Today, personal data is becoming a new economic asset. Personal data which generated from our smartphone can be used for many purposes such as identification, recommendation system, and etc. The purposes of our research are to discover human behavior based on their smartphone life log data and to build behavior model which can be used for human identification. In this research, we have collected user personal data from 37 students for 2 months which consist of 19 kinds of data sensors. There is still no ideal platform that can collects user personal data continuously and without data loss. The data which collected from user’s smartphone have various situations such as the data came from multiple sensors and multiple source information which sometimes one or more data does not available. We have developed a new approach to building human behavior model which can deal with those situations. Furthermore, we evaluate our approach and present the details in this paper.

A Software Defined Network (SDN) is a new paradigm in network management that separates control plane and data plane. A control plane has an important role in managing the whole networks. Since SDN introduces control plane as the manager of the network, it also introduces the single point of failure. When SDN controller is unreachable by the network devices, the whole networks will collapse. One of the attack methods that can make SDN controller unreachable is DDoS attack. This paper reports our initial step of our research to develop the method for DDoS attack detection and mitigation for SDN controller. The method considers the time duration of DDoS attack detection and attacks time pattern of DDoS attack to prevent the future attack. In this paper, we present the potential vulnerabilities in SDN controller that can be exploited for DDoS attack and discuss the methods to detect and mitigate DDoS attack.

Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.

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.