Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU

Technological development and the growth of the internet today have a positive and revolutionary impact in various areas of human life, such as banking, health, science, and more. The presence of Open Data and Open API also facilitates the exchange of data and information between entities without the restrictions imposed by different regions and geographical areas. However, information openness not only has a positive impact but also makes data vulnerable to data theft, viruses, and various other types of cyber attacks. The large-scale data exchange that occurs across the network poses a challenge in detecting unusual activity and new cyber attacks. Therefore, the existence of an Intrusion Detection System (IDS) is urgently essential. The IDS helps system administrators detect cyber attacks and network anomalies, thus minimizing the risk of data leaks and intrusions. The research developed a new approach using time-based sequential clustered data sets in the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This IDS model was implemented using the CIC-IDS 2018 data set, which has more than 4 million data lines. The capabilities and uniqueness of the LSTM and GRU models are used to classify and determine various attacks in IDS based on sequential data sets ordered by time and clustered according to the destination ports and protocols, such as TCP and UDP. The model was evaluated using the accuracy, precision, recall, and F-1 scores matrix, and the results showed that the time-based sequential clustered models in LSTM and GRU have an accurities of up to 97.21%. This suggests that this new approach is good enough to be applied to the future IDS models

Mandibular Image Segmentation and 3d Reconstruction using U-Net Model

Penelitian ini bertujuan untuk meningkatkan presisi dan efisiensi dalam segmentasi citra mandibula dan rekonstruksi 3D menggunakan model U-Net. Segmentasi otomatis dengan U-Net menangani tantangan metode manual yang memakan waktu. Struktur Encoder-Decoder pada U-Net memungkinkan pembelajaran fitur citra medis yang kompleks dengan akurasi tinggi, menghasilkan segmentasi yang konsisten dan presisi. Hasil penelitian menunjukkan bahwa Res U-Net mencapai performa segmentasi yang unggul dengan Dice Similarity Coefficient (DSC) sebesar 95,37%, meskipun memerlukan waktu komputasi yang lebih lama. Sementara itu, U-Net standar menawarkan efisiensi komputasi yang lebih tinggi dan cocok untuk aplikasi real-time meskipun akurasinya sedikit lebih rendah. Integrasi segmentasi dengan rekonstruksi 3D meningkatkan visualisasi anatomi mandibula, memperbaiki efektivitas perencanaan bedah, serta menyediakan alat simulasi interaktif untuk perawatan personal dan pelatihan profesional. Penggunaan standar DICOM memfasilitasi aksesibilitas antar perangkat medis, mendukung interoperabilitas sistem perawatan kesehatan. Studi ini menyimpulkan bahwa Res U-Net optimal untuk kebutuhan presisi tinggi, sedangkan U-Net lebih cocok untuk aplikasi dengan pemrosesan cepat. Temuan ini diharapkan dapat memajukan teknologi segmentasi dan visualisasi medis yang andal dan efektif dalam praktik klinis

Survey on Risks Cyber Security in Edge Computing for The Internet of Things Understanding Cyber Attacks Threats and Mitigation

Dalam era pesatnya perkembangan teknologi, penggunaan IoT terus meningkat, terutama dalam konteks edge computing. Makalah survei ini secara teliti menjelajahi tantangan keamanan yang muncul dalam implementasi IoT pada edge computing. Fokus utama penelitian ini adalah potensi serangan dan ancaman siber yang dapat mempengaruhi keamanan sistem. Melalui metode survei literatur, makalah ini mengidentifikasi risiko keamanan siber yang mungkin timbul dalam lingkungan IoT di edge computing. Pendekatan metodologi penelitian digunakan untuk mengklasifikasikan serangan berdasarkan dampaknya pada infrastruktur, layanan, dan komunikasi. Keempat dimensi klasifikasi, yaitu Network Bandwidth Consumption Attacks, System Resources Consumption Attacks, Threats to Service Availability, dan Threats to Communication, memberikan dasar untuk memahami dan mengatasi risiko keamanan. Makalah ini diharapkan memberikan landasan pemahaman yang kokoh tentang keamanan pada IoT dalam edge computing, serta kontribusi untuk pengembangan strategi keamanan yang efektif. Dengan fokus pada pemahaman mendalam tentang risiko keamanan, makalah ini mendorong pengembangan solusi keamanan yang adaptif di masa depan untuk mengatasi tantangan keamanan yang berkembang seiring dengan pesatnya adopsi teknologi IoT di edge computing

Audio Feature Analysis and Selection for Deception Detection in Court Proceedings

Deception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model\u27s accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%

Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques

This study aims to enhance face detection performance in 360-degree videos by utilizing advanced image augmentation techniques with the YOLOv8 algorithm, which is effective for real-time object detection. Acknowledging the unique challenges posed by equirectangular projection, this research introduces a novel equirectangular augmentation method specifically designed for this medium. Our findings demonstrate a remarkable 1.346% improvement in detection accuracy in Equirectangular Projection (ERP) settings compared to default YOLOv8 augmentation strategies. This significant enhancement not only addresses the geometric distortions inherent in panoramic video formats but also emphasizes the critical need for tailored augmentation approaches to improve face detection in complex environments. By showcasing the effectiveness of these customized methods, this research contributes to the growing field of deep learning applications for immersive video technologies, with implications for sectors like security, virtual reality, and interactive media. Ultimately, this work highlights the potential of innovative augmentation techniques to ensure robust face detection in challenging visual contexts

Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript

The imbalance of Javanese script data in ancient manuscript recognition poses a challenge due to the limited availability of data. A potential approach to addressing this issue is the use of Generative Adversarial Networks (GAN). This study evaluates the effectiveness of synthetic data generated using Enhanced Balancing GAN (EBGAN) in mitigating data imbalance. Various evaluation scenarios are conducted, including: (i) assessing the impact of syn-thetic data as augmentation, (ii) evaluating the sufficiency of synthetic data for recognition models, (iii) analyzing minority class oversampling with different selection strategies, and (iv) evaluating model generalization through cross-validation. Quantitative analysis of the generated synthetic data, based on Fréchet Inception Distance (FID) and Structural Similarity Index (SSIM), as well as visual assessment, indicates that the quality of synthetic data closely resembles real data. Additionally, experimental results demonstrate that combining real and synthetic data improves accuracy, precision, recall, and F1-score. The oversampling strategy for synthetic data has proven effective in meeting the data sufficiency requirements for training recognition models. Meanwhile, selecting minority classes and determining threshold values based on percentage, distribution, and model performance in oversampling can serve as guidelines for enhancing script recognition performance. Compared to previous methods, the use of EBGAN has been shown to produce more diverse synthetic data with better visual quality. However, further research is still needed to optimize GAN performance in supporting script recognition

Evaluating Object Collection In Emergency Simulations Using Virtual And Augmented Reality

Virtual Reality (VR) and Augmented Reality (AR) are two technologies that have received significant attention in recent years. While both hold immense potential, they offer distinct ways for users to interact with digital content and their physical surroundings. This research aims to evaluate the interaction between users and a collection of objects in both VR and AR settings. To achieve this, a user study was conducted with 24 participant using Meta Quest 3 headset to run simulation in both environments. The study focused on tasks related to object collection and emergency management while utilizing combination of objective and subjective metrics to evaluate user interactions in both VR and AR environments. Despite the relatively close scores for both result, research shows that participants prefer AR for emergency simulations over VR. Even considering participants\u27 first-time use of the applications, AR remains more popular, supported by lower symptom rates reported in the sickness than VR. Additionally, participants tended to focus more on collecting small objects, though VR users often forgot these items, while medium-sized objects were more frequently overlooked in AR. Although VR users experienced more human errors related to collisions with real objects, the overall impact on immersion during simulations was not significant enough to favor one technology over the other. Based on this result, it can be said that while VR is better for showing immersion, it is generally better for a first-time user to engage in AR first since it will give less incidence of virtual sickness

AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT

The development of automatic plant watering systems has recently gained popularity due to the need to conserve water and ensure healthy plant growth. This study focuses on integrating fuzzy logic, sensors, and algorithms to provide an automatic watering system. Fuzzy logic is a powerful tool that allows the system to interpret sensor data and make informed decisions. The sensors measure soil moisture, humidity, temperature, and light intensity. The data collected from these sensors is analyzed using algorithms to determine the appropriate watering schedule. The system’s ability to analyze and interpret data ensures that the plants receive the necessary moisture without over-watering or under-watering. Integrating the Telegram Bot is a significant feature of the system, enabling users to monitor and control the system remotely. The Telegram Bot sends users notifications when the system is activated, or the plants require attention. The system can also be controlled remotely through the Bot, enabling users to adjust the watering schedule or turn the system on or off. This research shows that the designed features of the system function effectively and can be used on a daily household scale. The system’s automated features reduce the need for constant monitoring and manual watering, making it ideal for those who engage in gardening at home. This innovation is particularly relevant in increasing the productivity of plants. In addition, the system’s ability to be controlled remotely through the Telegram Bot is a significant advantage, making it accessible and convenient for users

OVERSAMPLING HYBRID METHOD FOR HANDLING MULTI-LABEL IMBALANCED

Data and information continue to increase along with the development of digital technology. Data availability is becoming increasingly numerous and complex. The existence of unbalanced data causes classification errors due to the dominance of majority-class data over the minority class. Not only limited to the binary class, but data imbalance is also often encountered in multi-label data, which become increasingly important in recent years due to its vast application scope. However, the problem of class imbalance has been a characteristic of many complex multi-label datasets, making it the focus of this research. Handling unbalanced multi-label data still has a lot of potential for development. One approach, Synthetic Oversampling of Multi-Label Data Based on Local Label Distribution (MLSOL) and Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-Label Data (UCLSO), has been developed. UCLSO\u27s attention only focuses on the majority class, which can lead to data imbalance and excessive overfitting. Although effective in preventing majority class domination, this approach cannot overcome the lack of variation within the minority class. By contrast, MLSOL focuses on minority classes, allowing for variations in multi-label data and significantly improving classification performance. This research aims to overcome the problem of data imbalance by combining the MLSOL and UCLSO oversampling methods. Combining these two approaches is expected to exploit the strengths and reduce the weaknesses of each, resulting in significant performance improvements. The trial results show that the hybrid oversampling method produces the highest value on biological data with an F-1 score of 88%. Meanwhile, the single oversampling methods UCLSO and MLSOL on biological data produce an F-1 score of 67% and 62%, respectively

SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK

Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30)