In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and...

The paper presents a serial combination of two genetic algorithm-based intrusion detection systems. Feature extraction techniques are deployed in order to reduce the amount of data that the system needs to process. The designed system is simple enough not to introduce significant computational overhead, but at the same time is accurate, adaptive and fast. There is a large number of existing sol...

In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datas...

In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism. The K-means algorithm is first applied to the training dataset to reduce the a...

Anomaly based intrusion detection is a critical research area, since it does not require any prior knowledge of attack signatures in order to detect them. Typically, intrusion detection systems (IDSs) consider a fix learning model, and generally do not contemplate any memory constraint. These assumptions may not be valid in real world scenarios, where the learning model may evolve and the avail...

Network intrusion detection is an important research direction in the field of network security. The traditional algorithm based on feature extraction and separation, which has problems low accuracy high false alarm rate. In order to improve detection, this paper proposes model deep asymmetric convolutional encoder Random Forest(RF). First, use DACAE extract features from preprocessed data, the...

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attenti...

With the vulgarization of Internet, the easy access to its resources and the rapid growth in the number of computers and networks, the security of information systems has become a crucial topic of research and development especially in the field of intrusion detection. Techniques such as machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a mal...