SpamHD: Memory-Efficient Text Spam Detection using Brain-Inspired Hyperdimensional Computing

Thapa, Rahul; Lamichhane, Bikal; Ma, Dongning; Jiao, Xun

doi:10.1109/isvlsi51109.2021.00026

Cited by 24 publications

(4 citation statements)

References 19 publications

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“…In work [58], the proposed method achieves one/few-shot learning on edge devices for the task of epileptic seizure detection. In addition, HDC has also shown significantly faster learning in classifying human faces [59], spam texts [60], texts [61], etc. More recently, researchers also proposed to incorporate uncertainty estimation into HDC-based regression via a customized HDC encoder that randomly drops dimensions [62].…”

Section: Brain-inspired Hdcmentioning

confidence: 99%

Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing

Lesica

Zeng

et al. 2022

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

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Section: Brain-inspired Hdcmentioning

confidence: 99%

Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing

Lesica

Zeng

et al. 2022

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

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“…Classifying YouTube comments as spam and ham using machine learning [5]- [13], cascaded ensemble machine learning model [14], Markov decision process [15], artificial neural network [16], Microsoft structured query language server data mining tools [17], contextual feature based one-class classifier approach [18], hybrid ensemble machine learning models [19], multi-stage spam account [20]. Brain-inspired hyperdimensional computing [21], genetic algorithmic multi evaluation [22], n-gram assisted [23]. This comprehensive exploration demonstrates dedication to combat spam through various sophisticated techniques encompassing machine learning paradigms, algorithmic advancements, and even bio-inspired computing, collectively working to uphold the integrity of platforms like YouTube.…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning model for YouTube spam comment detection

Sam'an,

Imaddudin

2024

IJECE

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Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.

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“…HDC is an emerging machine learning paradigm that utilize high dimensional patterns imitating brain activation functions to complete learning tasks [9]. Recently, HDC has shown comparable capability with SOTA machine learning models under various application scenarios such as bio-informatics [12], [10], natural language processing [17], [15] and robotics [14]. Similar to other machine learning models, HDC also require significant amount of computation to train and fine-tune a model, which can serve as the computationally expensive task during the blockchain mining process.…”

Section: Introductionmentioning

confidence: 99%

HDCoin: A Proof-of-Useful-Work Based Blockchain for Hyperdimensional Computing

Ma¹,

Zhang²,

Jiao³

2022

Preprint

Self Cite

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Various blockchain systems and schemes have been proposed since Bitcoin was first introduced by Nakamoto Satoshi as a distributed ledger. However, blockchains usually face criticisms, particularly on environmental concerns as their "proofof-work" based mining process usually consumes a considerable amount of energy which hardly makes any useful contributions to the real world. Therefore, the concept of "proof-of-useful-work" (PoUW) is proposed to connect blockchain with practical application domain problems so the computation power consumed in the mining process can be spent on useful activities, such as solving optimization problems or training machine learning models. This paper introduces HDCoin, a blockchain-based framework for an emerging machine learning scheme: the brain-inspired hyperdimensional computing (HDC). We formulate the model development of HDC as a problem that can be used in blockchain mining. Specifically, we define the PoUW under the HDC scenario and develop the entire mining process of HDCoin. During mining, miners are competing to obtain the highest test accuracy on a given dataset. The winner also has its model recorded in the blockchain and are available for the public as a trustworthy HDC model. In addition, we also quantitatively examine the performance of mining under different HDC configurations to illustrate the adaptive mining difficulty.

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SpamHD: Memory-Efficient Text Spam Detection using Brain-Inspired Hyperdimensional Computing

Cited by 24 publications

References 19 publications

Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing

Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing

Hybrid deep learning model for YouTube spam comment detection

HDCoin: A Proof-of-Useful-Work Based Blockchain for Hyperdimensional Computing

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