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Malicious URL detection is an emerging research area due to the continuous modernization of various systems, for instance, Edge Computing. This article presents 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 a 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 et al., and Cuckoo Filter using various use cases. The use cases unearth the …