481 research outputs found
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
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Cloud-based Near Real-Time Multiclass Interruption Recognition and Classification using Ensemble and Deep Learning
Due to speedy development in internet facilities, detecting intrusions in a real-time cloud environment is challenging via traditional methods. In this case, advanced machine or deep learning methods can be efficiently used in anomaly or intrusion detection. Thus, the present study focuses on designing and developing the intrusion detection scheme using an ensemble learning-based random forest method and deep convolutional neural networks in a near real-time cloud atmosphere. The proposed models were tested on CSE-CICIDS2018 datasets in Python (Anaconda 3) environment. The proposed models achieved 97.73 and 99.91 per cent accuracy using random forest and deep convolutional neural networks, respectively. The developed models can be effectively utilised in real-time cloud datasets to detect intrusions
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