International Journal of Informatics and Communication Technology (IJ-ICT)
Not a member yet
494 research outputs found
Sort by
Cyber-physical resilience system for anomaly detection in industrial environments
Get PDFThis work explores the topic of cybersecurity in the context of electric vehicles (EVs). It ensures the resilience of cyber-physical systems against anomalies, which is paramount for maintaining operational efficiency and safety. This paper presents a cyber-physical resilience system (CPRS) customized for anomaly detection. Maintaining operational efficiency and safety in today’s networked industrial contexts requires that cyber-physical systems be resilient to abnormalities. With an emphasis on EVs, this research introduces a unique CPRS designed for anomaly detection in industrial settings. By utilizing the combination of digital and physical elements, the CPRS uses sophisticated monitoring and reaction systems to identify and address irregularities instantly. The process includes creating algorithms for anomaly detection and putting in place a framework that is responsive enough to change with the dangers that it faces. The efficiency of the CPRS in detecting unusual behaviors in EVs is demonstrated by experimental findings, which also improve the overall resilience of the system. Moreover, the research’s ramifications go beyond EVs to include a variety of industrial settings, providing valuable information for the development and execution of resilient cyber-physical systems. This paper highlights the significance of proactive resilience measures in protecting critical infrastructure and advances anomaly detection approaches
Advanced optimization load frequency control for multi - islanded micro grid system with tie-line loading by using PSO
Get PDFThis manuscript presents the design of a microgrid featuring solar and wind as uncontrollable energy sources, alongside controllable sources like batteries and a diesel generator, aiming to address power supply variations resulting from load fluctuations. Controllers are imperative to mitigate these challenges, and the manuscript emphasizes the need for precise tuning of gain values for optimal electrical energy utilization. In lieu of the trial-and-error approach, particle swarm optimization (PSO) is employed for enhanced steady-state response in the Microgrid. The study also introduces the application of proportional-integral (PI), proportional-integral-derivative (PID), and PID with feed forward (PIDF) controllers to effectively address and resolve identified issues ensuring improved system performance and consistent power supply stability in the microgrid system
Pilot study on deploying a wireless sensor-based virtual-key access and lock system for home and industrial frontiers
Get PDFThe rise in data processing activities vis-à-vis the consequent rise in adoption and adaptation of information and communication tech related approaches to resolve societal challenges has become both critical and imperative. Virtualization have become the order of the day to bridge various lapses of human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. This study advances a virtual wireless sensor-based key-card access system with cost-effective solution to manage access to restricted areas within a facility. We seek to integrate virtual key card access, web-access control, solenoid lock integration, and ESP32- controller to create a dependable internet of things (IoT)-enabled access control system. Results show system benefit includes improved security, improved convenience, privacy, efficiency with real-time control capabilities that will allows building administrators to track and manage access to the facility remotely
Managing cyber resilience literacy for consumers
Get PDFIt seems inevitable that digitalization will have a profound and irreversible impact on our lives, and it seems reasonable to suppose that our world will never be the same again. Objectives of this study is to gain insight into consumers’ understanding of cyber security threats and their willingness to enhance their cyber resilience. To achieve this, a survey was conducted using AI tools such as Open ChatGPT, Copilot and PI. The survey was distributed selectively among consumers via Google Form. The results of the survey conducted during the study indicated that the majority of respondents (72%) expressed interest in attending online interactive seminars to gain more knowledge about managing cybersecurity threats. However, respondents with the lowest cyber resilience knowledge did not express the same level of interest. With technology becoming an increasingly important aspect in our everyday lives, it is becoming ever clearer that cybersecurity posture relies on the behavior consumers and organizations. Based on the rule that ‘never trust, always verify’ we designed ‘cybersecurity zero-trust framework model’ for consumers that allows them to protect themselves against cybersecurity threats. In an ever-shifting landscape of cybersecurity, it is important to recognize the value of continuous education as a necessity, not just an option
Enhancing credit card security using RSA encryption and tokenization: a multi-module approach
Get PDFThe security of credit card information remains a critical challenge, with existing methods often falling short in safeguarding data integrity, confidentiality, and privacy. Traditional approaches frequently transmit sensitive information in unencrypted formats, exposing it to significant risks of unauthorized access and breaches. This study introduces a robust security framework that leverages Rivest-Shamir-Adleman (RSA) encryption and tokenization to protect credit card information during transactions. The proposed solution is structured into three key modules: merchant, tokenization, and token vault. The merchant module works in tandem with the tokenization module to generate transaction validation tokens and securely transmit credit card data. The token vault, maintained on a secure cloud storage platform, acts as a restricted-access database, ensuring that sensitive information is encrypted and inaccessible to unauthorized entities. Through this multi-layered approach, the study demonstrates a significant enhancement in the security of credit card transactions, effectively mitigating the risks of data breaches and unauthorized disclosures. The findings indicate that the proposed method not only addresses existing security vulnerabilities but also offers a scalable and efficient solution for protecting financial transactions
Srvycite: a hybrid scientific article recommendation system
Get PDFA recommendation system is becoming part of every work done today to reduce the effort of work done by the users in searching for items in need by recommending new items that may be useful. This theme has also been used in research article recommendation systems for recommending articles of interest to researchers from a bulk of digital research documents spread across different databases on the internet. To ease the task of this article recommendation process, we have proposed a novel approach, Srvycite, by utilizing the survey article citation network along with the original research article network. The purpose of utilizing the survey article citation network is to detect the most influential articles that are considered to be important by other researchers in the same field. The Srvycite approach utilizes the text and meta features of articles to recommend papers. To preprocess the text features utilized, we have employed Word2Vec and bidirectional encoder representations from transformers (BERT) for vectorization. Then citation graph and survey citation graphs are generated to find the most influential nodes. The weighted text similarity score is finally computed by combining the cited by values and the text similarity score from the citation and survey citation graph to list articles as recommendations for the user. This system is proven to increase the accuracy of the article recommendation by 3.8 and 2.1 in the case of the precision and recall measures for performance evaluation
Memory management of firewall filtering rules using modified tree rule approach
Get PDFFirewalls are essential for safety and are used for protecting a great deal of private networks. A firewall’s goal is to examine every incoming and outgoing data before granting access. A notable kind of conventional firewall is the rule-based firewall. However, when it comes to job performance, traditional listed-rule firewalls are limited, and they become useless when utilized with some networks that have extremely big firewall rule sets. This study proposes a model firewall architecture called “TreeRule Firewall,” which has benefits and functions effectively in large-scale networks like “cloud.” In order to improve cloud network security, this study suggests a modified tree rule firewall (MTRF cloud) that eliminates rule discrepancies. For the matching firewall policy, this work creates a tree rule firewall. There are no duplicate rules created by the proposed improved tree rule firewall. Also, memory utilization of different size rules is compared
Ensemble approach to rumor detection with BERT, GPT, and POS features
Get PDFAs vast amounts of rumor content are transmitted on social media, it is very challenging to detect them. This study explores an ensemble approach to rumor detection in social media messages, leveraging the strengths of advanced natural language processing (NLP) models. Specifically, we implemented three distinct models: (i) generative pre-trained transformer (GPT) combined with a bidirectional long short-term memory (BiLSTM) network; (ii) a model integrating part-of-speech (POS) tagging with bidirectional encoder representations from transformers (BERT) and BiLSTM, and (iii) a model that merges POS tagging with GPT and BiLSTM. We included additional features from the dataset in all these models. Each model captures different linguistic, syntactical, and contextual features within the text, contributing uniquely to the classification task. To enhance the robustness and accuracy of our predictions, we employed an ensemble method using hard voting. This technique aggregates the predictions from each model, determining the final classification based on the majority vote. Our experimental results demonstrate that the ensemble approach significantly outperforms individual models, achieving superior accuracy in identifying rumors. To determine the performance of our model, we used PHEME and Weibo datasets available publicly. We found our model gave 97.6% and 98.4% accuracy, respectively, on the datasets and has outperformed the state-of-the-art models
Human detection in CCTV screenshot using fine-tuning VGG-19
Get PDFClosed-circuit television (CCTV) systems have generated a vast amount of visual data crucial for security and surveillance purposes. Effectively categorizing security level types is vital for maintaining asset security effectively. This study proposes a practical approach for classifying CCTV screenshot images using visual geometry group (VGG-19) transfer learning, a convolutional neural network (CNN) classification model that works really well in image classification. The task in classification compromise of categorizing screenshots into two classes: “humans present” and “no humans present.” Fine-tuning VGG-19 model attained 98% training accuracy, 98% validation accuracy, and 85% test accuracy for this classification. To evaluate its performance, we compared fine-tuning VGG-19 model with another method. The VGG-19-based fine-tuning model demonstrates effectiveness in handling image screenshots, presenting a valuable tool for CCTV image classification and contributing to the enhancement of asset security strategies
Bridging generations: a scoping review of teaching technology to the elderly using intergenerational strategies
Get PDFThe proportion of the global population aged 60 and above is projected to nearly double by 2050, emphasizing the urgent need for societies to adapt to the challenges posed by an aging population. As the elderly increasingly face difficulties in navigating digital technologies, which are essential for daily tasks and accessing services, the digital divide often leads to digital exclusion. This scoping review investigates intergenerational strategies used to teach technology to older adults. Seventeen studies from 11 countries were analyzed, highlighting six key intergenerational learning strategies: reverse mentoring, virtual learning, collaborative learning, family intergenerational activities, game play learning, and storytelling. These strategies offer diverse methods for enhancing digital literacy and social engagement, with reverse mentoring showing promise in fostering digital competence, and virtual learning promoting inclusivity across generations. However, barriers such as technological access, ongoing support, and cultural differences complicate implementation. This review underscores the importance of adapting instructional approaches to the needs of the elderly while leveraging intergenerational interactions to bridge the digital literacy gap. It calls for sustained efforts to address user needs, provide technical support, and ensure inclusivity, especially for isolated individuals, to maximize the effectiveness and sustainability of these strategies