  Malicious URL detection is an emerging research area due to continuous
modernization of various systems, for instance, Edge Computing. In this
article, we present a novel malicious URL detection technique, called deepBF
(deep learning and Bloom Filter). deepBF is presented in two-fold. Firstly, we
propose a learned Bloom Filter using 2-dimensional Bloom Filter. We
experimentally decide the best non-cryptography string hash function. Then, we
derive a modified non-cryptography string hash function from the selected hash
function for deepBF by introducing biases in the hashing method and compared
among the string hash functions. The modified string hash function is compared
to other variants of diverse non-cryptography string hash functions. It is also
compared with various filters, particularly, counting Bloom Filter, Kirsch
\textit{et al.}, and Cuckoo Filter using various use cases. The use cases
unearth weakness and strength of the filters. Secondly, we propose a malicious
URL detection mechanism using deepBF. We apply the evolutionary convolutional
neural network to identify the malicious URLs. The evolutionary convolutional
neural network is trained and tested with malicious URL datasets. The output is
tested in deepBF for accuracy. We have achieved many conclusions from our
experimental evaluation and results and are able to reach various conclusive
decisions which are presented in the article.
