# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
## 1 Introduction  
Recently, the Internet became an integral part of everyday life. The existing web-based data processing systems are subject to numerous attacks, resulting in substantial losses of multiple types of injury [1]. The importance of security of information, therefore, evolves rapidly. Information security is most importantly designed to develop defense systems that are locked against unauthorized use, alteration, disruption, or destruction, access, and disclosure [2]. Cyber management minimizes the threats regarding secrecy, honesty, and quality relevant to the three main protection goals. In the past, different systems were developed to detect and block attacks from the Internet. Mortgage detection systems (MDS) are essential systems because they can effectively withstand external attacks [3]. Besides, MDSs deliver a security wall that overcomes the assault on the Internet on computer systems. If a traditional firewall is not good, MDS has been used to detect dissimilar types of occurrences on the networking and the workstation system [4]. The identification of intrusion is expected to vary from the legal user in the actions of intruders. MDS is generally divided into two types such as misuse detection and anomaly based on its methods of exposure [5]. The analysis of anomaly intrusions decides how defects from the standard patterns of use can be identified as intrusions. Issue detection systems, of effectively distinguish destructions of permissions [6]. Smart agents and classification techniques can be used to build intrusion detection systems. Most MDS s operate in two phases, namely intrusion detection and pre-processing step on developing an intrusion prevention system, the intrusions identified the MDS has been effectively prohibited. The main feature of this paper is a smart technique survey suggested in MDSdevelopment [7]. It discusses a new MDS industrialized using two future algorithms such as smart rule-based collection algorithms and smart rules-based improved multiclass vector provision machines. Smart MDS are the smart computer programs in the network, which evaluate the flexibly function and environment to improve the precision of the identification [8]. These programmers, both through environmental learning and through inference rules, are used to calculate actions to be carried out in the environment. Smart MDSs are in a position to take decisions and to test restrictions. In most smart programs, laws are either shot or decision-making agents are included. Besides, static agents and a number of mobiles have been used to realize a particular objective. Smart intrusion detection systems were developed through the provision of intelligent pre-processing and effective classification techniques [9]. In comparison with other approaches, such MDSs provided a better detection rate. Classification is used to learn from many labeled data instances called training a model called a classifier and classified a test example the classes utilizing an educated ideal known as analysis. Classification-based on techniques for variance finding work similarly in two phases. The course segment will teach a classifier with the characterized training statistics available [10]. The test segment uses the classificatory to classify a test instance as normal or abnormal. Classification variance detection techniques operate either under one or more classes. One-class-specific variance recognition performances are based on the statement that only one class label exists for each training instance. Both methods use a one-gender classification algorithm to know a biased boundary across standard cases. Any test instance not within the studied limits is considered to be \"anomalous\" in a multi-class anomaly detection process. The training data suggest that there are classified instances in different ordinary classes [11]. The classificatory is directed to vary between every normal course and the rest of classes such methods of anomaly detection.