Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over $67\%$ in Malay language, $27\%$ in English, $2\%$ in Chinese, and $4\%$ in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset.

Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply’s quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the legitimate users who have to pay excessive electricity bills. That is why the method to detect electricity theft is indispensable. Recently, machine learning algorithms have been used to develop a model for detecting electricity theft. However, most algorithms have problems due to imbalanced data, overfitting issues, and lack of data. Therefore, this paper proposes a solution that implements the oversampling technique to address the problems and increase the developed model’s accuracy. It is used to perform oversampling on the imbalanced dataset. Our proposed method consists of a pre-processing step to remove empty values and extract several parameters. After that, the oversampling technique is performed on the result of the pre-processing step. The logistic regression model combined with the oversampling techniques shows the best performance results on the developed model of electricity theft detection based on the state electricity company customers. The experiment shows that the proposed method, logistic regression combined with the synthetic minority oversampling technique, shows superior performance in terms of the accuracy of the training data and data testing, precision, recall, and F1-scores of 98.97%, 98.7%, 95%, 99%, and 97%, respectively. Moreover, the experiment also shows that the proposed solution outperforms existing methods.

As the internet and complex network infrastructures continue to expand, so does the threat of sophisticated cyberattacks, compelling organizations to adopt advanced proactive defenses. A cornerstone of these defensive strategies is the honeypot. However, existing dynamic solutions often rely on reactive deployment or centroid-based clustering (e.g., K-Means), which mathematically yields invalid, unrealistic host profiles. Because intelligent threat detection increasingly relies on high-fidelity honeypot data to analyze adversary tactics, deploying easily fingerprinted decoys fundamentally undermines downstream AI-driven defense mechanisms. To overcome this limitation, we propose DYNAMIT, an intelligent honeynet deployment system that resolves the centroid validity problem by utilizing the unsupervised K-Medoids algorithm. By combining K-Medoids with a novel hybrid Manhattan-Jaccard distance metric, DYNAMIT selects valid, existing hosts as templates based on categorical hardware and binary software similarities. The system then leverages containerization and network virtualization to simulate multiple realistic, internet-facing honeypot profiles from a single physical host, ensuring the decoys remain indistinguishable from legitimate targets. Our evaluation demonstrates that DYNAMIT accurately captures the intended number of clusters with a low relative error ($18.75\%$ for 40 hosts and $6.625\%$ for 1000 hosts) while maintaining minimal resource overhead, establishing it as a highly scalable and robust data-generation prerequisite for modern intelligent network security.