Feature Selection of Denial-of-Service Attacks Using Entropy and Granular Computing

Recently, many researchers have paid attention toward denial of services (DoS) and its malicious handling. The Intrusion detection system is one of the most common detection techniques used to detect malicious attack which attempts to compromise the security goals. To deal with such an issue, some of the researchers have used entropy calculation recently to detect malicious attacks. However, it fails to identify the most potential feature for DoS attack which needs to be addressed on its early occurrence. Therefore, this paper focused on identifying some of the potential attributes of a DoS attack based on computed weight for each of the attributes using entropy calculation. In addition, the selection of potential attributes based on user-defined chosen granulation is also given using NSL KDD dataset.