Securing IoT-Cloud Applications with AQ-KGMO-DMG Enhanced SVM for Intrusion Detection

In contemporary society, the Internet has evolved into an indispensable facet of daily life, serving myriad functions across various domains. Intrusion detection, as a cornerstone of information security, plays a pivotal role in fortifying networks against potential threats, emphasizing the necessity for robust and reliable methods capable of discerning and mitigating network vulnerabilities effectively. In this work, a pioneering network intrusion detection model is introduced, leveraging Adaptive Quantum-Inspired KGMO with Dynamic Molecular Grouping (AQ-KGMO-DMG) for feature selection and employing Simplified Support Vector Machines (SVM) for the classification of intrusion data. The utilization of the UNSW-NB15 dataset serves as the litmus test for evaluating the efficacy of the developed intrusion detection model. Notably, this approach enhances the accuracy in categorizing classes with minimal instances while concurrently mitigating the false alarm rate (FAR). A notable innovation in this methodology involves the transformation of raw traffic vector data into a visual representation, thereby reducing computational costs significantly. To reduce the computation cost further, the raw traffic vector data is converted into picture format. The experimental findings showed that the proposed model performed better than conventional techniques in terms of FAR, accuracy, and computation cost

Enhancing Security of Databases through Anomaly Detection in Structured Workloads

In today’s world, the protection of databases in any global organization has become paramount due to the rapid growth of data and the new generations of cyber threats. This highlights the need for more enhanced security precautions to secure these databases containing sensitive information. One of the most advanced ways of enhancing database security is using an anomaly detection system, especially for structured workloads. Structured workloads typically exhibit predictable patterns of data access and usage, making them susceptible to displaying anomalies that may indicate unauthorized access, data manipulation, or other security breaches. Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. Our study showed that the proposed model, with an accuracy of 85%, outperformed various state-of-the-art methods. Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability

A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI

Virtual Reality (VR) Method to Improve Sense of Place for Interior Design Studio Students

Virtual reality (VR) technology has emerged in response to recent developments in the 3-dimensional (3D) world. VR enables people to engage in various metaverse world experiences in a more immersive way. Immersive learning is a learning method that uses 3D digital technology to facilitate the learning process by visualization in the classroom. This research used a case study of the Interior Design II studio course taken by level-2 students of the Department of Interior Design, School of Creative Industries, Telkom University, Indonesia. The Interior Design II course requires students to design the interior of a residence with a minimum area of 100 m2. The method of paired sample test analysis was used to assess student’s preferences for pre-test and post-test statements from the VR intervention method in assessing student’s sense of place in the final design of the course. The results showed significant differences in student preferences during the pre-test (481.3% and 790.6%), which increased during the post-test (641.7% and 801%). The paired sample t-test analysis results also showed a Sig (2-tailed) number of 0.000 < 0.05, so there is a significant relationship between the pre-test and post-test intervention

Performance of Interconnected Hybrid ZigBee-Optic for Extended Wireless Sensor Networks

Wireless sensor networks (WSNs) are widely used to monitor remote areas far away from the monitoring center. For large-scale or high-capacity WSNs, when they contain many sensor nodes, a transmission system with low latency and large bandwidth is required. In order to extend the network range, the use of optical communication is one of the alternatives to provide more capacity and a longer range. This study discusses the performance of a range-extended WSN utilizing a hybrid of ZigBee and optic transmission. The performance of the proposed method was evaluated by analyzing the throughput, delay per meter, package loss, and error, which were then compared to a ZigBee-Wifi based system. The experimental results showed that the throughput of the hybrid ZigBee-Fiber Optic (ZigBee FO) system was about 12% greater than that of the ZigBee-Wifi system, and it transmitted the sensor data with a significantly lower delay, reduced by 83%, compared to the ZigBee-Wifi. The package loss and error of ZigBee-FO was 35.7% lower than that of ZigBee-Wifi. Based on these results, the ZigBee-FO WSN has the advantage of significantly improving network performance by reducing the transmission delay, therefore it is beneficial in extending the WSN range

X-Band Metasurface EM Wave Absorber using SRR and Stripline: Model, Design and Implementation

This paper presents a model, design, and implementation of a metasurface electromagnetic (EM) wave absorber for operation in the frequency range of the X-band. The model of the metasurface was constructed with a split ring resonator (SSR) and a stripline and it was designed with a single unit cell, whereby the results were approached with transmission line theory for patch impedance extraction. Implementation of a metasurface EM wave absorber was deployed on an FR4 Epoxy dielectric substrate with dimensions of 80-unit cells  80-unit cells and characterized with two horn antennas, which were connected to a signal generator as the transmitter and a spectrum analyzer as the receiver. In front of the horn antennas a device under test (DUT) was installed, i.e., a metasurface EM wave absorber and a metal plate with similar dimensions. The metal plate was expected to perform full reflection at the same distance and antenna orientation. The same condition was used as a normalization factor to optimize the absorption of the metasurface EM wave absorber. The characterization results showed that the minimum normalized absorption of the SRR and stripline at the designated measurement distances was 0.99, 0.99, 0.99, and 0.97, at frequencies of 8.85 GHz, 9.08 GHz, 9.15 GHz, and 9.10 GHz, respectively, and a  antenna orientation

LoVi App: Android Application-based Image Classification for Low Vision

In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility

Performance Evaluation of Fractal Wavelet Packet Transform on Wireless Communication Systems

The performance of the phase shift keying (PSK) modulation technique over additive white Gaussian noise (AWGN) and multipath propagation channels generally becomes worse for higher-order modes. Therefore, a new modulation technique should be provided in order to have a system that is capable of transmitting data with higher efficiency while maintaining better performance at the same time. This paper presents the development of a fractal wavelet packet transform incorporated within the M-ary PSK system, namely M-ary PSK orthogonal wavelet division multiplexing (OWDM), which is proposed to obtain high performance of modulation in terms of spectrum efficiency and bandwidth resources intended for wireless communication systems. To demonstrate performance improvement over a Rayleigh frequency selective fading channel and in the presence of AWGN noise, the proposed system was evaluated and compared to the basic modulation system and M-ary PSK employing orthogonal frequency division multiplexing (OFDM). The performance results show that M-ary PSK OWDM had better performance in comparison with M-ary PSK OFDM and the conventional system. By utilizing 16 subcarriers, QPSK OWDM achieved bit error rate performance improvement from 1.5 × 10-3 to 1 × 10-4 for Eb/N0 of 20 dB with efficient bandwidth

Prediction of On-time Student Graduation with Deep Learning Method

Universities have an important role in providing quality education to their students so they can build a foundation for their future. However, a problem that often arises is that the process experienced will be different for each individual. Therefore, it is necessary to apply on-time graduation predictions for students with academic attributes in the hope that educational institutions can better understand student conditions and maximize on-time student graduation. In this study, a deep learning method was implemented to help predict on-time graduation for students at the Faculty of Computer Science, University of Brawijaya. Based on the test results and hyperparameter tuning using Optuna, the best hyperparameter configuration for the deep learning method consisted of number of layer combinations = 4; first-layer nodes = 118; first dropout = 0.3393; second-layer nodes = 83; second dropout = 0.0349; third-layer nodes = 88; third dropout = 0.0491; fourth-layer nodes = 65; fourth dropout = 0.4169; number of epochs = 244; learning rate = 0.0710; and optimizer = SGD. Thus, an accuracy rate of 86.61% was achieved for the two classes of the test data set, i.e., on-time graduation and not on-time graduation

Investigating the Effect of m-Commerce Application’s Functional and Non-Functional Attributes on Usability and Continuance Intention

The development of mobile commerce (m-commerce) applications has indicated a shift in goals from ease-of-use to sustainable use in the future. This shift has prompted changes in the combination of attributes that constitute the usability of m-commerce applications. This study developed an m-commerce usability model that combines functional and non-functional attributes. The research data was collected using questionnaires distributed to users living in Jakarta and was processed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The results demonstrate that efficiency, satisfaction, and effectiveness can explain the usability of m-commerce applications. This study proved that three non-functional usability attributes – productivity, navigation, and memorability – have a positive effect on efficiency, while two functional attributes of usability – content and security – positively influence effectiveness. The study further proved that beside non-functional attributes, functional attributes also play an important role in increasing the user experience in the m-commerce context and thereby contribute to improving the usability of m-commerce systems and the continuance intention. The overall results of the study can be used in developing strategies for m-commerce application developers to design applications that can satisfy users and help them complete their tasks correctly and efficiently