Adapting New Data In Intrusion Detection Systems

, , and . International Journal of Artificial Intelligence and Expert Systems (IJAE), 8 (1): 1 - 11 (February 2019)


Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.

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