ECTI Transactions on Computer and Information Technology (ECTI-CIT)

An Effective Prevention Approach against ARP Cache Poisoning Attacks in MikroTik-based Networks

Ekarin Suethanuwong — 2024-11-16

Nowadays, leading manufacturers of enterprise-grade networking devices offer the dynamic ARP inspection (DAI) feature in their Ethernet Switches to detect and prevent ARP cache poisoning attacks from malicious hosts. However, MikroTik Ethernet switches do not yet support this feature. Within MikroTik-based networks, three potential approaches exist to prevent ARP cache poisoning attacks, each with drawbacks. This paper proposes an innovative approach called Gateway-controlled ARP (GCA) to prevent ARP cache poisoning attacks on a router-on-a-stick (RoaS) network using MikroTik networking devices, where a single router performs inter-VLAN routing through one physical interface. With this approach, all Ethernet switches are configured to forward ARP messages from hosts directly to the router for inspection and handling. A RouterOS script based on the GCA approach was implemented and executed on the router to handle all incoming ARP requests from any host in all VLANs, ensuring all hosts receive legitimate ARP responses from the router. This approach can effectively prevent spoofed ARP packets sent by malicious attackers. This approach was tested and evaluated on an actual RoaS network, focusing on processing time, CPU Load, and response time. The evaluation results show that the approach effectively prevents ARP cache poisoning attacks.

A Study on Bilingual Deep Learning PIS Neural Network Model Based on Graph-Text Modal Fusion

Yan Xie — 2024-11-16

This research investigates a multilingual cross-modal pedestrian information search (PIS) technique based on graph-text modal fusion. Initially, we used a combination of replacement neural networks to improve the English Language-Based Pedestrian Information Search model with Graph-Text Modal Fusion (GTMFLPIS) performance. In addition, existing research lacks GTMFLPIS models for other languages. Therefore, we propose to train GTMFLPIS models for Chinese. The Chinese GTMFLPIS model was trained using our previously constructed Chinese CUHK-PEDES dataset. The Rank1 of the Chinese RN50_PMML12V2 model reached 0.5989. In addition, we found that a single model could not adapt to the limitations of multiple languages. Therefore, we propose a novel architecture to implement a single-model multilingual cross-modal GTMFLPIS model in this research. We propose RN50_DBMCV2 and ENB7_DBM-CV2, both of which have improved performance over the existing ones. We constructed a bilingual dataset using our Chinese CUHK-PEDES dataset and existing English CUHK-PEDES dataset to test our novel multilingual cross-modal GTMFLPIS model. In addition, we found that the loss function significantly impacts the model during our experiments. Therefore, we optimized the performance of the existing loss functions for cross-modal GTMFLPIS models. Our proposed CCMPM loss function improves the performance of the model by 2%. The experimental results of this research show that our proposed model has advantages in improving the accuracy of PIS.

Ensemble Transfer Learning for Image Classification

Nayan Kumar Sarkar — 2024-11-16

The deep learning (DL) techniques used for image classification might not deliver the desired level of classification accuracy as some features belonging to some class of a dataset are missed during feature extraction. The ensemble learning (EL) based model improves classification accuracy by combining the strengths of individual classifiers. As a result, those features that were missed during feature extraction by a specific DL technique will be taken care of by another DL technique in an ensemble DL approach. In this paper, averaging EL (AENet), weighted averaging EL (WAENet), and stacking EL (StackedNet) approaches are proposed, considering the DenseNet201, EcientNetB0, and ResNetRS101 as base models. The predictions of the base models are averaged to generate the AENet. The WAENet is constructed by assigning weights to each base model based on their prediction and then taking their average. Similarly, the Stacked-Net is developed by considering the DenseNet201, EcientNetB0, and ResNetRS101 as base-learners and ResNetRS101 as meta-learner. Analysed performance of the considered pre-trained base models and the developed EL models on the standard and application-specific datasets such as MiniImageNet, CIFAR10, CIFAR100, Plant Village (PV), Tomato, Covid-19 and 9IndianFood. 80% of the datasets were used to train and 20% to test the base and proposed models. The models are trained for an epoch of 30, considering a learning rate of 0.001 and adam optimizer. The stackedNet delivered better results than others.

Single Image Denoising through Downsampling and Self-Resolution Restoration Learning

Asavaron Limsuebchuea — 2024-11-23

Image denoising using supervised learning effectively removes image noise by learning from available data. However, it may lack efficiency when faced with insufficient data, such as in the case of single images or blind noise. This challenge has led to the adoption of unsupervised learning methods, which utilize the inherent properties of noise to extract and enhance image features. This research aims to leverage the benefits of the downsampling effect for noise removal, even though downsampling may impact image features. Therefore, deep learning must be used to restore image details lost during downsampling. This research proposes the Noisy Low-Resolution to Noisy Super-Resolution (NLR2NSR) framework, which leverages image downsampling to simultaneously reduce image and noise features. A super-resolution network is then used to restore the image features. Experimental results show that under conditions where noise features are less prominent than image features, the NLR2NSR can effectively remove noise and preserve image features using only noisy data for training. However, the NLR2NSR has limitations in handling high-level noise.

Secure Supply Chain Information Interchange using Distributed Trust Backbone

Potchara Pruksasri — 2024-11-30

International trade requires transparent visibility of the goods transportation. High-quality data related to containers is essential for container movement across the border speed. However, customs and port authorities face information incorrectness and inconsistency, which are significant determinants that decrease the performance of container clearance in supply chain activities. The Seamless Integrated Data Pipeline principle has been proposed to overcome the mentioned data quality shortcomings and enhance supply chain visibility. Based on the Data Pipeline idea, we proposed the Distributed Trust Backbone (DTB) as a model of secure information exchange between parties within the supply chain activity. However, the supply chain data is highly dynamic. Access control on dynamic resources is the key to enabling secure data exchange and clear visibility. We take this challenge up in this paper. We propose an access control mechanism based on the supply chain Data Pipeline concept and apply it to the DTB model. The elaboration on the concrete detail of the system is presented in this paper. The prototype has been developed and performed in the simulation tests. It reduces 58% of requesting data for supply chain activities. The results of the experiments show that our proposed method performs 100% access control to data with BigO(1) accessing the Access Control List. It can ensure that the information for decision-making in the supply chain is of high quality. The supply chain visibility is clearer and speeds up a modern information exchange system of supply chains.

Assessment Pattern Mapping in NANDA-I Nursing Diagnoses Framework by BERT Approach

Kubo Takahiro — 2024-11-30

Nurses play a crucial role in healthcare, directly influencing patient care quality. With a global nursing shortage, enhancing nursing efficiency and care quality is urgently needed. This foundational study explores the advantages of text and data processing techniques to determine NANDA-I nursing diagnoses using both subjective and objective patient data recorded by nurses. By employing text data similarity analysis and a prototype of the predictive model, our research aims to rene the nursing assessment process and facilitate the automation of nursing diagnoses. This work highlights the accuracy of BERT-based assessment pattern matching to support nursing practices and sets a platform for future research to address the nursing shortage effectively.