# DeepBF: Malicious URL detection using Learned Bloom Filter and Evolutionary Deep Learning

arXiv: https://arxiv.org/abs/2103.12544 · HTML: https://mlsanity.com/paper/2103.12544/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [A Model for Learned Bloom Filters, and Optimizing by Sandwiching](https://mlsanity.com/paper/1901.00902/) (2019)
- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier](https://mlsanity.com/paper/1910.09131/) (2019)
- [Optimizing Bloom Filter: Challenges, Solutions, and Comparisons](https://mlsanity.com/paper/1804.04777/) (2018)
- [Secure Payment System Utilizing MANET for Disaster Areas](https://mlsanity.com/paper/2002.01081/) (2020)

## Built on by (most cited first, 4 of 4)

- [An Investigation on Learning, Polluting, and Unlearning the Spam Emails for Lifelong Learning](https://mlsanity.com/paper/2111.14609/) (2021)
- [A Review of Data-driven Approaches for Malicious Website Detection](https://mlsanity.com/paper/2305.09084/) (2023)
- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)
- [A Hybrid Deep Learning and Anomaly Detection Framework for Real-Time Malicious URL Classification](https://mlsanity.com/paper/2512.03462/) (2025)

## Abstract (arXiv)

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 textitet 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.