Browsing by Author "Nawaratne, R"

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item: Article-Full-text
Minority resampling boosted unsupervised learning with hyperdimensional computing for threat detection at the edge of internet of things
(Institute of Electrical and Electronics Engineers, 2021) Christopher, V; Aathman, T; Mahendrakumaran, K; Nawaratne, R; De Silva, D; Nanayakkara, V; Alahakoon, D
The Internet of Things (IoT) has rapidly transformed digital environments across a multitude of domains with increased connectivity and pervasive virtualization. The distributed computing paradigm of Edge Computing has been postulated to overcome the concerns of response time, bandwidth, energy consumption, and cybersecurity. In comparison to the other concerns, limited studies have focused on cybersecurity, mainly due to the inherent complexity of threat detection at the Edge. However, the widespread adoption of IoT applications in economic, social, and political contexts is a stringent indication of the signi cant impact from cyber-attacks. This paper aims to address this challenge by presenting an effective and ef cient machine learning approach for threat detection at the Edge of IoT. The novel contributions of this approach are, a new Enhanced Geometric Synthetic Minority Oversampling Technique (EG-SMOTE) algorithm to resolve the imbalanced distribution of data streams at the IoT Edge, an extension to the Growing Self Organizing Map (GSOM) algorithm based on Hyperdimensional Computing for energy ef cient machine learning from unlabeled data streams. The proposed EG-SMOTE C GSOM approach has been tested using four open access datasets; three benchmark, KDD99 (F-ScoreD0.9360), NSL-KDD (F-ScoreD 0.9647), CICIDS2017 (F-Score D 0.9999), and one industry-focused botnet IoT traf c dataset, BoT-IoT (F-Score D 0.9445). The EG-SMOTE approach has outperformed SMOTE and G-SMOTE approaches in a vast number of experiments that are tried with different classi ers. The results of these experiments con rm the novelty, ef ciency and effectiveness of this approach for cybersecurity at the IoT Edge.
item: Article-Full-text
A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling
(Springer Netherlands, 2022) Chamishka, S; Madhavi, I; Nawaratne, R; Alahakoon, D; De Silva, D; Chilamkurti, N; Nanayakkara, V
The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.