrid feature selection method, we have also applied the same set of classifiers on the NSL KDD dataset, where it gives the best anomaly detection rate of 99.72% and average detection rate 98.47% similarly, we have applied the same set of classifiers on NSL DDoS dataset and obtain the average DDoS detection of 99.01% and the best DDoS detection rate of 99.86%. In order to compare the performance of our proposed hybrid method, we have also applied the existing feature selection methods and measured the detection rate using the same set of classifiers. Finally, we have seen that our hybrid approach for detecting the DDoS attack gives the best detection rate compared to some existing methods.

Network security became a viral topic nowadays, Anomaly-based Intrusion Detection Systems [1] (IDSs) plays an indispensable role in identifying the attacks from networks and the detection rate and accuracy are said to be high. The proposed work explore this topic and solve this issue by the IDS model developed using Artificial Neural Network (ANN). This model uses Feed - Forward Neural Net algorithms and Probabilistic Neural Network and oppositional based on Particle Swarm optimization Algorithm for lessen the computational overhead and boost the performance level. The whole computing overhead produced in its execution and training are get minimized by the various optimization techniques used in these developed ANN-based IDS system. The experimental study on the developed system tested using the standard NSL-KDD dataset performs well, while compare with other intrusion detection models, built using NN, RB and OPSO algorithms.

Digital information security is the field of information technology which deal with all about identification and protection of information. Whereas, identification of the threat of any Intrusion Detection System (IDS) in the most challenging phase. Threat detection become most promising because rest of the IDS system phase depends on the solely on "what is identified". In this view, a multilayered framework has been discussed which handles the underlying features for the identification of various attack (DoS, R2L, U2R, Probe). The experiments validates the use SVM with genetic approach is efficient.