Network Intrusion Detection Using Stack-Ensemble ANN

Network and security is connected with each other. At present days thinking about communication without the network is impossible. Since the network is a public domain and anyone can use it, some corrupted people and hackers will try to gain profit by intruding others' sensitive information. The network intrusion can be done by some hackers or the network itself. For that reason ensuring security is more challenging than ever before. The proposed model detects the intrusion types currently in the network by using deep learning ANN and stack ensemble techniques. There is no space for compromise in security so it is strongly recommended to use an intrusion detection system that is more accurate and efficient. The reason for using a stacked ensemble is that even though a single deep learning model is strong enough to detect the intrusion, yet by using the stacked ensemble combines multiple deep learning models together to get a more efficient stronger intrusion detection mechanism.