classifier

Intrusion detection is a classification problem in which various machine learning (ML) and data mining (DM) techniques are used to classify network data in normal traffic and attack. In addition, the types of network attacks have changed over the years. This paper tries to compare two models of intrusion detection systems, which include adaptive neuro-fuzzy inference systems (ANFIS) and support vector machines (SVM). In addition, it examines and evaluates several instances of data sets related to intrusion detection systems. In the following, a new hybrid method is proposed that uses Particle Swarm Optimization (PSO) to create a classifier combination to provide better accuracy for intrusion detection. Experimental results show that the new method can produce a better performance based on different evaluation criteria. This paper lists the different datasets for evaluating the IDS model and discusses the performance of the proposed hybrid method on the IDS datasets that can be used to efficiently and effectively use the datasets to develop IDS based on ML and DM.

Steganalysis is one of the challenging and attractive subjects for the scientists interested in the development of steganography methods. Steganalysis is the procedure to detect the hidden information from the stego created by known steganography algorithms. Different kinds of extraction methods have been proposed for steganalysis, each have their own advantages when attacking different kinds of steganography methods. Making a combination of different feature sets will improve the performance of the steganalysis system. Most modern steganalysis algorithms train a supervised classifier on the feature vectors. One of the most popular and most accurate classifier is support vector machine (svm). In this paper, based on experiences and study different ways, an efficient steganalysis method using windowing on high-frequency coefficients of wavelet transform is proposed. In the proposed method, the final decision on the entire images is announced by majority voting technique using extracted convenient features of each window. Experimental and simulation results show that the proposed method for the detection message embedded in images, especially in low rates compared to existing steganalysis methods has more carefully hidden by about 99 percent.