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Botnet Detection has been an active research area over the last few decades. Researchers have been working hard to develop effective techniques to detect Botnets. From reviewing existing approaches, it can be noticed that many of them target specific Botnets and many others try to identify any Botnet activity by analysing network traffic. They achieve this by concatenating existing Botnet datasets to obtain larger datasets, building predictive models, and then employing these models to predict whether network traffic is safe or harmful. The problem with the first approaches is that data is usually scarce and costly to obtain. By using small amounts of data, the quality of predictive models will be questionable. On the other hand, the problem with the second approaches is that it is not always correct to concatenate datasets from different Botnets. Datasets can have different distributions which means they can downgrade the predictive performance of machine learning models. This paper introduces a transfer learning approach that utilises datasets from different but related domains. The idea is instead of concatenating datasets, transfer learning can be used to carefully decide what data to use. The hypothesis is that predictive performance can be improved by using transfer learning across datasets containing network traffic from different Botnets. The approach is compared to a classical open source transfer learning algorithm. Experiments show that the proposed method outperforms this approach and produces higher accuracy. Not only this, but it is also faster which gives it another advantage.
Botnet Detection has been an active research area over the last few decades. Researchers have been working hard to develop effective techniques to detect Botnets. From reviewing existing approaches, it can be noticed that many of them target specific Botnets and many others try to identify any Botnet activity by analysing network traffic. They achieve this by concatenating existing Botnet datasets to obtain larger datasets, building predictive models, and then employing these models to predict whether network traffic is safe or harmful. The problem with the first approaches is that data is usually scarce and costly to obtain. By using small amounts of data, the quality of predictive models will be questionable. On the other hand, the problem with the second approaches is that it is not always correct to concatenate datasets from different Botnets. Datasets can have different distributions which means they can downgrade the predictive performance of machine learning models. This paper introduces a transfer learning approach that utilises datasets from different but related domains. The idea is instead of concatenating datasets, transfer learning can be used to carefully decide what data to use. The hypothesis is that predictive performance can be improved by using transfer learning across datasets containing network traffic from different Botnets. The approach is compared to a classical open source transfer learning algorithm. Experiments show that the proposed method outperforms this approach and produces higher accuracy. Not only this, but it is also faster which gives it another advantage.
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