Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem nuisance network administrators in detecting attacks false alerts. Regardless methods implemented by this system, eliminating alerts still a huge problem. To describe traffic passing through network, database security layer (NSL) knowled...

The enormous increase in the use of Internet daily life has provided an opportunity for intruder attempt to compromise security principles availability, confidentiality, and integrity. As a result, organizations are working level by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors analyzes network flow attacks detection. There lot researches pr...

With the rapid growth of internet based services and data generated on these are attracted by attackers to intrude networking information. Based characteristics intruders, many researchers attempted aim detect intrusion with help automating process. Since, large volume is transferred through network, security performance remained an issue. IDS (Intrusion Detection System) was developed prevent ...

Research in the field of IDS has been going on since long time; however, there exists a number ways to further improve efficiency IDS. This paper investigates performance Intrusion detection system using feature reduction and EBPA. The first step involves features, based combination information gain correlation. In next step, error back propagation algorithm (EBPA) is used train network then an...

Abstract Network security is subject to malicious attacks from multiple sources, and intrusion detection systems play a key role in maintaining network security. During the training of models, results generally have relatively large false rates due shortage data caused by imbalance. To address existing sample imbalance problem, this paper proposes algorithm based on enhanced random forest synth...

Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud D...

Intrusion detection systems (IDSs) are currently drawing a great amount of interest as a key part of system defence. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Recently, machine learning methodologies are playing an important role in detecting network intrusions (or attacks), which further helps...

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related formal is their stable essence, which makes them incapable of meeting requirements nodes recently inserted into network. Thus, are substituted by a Software Defined Network (SDN). latter can be construct structure for intensive applications like big data. this pap...

Abstract Intrusion detection systems get more attention to secure the computers and network systems. Researchers propose different intrusion using machine learning techniques. However, massive amount of data that contain irrelevant redundant features is still challenging The redundancy irrelevance may slow processing time decrease prediction performance. This paper proposes a Heterogeneous Ense...

Internet of Things (IoT) has transcended from its application in traditional sensing networks such as wireless and radio frequency identification to life-changing critical applications. However, IoT are still vulnerable threats, attacks, intrusions, other malicious activities. Intrusion Detection Systems (IDS) that employ unsupervised learning techniques used secure sensitive data transmitted o...