Decision tree

Empirical s tudies in the field of the design process s tarted in the 60s. Protocol analysis is among the empirical research methods that have been developed simultaneously with the growth of empirical s tudies. Concurrent with the use of protocol analysis for researching in the field of the design process, analysis methods have been presented by some researchers, which can be used with the protocol analysis method in order to analyze the s tructure of the design process. Among these analysis methods, problem behavior graph, decision tree, linkography, and extended linkography could be mentioned. The problem behavior graph is based on problem-solving theories. In the decision tree method, the extracted data from protocol analysis is used for the perception of decision-making processes. Linkography is another method for analyzing the s tructure of the design process. In this method, the design process of a designer is unfolding by drawing a graph, which is called linkograph. This paper considers making a s tudy and comparison of these different analysis methods by the use of sys tematic review. By comparison of diverse analysis methods, two approaches could be recognized, formal and informal ones. In the formal approach, the design is mentioned as a logical research process of solving the design problem. The second approach is informal. In this one, the design process is mentioned as a reflective conversation with the situation. In this approach, which is based on Donald Schon’s theories, the design process is referred as an argumentative process.

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.