Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Abstract

The detection technique for IP packet header modifications associated with store-and-forward operation pertains to a methodology or mechanism utilized for the identification and detection of alterations made to packet headers within a network setting that utilizes a store-and-forward operation. The problem that led to employing this technique lies with the fact that previous research studies expected intrusion detection systems (IDSs) to perform everything associated with inspecting the entire network transmission session for detecting any modification. However, in the store-and-forward process, upon arrival at a network node such as a router or switch, a packet is temporarily stored prior to being transmitted to its intended destination. Throughout the duration of storage, IDS operation tasks would not be able to store that packet; however, it is possible that certain adjustments or modifications could be implemented to the packet headers that IDS does not recognize. For this reason, this current research uses a combination of a convolutional neural network and long short-term memory to predict the detection of any modifications associated with the store-and-forward process. The combination of CNN and LSTM suggests a significant improvement in the model’s performance with an increase in the number of packets within each flow: on average, 99% detection performance was achieved. This implies that when comprehending the ideal pattern, the model exhibits accurate predictions for modifications in cases where the transmission abruptly increases. This study has made a significant contribution to the identification of IP packet header modifications that are linked to the store-and-forward operation.