Effective intrusion detection system using semi-supervised learning

Network security is a very important aspect of internet enabled systems in the present world scenario. As the internet keeps developing the number of security attacks as well as their severity has shown a significant increase. Due to intricate chain of computers the opportunities for intrusions and attacks have increased. Therefore it is need of the hour to find the best ways possible to protect our systems. Every day new kind of attacks are being faced by industries. Hence intrusion detection system are playing vital role for computer security. The most effective method used to solve problem of IDS is machine learning. Getting labeled data does not only require more time but it is also expensive. Labeled data along with unlabeled data is used in semi-supervised methods. The rising field of semi-supervised learning offers a assured way for complementary research. In this paper, an effective semi-supervised method to reduce false alarm rate and to improve detection rate for IDS is proposed.