Intruders detection in computer networks has some deficiencies from machine learning approach, given by the nature of the application. The principal problem is the modest display of detection systems based on learning algorithms under the constraints imposed by real environments. This article focuses on the machine learning approach for network intrusion detection in batch and data stream envir...

Generally, Intrusion Detection Systems (IDS) work using two methods of identification of attacks: by signatures, that are specific defined elements of the network traffic possible to identify and by anomalies being some deviation form of the network behaviour assumed as normal. Recently, some attempts have been made to implement artificial intelligence method for detection of attacks. Many such...

Although KDD99 dataset is more than 15 years old, it is still widely used in academic research. To investigate wide usage of this dataset in Machine Learning Research (MLR) and Intrusion Detection Systems (IDS); this study reviews 149 research articles from 65 journals indexed in Science Citation Index Expanded and Emerging Sources Citation Index during the last six years (2010–2015). If we inc...

9 This study investigates the effects of using a large data set on supervised machine learning classifiers in the domain of Intrusion Detection Systems (IDS). To investigate this effect 12 machine learning algorithms have been applied. These algorithms are: (1) Adaboost, (2) Bayesian Nets, (3) Decision Tables, (4) Decision Trees (J48), (5)Logistic Regression, (6) Multi-Layer Perceptron, (7) Nai...

Securing data in a networked environment has been a major concern for Network Administrator as intruders may get access and steal the information available in the Computer network. As absolute security is not possible in a network, detecting intrusion is very important from the standpoint of protection of the information as well as the network. The paper intends to cover the development of an I...

The rapid evolution of network intrusions has rendered traditional Intrusion Detection Systems (IDS) insufficient for cyber attacks such as the Advanced Persistent Threats (APT), which are sophisticated and enduring network intrusion campaigns comprising multiple imperceptible steps of malicious cyber activities. Dealing with such elaborated network intrusions calls for novel and more proactive...

computing environment is continually growing and changing with new technology and the Internet. In addition, vulnerabilities in this environment are also steadily increasing. So Intrusion Detection Systems (IDS) have turn out to be an important part in provisions of computer and network security. This paper presents a fuzzy-genetic approach to detecting network intrusion. To implement and measu...

One-versus-all (OVA) classification is one of the multiclass classification problems as well as it is a binary classifier. On the basis of this, we propose a network intrusion detecting system for the security of computers and networks. In this paper, we present a new learning algorithm for detection of a network intrusion using one versus all decision tree algorithm, that differentiates attack...

Classification is a classic data mining technique based on machine learning. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. Naïve Bayes is a commonly used classification supervised learning method to predict class probability of belonging. This paper proposes a new method of Naïve Bayes Algorithm in which we tried to find effective...