A cognitive based Intrusion detection system

Intrusion detection is one of the primary mechanisms to provide computer networks with security. With an increase in attacks and growing dependence on various fields such as medicine, commercial, and engineering to give services over a network, securing networks have become a significant issue. The purpose of Intrusion Detection Systems (IDS) is to make models which can recognize regular communications from abnormal ones and take necessary actions. Among different methods in this field, Artificial Neural Networks (ANNs) have been widely used. However, ANN-based IDS, has two main disadvantages: 1- Low detection precision. 2- Weak detection stability. To overcome these issues, this paper proposes a new approach based on Deep Neural Network (DNN. The general mechanism of our model is as follows: first, some of the data in dataset is properly ranked, afterwards, dataset is normalized with Min-Max normalizer to fit in the limited domain. Then dimensionality reduction is applied to decrease the amount of both useless dimensions and computational cost. After the preprocessing part, Mean-Shift clustering algorithm is the used to create different subsets and reduce the complexity of dataset. Based on each subset, two models are trained by Support Vector Machine (SVM) and deep learning method. Between two models for each subset, the model with a higher accuracy is chosen. This idea is inspired from philosophy of divide and conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to reduce the error from the previous step, an ANN model is trained to gain and use the results in order to be able to predict the attacks. We can reach to 95.4 percent of accuracy. Possessing a simple structure and less number of tunable parameters, the proposed model still has a grand generalization with a high level of accuracy in compared to other methods such as SVM, Bayes network, and STL.Comment: 18 pages, 6 figure

A 10-watt CW photodissociation laser with IODO perfluoro-tert-butane

NASA has been investigating the feasibility of direct solar-pumped laser systems for power beaming in space. Among the various gas, liquid, and solid laser systems being proposed as candidates for solar-pumped lasers, the iodine photodissociation gas laser has demonstrated its potential for space application. Of immediate attention is the determination of system requirements and the choice of lasants to improve the system efficiency. The development of an efficient iodine laser depends on the availability of a suitable iodide which has favorable laser kinetics, chemically reversibility, and solar energy utilization. Among the various alkyliodide lasants comparatively tested in a long-pulse system, perfluoro- tert-butyl iodide, T-C4F9I, was found to be the best. However, the operating conditions for the laser medium in a continuously pumped and continuous-flow iodine laser differ considerably from those in the pulsed regime. The results of the continuous wave (CW)) laser performance from t-C4F9I are reported. Perfluoro- n-propyl iodide, n-C3F7I is used for comparison because of its universal use in photodissociation iodine lasers