Identification of Malicious Activity for Network Packet using Deep Learning

  • Monika D.Rokade, Dr. Yogesh Kumar Sharma


Data and application security is most essential in today environment due to the advancement as well as exchange of information and communication techniques that generating new value added services by different network threats. As a result, they developed diverse online services. However, cyber security threats are also growing as the contact points to the Internet are increasing. A significant security issue today is the intrusion detection system (IDS). A Network Intrusion Detection System (NIDS) helps system administrators detect violations of network security within their operations. However, many problems arise when a robust and efficient NIDS is developed for unexpected and unforeseeable attacks. In this work, a deep learning based approach is to implement such an effective and flexible NIDS. Through the performance test, it is confirmed that the deep neural network is effective for NIDS. In this work, A deep learning based approach to implement such an effective and flexible Intrusion Detection System on cloud environment. System uses Recurrent Neural Network (RNN) which is supervised learning algorithm to detect known and unknown attacks respectively. Initially, the The data is pre-processed using Data Balancing and standardization for input to the RNN model. The RNN algorithm was applied to the refined data to create a learning model by preprocessing, and the whole KDD Cup 99 was used to check that. When everything's said and done, the false alarm rate, accuracy and detection rate were calculated to ascertain the detection efficiency of the RNN model. Additionally, We are evaluating and comparing different deep learning algorithms, namely. RNN, CNN, DNN and PNN algorithm on cloud environment to detect intrusion in the network.

How to Cite
Monika D.Rokade, Dr. Yogesh Kumar Sharma. (2020). Identification of Malicious Activity for Network Packet using Deep Learning. International Journal of Advanced Science and Technology, 29(9s), 2324 - 2331. Retrieved from