Prevention of IP spoofing attack in cyber using artificial Bee colony and artificial neural network

IP spoofing is a method in which the attacker develops and IP with bogus or fake source IP address the header. IP can be spoofed by means of fake information for hiding the sender's individuality or for helping with developing attacks like DDoS. In this paper, IP spoofing attack in cyber is prevented by using an optimization algorithm namely Artificial Bee Colony (ABC) with the blend of classifiers such as Artificial neural network (ANN). The properties of nodes are optimized by using the fitness function of the ABC algorithm and then the neural network model will further upskill, using the optimized properties and later store it in the database. For depicting the performance of the proposed architecture, different parameters named as Packet delivery ratio, Efficiency in terms of Throughput and Energy consumption are measured. The simulation is performed in MATLAB simulation tool.

[1]  Jian Zhang,et al.  A Hadoop Based Analysis and Detection Model for IP Spoofing Typed DDoS Attack , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[2]  Archana .S. Pimpalkar,et al.  Defense against DDoS Attacks Using IPAddress Spoofing , 2015 .

[3]  Sanjeev Jain,et al.  Bandwidth Spoofing and Intrusion Detection System for Multistage 5G Wireless Communication Network , 2018, IEEE Transactions on Vehicular Technology.

[4]  Oguz Findik,et al.  A directed artificial bee colony algorithm , 2015, Appl. Soft Comput..

[5]  Chen Chang,et al.  Application of Back Propagation Neural Network with Simulated Annealing Algorithm in Network Intrusion Detection Systems , 2017 .

[6]  Archana S. Pimpalkar,et al.  DDoS Attack Defense against Source IP Address Spoofing Attacks , 2015 .

[7]  Nabajyoti Medhi,et al.  A Flow Marking Based Anti-spoofing Mechanism (FMAS) Using SDN Approach , 2018 .

[8]  T. Subbulakshmi,et al.  A learning-based hybrid framework for detection and defence of DDoS attacks , 2017, Int. J. Internet Protoc. Technol..

[9]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[10]  Anja Feldmann,et al.  Detection, classification, and analysis of inter-domain traffic with spoofed source IP addresses , 2017, Internet Measurement Conference.

[11]  Divakar,et al.  DETECTING IP BASED ATTACK ON CLOUD SERVER USING PASSIVE IP TRACEBACK , 2017 .

[12]  Sang-Jin Lee,et al.  A study on the detection of DDoS attack using the IP Spoofing , 2015, Inscrypt 2015.

[13]  Arun Kumar Sangaiah,et al.  Towards a SDN-Based Integrated Architecture for Mitigating IP Spoofing Attack , 2018, IEEE Access.

[14]  Jung Woo Seo,et al.  A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems , 2016, SpringerPlus.

[15]  Neminath Hubballi,et al.  An event based technique for detecting spoofed IP packets , 2017, J. Inf. Secur. Appl..

[16]  G. Usha Devi,et al.  Detection of DDoS Attack using Optimized Hop Count Filtering Technique , 2015 .

[17]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[18]  R. Kesavamoorthy,et al.  Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system , 2018, Cluster Computing.

[19]  K. Venugopal Rao,et al.  DoS and DDoS Attacks: Defense, Detection and Traceback Mechanisms - A Survey , 2014 .

[20]  Amir Herzberg,et al.  Bandwidth Distributed Denial of Service: Attacks and Defenses , 2014, IEEE Security & Privacy.