IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior.

Abstract - This research proposes “Integrated Network Intrusion Detection System (INIDS)” which is NIDS for wire/wireless networks. INIDN consider features of the three layers; transport and Internet layers for wire and data link layer for wireless. The proposal is a Data Mining (DM)-based INIDS, which trained over a labeled wire and wireless datasets (each transaction labeled normal, intrusion name or unknown), INIDS is a hybrid IDS (anomaly and misuse). INIDS, train and construct two separated proposed models these are, Wire-NIDS and Wireless-NIDS then integrate the two models to build the final INIDS. Wire-NIDS use NSL-KDD dataset; use Principle Component Analysis (PCA) as a feature extraction, and use Support Vector Machine (SVM) with Artificial Neural Network (ANN) as classifiers. Wireless-NIDS use proposed Wdataset dataset, use Gain Ratio (GR) as feature selection, and use Naïve Bayesian (NB) as a classifier. The results obtained from executing the proposed INIDS model showing that Wire-NIDS and Wireless-NIDS classifier accuracy and detection rate is generally higher with the subset of features obtained by PCA (8 from 41) and GR (8 from 17) than with all sets of features. Proposed confusion matrix of INIDS gives less confusion in detection rates with reduced features.

Keywords: IDS, SVM, ANN, NB, PCA, and GR.