EMITTER International Journal of Engineering Technology

KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks

2024-01-17T15:52:47+00:00

An exponential increase in number of attacks in IoT Networks makes it essential to formulate attack-level mitigation strategies. This paper proposes design of a scalable Kernel-level Forensic layer that assists in improving real-time evidence analysis performance to assist in efficient pattern analysis of the collected data samples. It has an inbuilt Temporal Blockchain Cache (TBC), which is refreshed after analysis of every set of evidences. The model uses a multidomain feature extraction engine that combines lightweight Fourier, Wavelet, Convolutional, Gabor, and Cosine feature sets that are selected by a stochastic Bacterial Foraging Optimizer (BFO) for identification of high variance features. The selected features are processed by an ensemble learning (EL) classifier that use low complexity classifiers reducing the energy consumption during analysis by 8.3% when compared with application-level forensic models. The model also showcased 3.5% higher accuracy, 4.9% higher precision, and 4.3% higher recall of attack-event identification when compared with standard forensic techniques. Due to kernel-level integration, the model is also able to reduce the delay needed for forensic analysis on different network types by 9.5%, thus making it useful for real-time & heterogenous network scenarios.

Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation

2024-01-17T15:52:49+00:00

As artificial intelligence (AI) translation technology advances, big data, cloud computing, and emerging technologies have enhanced the progress of the data industry over the past several decades. Human-machine translation becomes a new interactive mode between humans and machines and plays an essential role in transmitting information. Nevertheless, several translation models have their drawbacks and limitations, such as error rates and inaccuracy, and they are not able to adapt to the various demands of different groups. Taking the AI-based translation model as the research object, this study conducted an analysis of attention mechanisms and relevant technical means, examined the setbacks of conventional translation models, and proposed an AI-based translation model that produced a clear and high quality translation and presented a reference to further perfect AI-based translation models. The values of the manual and automated evaluation have demonstrated that the human-machine translation model improved the mismatchings between texts and contexts and enhanced the accurate and efficient intelligent recognition and expressions. It is set to a score of 1-10 for evaluation comparison with 30 language users as participants, and the achieved 6 points or above is considered effective. The research results suggested that the language fluency score rose from 4.9667 for conventional Statistical Machine Translation to 6.6333 for the AI-based translation model. As a result, the human-machine translation model improved the efficiency, speed, precision, and accuracy of language input to a certain degree, strengthened the correlation between semantic characteristics and intelligent recognition, and pushed the advancement of intelligent recognition. It can provide accurate and high-quality translation for language users and achieve an understanding of natural language input and output and automatic processing.

Comparative Evaluation of VAEs, VAE-GANs and AAEs for Anomaly Detection in Network Intrusion Data

2024-01-17T15:52:50+00:00

With cyberattacks growing in frequency and sophistication, effective anomaly detection is critical for securing networks and systems. This study provides a comparative evaluation of deep generative models for detecting anomalies in network intrusion data. The key objective is to determine the most accurate model architecture. Variational autoencoders (VAEs), VAE-GANs, and adversarial autoencoders (AAEs) are tested on the NSL-KDD dataset containing normal traffic and different attack types. Results show that AAEs significantly outperform VAEs and VAE-GANs, achieving AUC scores up to 0.96 and F1 scores of 0.76 on novel attacks. The adversarial regularization of AAEs enables superior generalization capabilities compared to standard VAEs. VAE-GANs exhibit better accuracy than VAEs, demonstrating the benefits of adversarial training. However, VAE-GANs have higher computational requirements. The findings provide strong evidence that AAEs are the most effective deep anomaly detection technique for intrusion detection systems. This study delivers novel insights into optimizing deep learning architectures for cyber defense. The comparative evaluation methodology and results will aid researchers and practitioners in selecting appropriate models for operational network security.

The Network Slicing and Performance Analysis of 6G Networks using Machine Learning

2024-01-17T15:52:43+00:00

6G technology is designed to provide users with faster and more reliable data transfer as compared to the current 5G technology. 6G is rapidly evolving and provides a large bandwidth, even in underserved areas. This technology is extremely anticipated and is currently booming for its ability to deliver massive network capacity, low latency, and a highly improved user experience. Its scope is immense, and it’s designed to connect everyone and everything in the world. It includes new deployment models and services with extended user capacity. This study proposes a network slicing simulator that uses hardcoded base station coordinates to randomly distribute client locations to help analyse the performance of a particular base station architecture. When a client wants to locate the closest base station, it queries the simulator, which stores base station coordinates in a K-Dimensional tree. Throughout the simulation, the user follows a pattern that continues until the time limit is achieved. It gauges multiple statistics such as client connection ratio, client count per second, Client count per slice, latency, and the new location of the client. The K-D tree handover algorithm proposed here allows the user to connect to the nearest base stations after fulfilling the required criteria. This algorithm ensures the quality requirements and decides among the base stations the user connects to.

Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm

2024-01-17T15:52:48+00:00

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.

Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data

2024-01-17T15:52:52+00:00

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.

Analytical Analysis of Flexible Microfluidic Based Pressure Sensor Based on Triple-Channel Design

2024-01-18T15:54:59+00:00

In designing a flexible microfluidic-based pressure sensor, the microchannel plays an important role in maximizing the sensor's performance. Similarly, the material used for the sensor's membrane is crucial in achieving optimal performance. This study presents an analytical analysis and FEA simulation of the membrane and microchannel of the flexible pressure sensor, aimed at optimizing it design and material selection. Different types of materials, including two commonly used polymers, Polyimide (PI) and Polydimethylsiloxane (PDMS) were evaluated. Moreover, different designs of the microchannel, including single-channel, double-channel, and triple-channel, were analyzed. The applied pressure, width of the microchannel, and length of the microchannel were varied to study the normalized resistance of the microchannel and maximize the performance of the pressure sensor. The results showed that the triple-channel design produced the highest normalized resistance. To achieve maximum performance, it is found that using a membrane with a large area facing the applied pressure was optimal in terms of dimensions. In conclusion, optimizing the microchannel and membrane design and material selection is crucial in improving the overall performance of flexible microfluidic-based pressure sensors.

IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio

2024-01-18T15:55:00+00:00

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score.