FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning

Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-physical system over the last decade. Defending against DDoS attack is not only challenging but also strategic. Tons of new strategies and approaches have been proposed to defend against different types of DDoS attacks. The ongoing battle between the attackers and defenders is full-fledged due to its newest strategies and techniques. Machine learning (ML) has promising outcomes in different research fields including cybersecurity. In this paper, ensemble unsupervised ML approach is used to implement an intrusion detection system which has the noteworthy accuracy to detect DDoS attacks. The goal of this research is to increase the DDoS attack detection accuracy while decreasing the false positive rate. The NSL-KDD dataset and twelve feature sets from existing research are used for experimentation to compare our ensemble results with those of our individual and other existing models