International Journal of Computer science engineering Techniques-– Volume 2 Issue 3, Mar - Apr 2017 ISSN: 2455-135X http://www.ijcsejournal.org Page 17 Survey on Evaluating The Sampling For Intrusion Detection System Using Machine Learning Technique Soumya Tiwari (M-tech Research Scholar) 1 , Nitesh Gupta (Assistant Professor) 2 , Dr. Umesh K Lilhore(Dean PG) 3 1,2,3 (Department Of Computer Science & Engineering, NRI Institute Of Information Science and Technology,Bhopal) I. INTRODUCTION We secure information either in private or government sector as it has become an essential requirement. System vulnerabilities and valuable information magnetize most attackers’ attention. Traditional approaches that are used for intrusion detection, such as firewalls or encryption are not sufficient to prevent system from all attack types. Subsequently the number of attacks through network and other medium has been increased radically in recent years. Thus efficient intrusion detection is required as a security layer against these malicious or suspicious and abnormal activities. Thus, intrusion detection system (IDS) has been introduced as a security technique to detect various attacks. IDS can be identified by two techniques, namely misuse detection and anomaly detection. Misuse detection techniques can detect known attacks by examining attack patterns, much like virus detection by an antivirus application. However they cannot detect unknown attacks and need to update their attack pattern signature whenever there is new attacks .On the other hand, anomaly detection identifies any unusual activity pattern which deviates from the normal usage as intrusion. Although anomaly detection has the capability to detect unknown attacks which cannot be addressed by misuse detection, it suffers from high false alarm rate. In recent years, an interest was given into machine learning techniques to overcome the constraint of traditional intrusion techniques by increasing accuracy and detection rates. New machine learning based IDS with sampling is used in our detection approach. The advantage of IDS (Intrusion Detection system) can greatly reduce the time for system administrators/users to analyse large data and protect the system from illicit attacks. Improve the performance of IDS and the low false alarm rate. II. MACHINE LEARNING TECHNIQUE Abstract: Rapid development and popularization of information system, network security is a main important issue. Intrusion Detection System (IDS) as the main security defensive technique and is widely used against intrusion. Data Mining and Machine Learning techniques proved useful attention in security research area. Recently, many machine learning methods have also been applied by researchers, to obtain highly detection rate. Problem of all those methods is that how to classify attack or intrusion effectively. Looking at such inadequacies, the machine learning technique is used for obtain the high detection rate. Also, internet usage is increasing progressively, so that large amount of data and its security has become an important issue. Sampling technique can be an efficient solution for large dataset. Sampling technique can be applied for obtaining the sampled data. Sampled dataset represent the whole dataset with proper class balancing. Class imbalanced can be balanced by sampling techniques. The purpose of this paper is to propose classification framework based on different model. This model also based on machine learning and sampling technique to improve the classification performance. Keywords - Sampling, Classification, Machine learning technique, IDS. RESEARCH ARTICLE OPEN ACCESS