Bhogeswar Borah

Learn More
The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the(More)
Anomaly based network intrusion detection (ANID) is an important problem that has been researched within diverse research areas and various application domains. Several anomaly based network intrusion detection systems (ANIDS) can be found in the literature. Most ANIDSs employ supervised algorithms, whose performances highly depend on attack-free training(More)
Most existing network intrusion detection systems use signature-based methods which depend on labeled training data. This training data is usually expensive to produce due to cost of laboratory set up, experienced or knowledge person and non availability of ready software tool. Above all, these methods have difficulty in detecting new or unknown types of(More)
With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. Data mining methods for misuse and anomaly-based(More)
— Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN(More)
OBJECTIVE Seeking jobs in the area of academics and research in the field of Computer Science & Engineering where I can apply my knowledge and skills to my full potential, to fulfill the goals of the organization I am associated with as well as to satisfy my ambitions in the field of research. An effective neural network and fuzzy time series based(More)
Fuzzy time series forecasting method has been applied in several domains, such as stock market price, temperature, sales, crop production and academic enrollments. In this paper, we introduce a model to deal with forecasting problems of two factors. The proposed model is designed using fuzzy time series and artificial neural network. In a fuzzy time series(More)
In this paper we present a clustering based classification method and apply it in network anomaly detection. A set of labeled training data consisting of normal and attack instances are divided into clusters which are represented by their representative profiles consisting of attribute-value pairs for selected subset of attributes. Each category of attack(More)