Intrusion detection system is one of the most important parts of network security in competing against illegitimate network access. Intruder uses hijacking technique like host file hijack or IP spoofing, which is IP address forgery. There are different ways by which intrusion can take place, such as fake emailing and denial of service attack. In such kind of intrusion it is difficult to find the identity of the sender. Because of this, the system or the main server can crash from the overloading requests coming from an unauthorized source. Therefore, the system becomes unresponsive to the authorized requests. In the IP address spoofing, the attacker’s mimics as the legitimate user and access the network authorization. Further, it modifies the packet headers of the authorized users IP address. To mitigate this issue, in this thesis work, a new intrusion detection method has been introduced for preventing IP spoofing attack. The properties of nodes are optimized by using artificial bee colony optimization (ABC). Furthermore, the neural network model is trained using the optimized properties to predict the output in terms Packet delivery ratio (PDR), throughput and energy consumption.

• Intrusion Detection System (IDS) is one of the crucial parts in the cyber security system because they are capable to

• Identify threats and

• It minimizes the potential damage or security breaches

• Intruder uses hijacking technique like

• IP spoofing or Host file hijack, which is also called IP address forgery.

• Day by Day new technology are developing, due to this new type of attacks are also coming to breach the security.
• For the prevention of IP spoofing, various researchers has proposed their work but due to lack of proper knowledge about nodes, and consecutively new type of attacks are coming .
• It is difficult to mitigate that issue. 

• A lot of research is already being done in the field of IP spoofing detection and prevention but due to the lack of tracing and proper classifier, the existing work is not effective as per requirements

• Therefore, this research has shown the awareness regarding the problem and proposed a new system that includes optimization of property of nodes in the network for enhanced detection accuracy.

• We are using swarm intelligence technique named as Artificial Bee Colony (ABC) optimization for finding the best route and properties of node.
• Then neural network is trained by the properties of ABC.
• Thus whenever, spoofer comes into the network system, ANN (Artificial neural network) detects it by comparing their property with the properties stored.

• It is Simple, Stable and Flexible.
• It is very effective in exploring local solutions.
• It is easy to implement.
• It is very flexible in changing the adjustment.
• Handling Cost is very low.
• Easily give the solutions of populations