In Industry 4.0, interconnected systems and real-time communication are vital for seamless operations but expose industrial networks to sophisticated cyber threats. Traditional intrusion detection systems often fail t...
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In Industry 4.0, interconnected systems and real-time communication are vital for seamless operations but expose industrial networks to sophisticated cyber threats. Traditional intrusion detection systems often fail to address modern, evolving attacks. This paper presents a novel cyber threat detection approach using a Bidirectional Long Short-Term Memory (BiLSTM) model integrated with GloVe word embeddings and a self-attention mechanism. GloVe captures global co-occurrence relationships in network events, enhancing contextual representation and detection accuracy. To handle class imbalance, random oversampling balances attack category distributions, followed by Principal Component Analysis (PCA) for feature reduction. The model's parameters are fine-tuned using the single candidate optimization algorithm (SCOA) and Greylag Goose optimizationalgorithm (GLGOA), improving computational efficiency and detection performance. Evaluation on the CIC-IDS-2018 dataset demonstrates superior accuracy, precision, recall, and F1-score compared to state-of-the-art methods. The model effectively detects intrusions and prioritizes high-risk threats, strengthening cybersecurity in Industry 4.0 environments. This adaptable framework can be enhanced to address more complex attack patterns, ensuring robust protection for critical infrastructures.
In recent times, online shopping has become commonly used method for consumers to make purchases and engage in consumption with the rapid advancement of Internet technology. Enhancing user satisfaction is achievable t...
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In recent times, online shopping has become commonly used method for consumers to make purchases and engage in consumption with the rapid advancement of Internet technology. Enhancing user satisfaction is achievable through sentiment analysis (SA) of the enormous user evaluations found on e-commerce platforms. However, accurately predicting the sentiment orientations of these user reviews remains a challenge due to varying sequence lengths, text arrangements, and intricate logic. Nowadays, sentiment analysis is widely employed to assess customer feedback, which holds great significance in determining a product's success. In the past, people relied on word-of-mouth reviews to judge a product's quality. This practice of sentiment analysis is extensively applied in social media. Natural language processing (NLP) plays a crucial role in deciphering sentiment, also referred to as opinion mining or emotion AI, as it encompasses the collective perception of customers. In this manuscript, a Hamiltonian Deep Neural Networks-based Sentiment Analysis on Product Recommendation System (HDNN-SCOA-SA-PR) is proposed. First, the data are gathered from Amazon Product Reviews dataset. Then the data are pre-processed utilizing adaptive self-guided filtering for space tokenization, Gensim lemmatization, and Snowball stemming. By using Structured Optimal Graph-Based Sparse Feature Extraction, the features are extracted. Extracted features are selected using single candidate optimization algorithm. Finally, the classification process is done using Hamiltonian deep neural network and classified sentiment analysis as positive, negative, neutral. The proposed HDNN-SCOA-SA-PR method is activated in Python, and the efficiency of the proposed method is analyzed with different metrics, such as accuracy, sensitivity, RoC, precision, error rate, F1-score,computation time. ROC is evaluated and compared to the existing methods, such as sentiment analysis based upon machine learning of online produc
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