{'fraction_non_alphanumeric': 0.018501387604070305, 'fraction_numerical': 0.0, 'mean_word_length': 5.182857142857143, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Nature-Inspired Techniques for Data Security in Big Data

Inspired computing is based on biomimcry of natural occurrences. It is a discipline in which problems are solved using computer models which derive their abstractions from real-world living organisms and their social behavior. It is a branch of machine learning that is very closely related to artificial intelligence. This form of computing can be effectively used for data security, feature extraction, etc. It can easily be integrated with different areas such as big data, IoT, cloud computing, edge computing, and fog computing for data security. The chapter discusses some of the most popular biologically-inspired computation algorithms which can be used to create secured framework for data security in big data like ant colony optimization, artificial bee colony, bacterial foraging optimization to name a few. Explanation of these algorithms and scope of its application are given. Furthermore, case studies are presented to help the reader understand the application of these techniques for security in big data.