2022 IEEE International Conference on Computing (ICOCO)最新文献

{"title":"An End-To-End Cyber Security Maturity Model For Technology Startups","authors":"A. Selamat, Mohamed Noordin Yusuff Marican, S. H. Othman, S. Razak","doi":"10.1109/ICOCO56118.2022.10031900","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031900","url":null,"abstract":"Cybersecurity is increasingly becoming an important discussion topic in the boardroom of companies, regardless of the size or industry. Hackers nowadays are becoming increasingly smart. Instead of attacking big multi-national companies, international banks and government organisations which have built strong protection against cyber threats, the perpetrators now placed their focus on smaller and medium size businesses like technology start-ups through a variety of attacks from phishing, ransomware to the exploitation of vulnerabilities in the web or mobile applications. Therefore, it is imperative that technology start-ups have the capability in assessing their cyber security maturity to combat against cyber threats. However, for technology start-ups, it is especially imperative as cyber-attacks or data breaches could undeniably result in the loss of customers’ confidence, regulatory implications and revenue loss which could eventually result in the start-up untimely closure. Although there are available security frameworks commonly used in the industry by cyber security practitioners, these frameworks are not suitable for technology start-ups as they tend to be broad and generic, taking a long time to conduct the assessment requiring adequate manpower or even the need for a budget to hire external consultants to help in conducting the assessment. This study seeks to analyse the current cyber security frameworks and introduce an end-to-end Cyber Security Maturity Model, which can be used specifically for technology start-ups. The proposed model not only provides an end-to-end maturity assessment of the start-up’s cyber security posture but also coupled with an existing quantification model to justify the investments allocated in implementing cyber security measures for the start-up. Right-sizing the cyber security measures for the start-up in the different stages of the start-up lifecycle could allow reasonable controls to be implemented at the appropriate phase.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115398906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"fProSentiment Analysis on Mobile Phone Brands Reviews using Convolutional Neural Network (CNN)","authors":"Noor Izati Abdul Hamid, N. Kamal, H. M. Hanum, Noor Latiffah Adam, Z. Ibrahim","doi":"10.1109/ICOCO56118.2022.10031660","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031660","url":null,"abstract":"Due to the rapid growth of online e-commerce, customers can now voice and express their reviews and thoughts on online products. Therefore, companies who market their product on the e-commerce website will receive thousands of reviews and feedback from their end-users directly on this platform. As the amount of textual data grows tremendously, developing sentiment analysis that automatically analyses text data becomes increasingly vital. It is because reading every review manually can be a tedious task and time-consuming. Analyzing the sentiment for all reviews can provide the companies with an overview of how positive or negative the customers are about their products. The convolutional neural network (CNN) has recently been used for text classification tasks and has achieved impressive results. Hence, this study proposes a CNN method for sentiment analysis to classify the reviews on mobile phone brands. The customer reviews dataset is obtained from the Amazon website. This study combined the Word2Vec-CNN model to predict the sentiment of mobile phone reviews effectively. Pre-trained Word2Vec model is utilized to generate word vectors in word embedding. CNN layers are used to extract better features for sentence categorization to identify the sentiment polarity of the reviews, whether positive or negative. The obtained results give us 88% accuracy and the developed application can also function well in analyzing the sentiment of customers’ reviews.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Disruptive Technologies on Customer Experience Management In ASEAN: A Review","authors":"Vyankatesh Adke, Priti Bakhshi, Muniza Askari","doi":"10.1109/ICOCO56118.2022.10031882","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031882","url":null,"abstract":"The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around ${$}$ 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains positive.To further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer needs.Customers today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and growth.This however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued benefits.In this context, this study explores the impact of cloud, Artificial Intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience management.It does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits, and customer segmentation attributes that impact customer experience.The study contributes by providing insights to retail banks on key factors to consider while embarking on digital transformation projects to improve customer experience. While the study focuses on retail banking, its contributions could be beneficial to adjacent financial services like lending and insurance in ASEAN.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Public Sentiment Towards a Public Rally Using Text and Social Media Analytic","authors":"Sian Lun Lau, Marvin J. H. Lee, Min Xuan Teoh","doi":"10.1109/ICOCO56118.2022.10031692","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031692","url":null,"abstract":"The use of social media for analysing behaviours and trends of the public is a growing research area. In 2021, the #LAWAN social movement emerged in Malaysia as a result of discontentment of the people towards the government. This study intends to study and discover the public perception and sentiment towards the #LAWAN rally. The investigation also questions whether is the civil society supportive of such social movement and rally, especially when rally, protests and demonstration are traditionally and often seen as a negative and non-constructive approach in the country. Tweets with the hashtag #LAWAN over a day on and before the rally day on 31st July 2021 have been collected and analysed. Sentiment analysis helped to identify the public sentiments towards the rally, and topic modelling helped to discover common topics from the 5000+ tweets scrapped from the social media.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127706858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bibliometric Analysis of Global Scientific Literature on Robust Neural Network","authors":"Tengku Nurul Aimi Balqis Tengku, Malim Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat","doi":"10.1109/ICOCO56118.2022.10031676","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031676","url":null,"abstract":"The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of Multi-Channel for 10×10 Mesh Wireless Network-on-Chip Architecture","authors":"A. Lit, Popoola Oluwaseun Lydia, S. Suhaili, R. Sapawi, K. Kipli, D. N. S. Dharmiza","doi":"10.1109/ICOCO56118.2022.10031710","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031710","url":null,"abstract":"Wireless Network-on-Chip (NoC) is envisioned as complementary to the conventional NoC due to its CMOS compatibility and architectural flexibility, which is advantageous as no wiring infrastructure is required for wireless transmission. On-chip wireless channels are used to actually minimize the communication latency between the distant processing cores because of its ability to communicate with long-distance communication processing cores in a single-hop. This paper investigates the effect of the single-, dual-, and triple-channels on the mesh-WiNoC architecture. Additionally, four and nine radio hubs are evenly distributed throughout the mesh-WiNoC topological structure to evaluate its global transmission latency, network throughput, and energy characteristics. The investigated architectures under test are simulated on the cycle-accurate systemC based Noxim simulator under a random traffic workload scenario for WiNoC performance evaluation. This study’s contribution is that it looks into the best number of wireless channels to use in a 10 × 10 mesh WiNoC architecture for 4 and 9 radio hub scenarios to get the best performance in transmission latency and energy consumption. Experimental results show that for both investigated number of radio hub on mesh-WiNoC architecture demonstrates nearly identical system performance in terms of transmission latency and throughput. However, the meshWiNoC architecture with 4 radio hub demonstrates better energy characteristics, saving 9.63% and 13.60% of energy, respectively, when compared to the architecture with 6 and 9 radio hub.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122966221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of FTSE Bursa Malaysia KLCI Stock Market using LSTM Recurrent Neural Network","authors":"T. M. Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat","doi":"10.1109/ICOCO56118.2022.10031901","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031901","url":null,"abstract":"Stock market prediction is vital in the financial world. Investors and people interested in investing would be interested in the future value of the stock market before they invest in it. By using the method of time series, this research gives a contribution to forecast and modelling the FTSE Bursa Malaysia KLCI (FBM KLCI) stock market. In this research, the stock market is forecasted to identify the stock market trend in the future. The FBM KLCI closing prices data was utilized to build Long Short-Term Memory (LSTM) models to predict the stock market. The performance of the model has been evaluated using the root mean squared error (RMSE) and the mean absolute error (MAE) in order to choose the best model. The researcher used the Bursa Malaysia data to forecast the stock market for five years, from October 20, 2016, to October 20, 2021, which has been scrapped from the Yahoo Finance website. The data is analyzed by running Python coding in Google Colab. The result proves that the accuration of the LSTM model by using Recurrent Neural Network (RNN) approach is accurate and the predicted value of the stock market at the date 2021-10-05 is increased by 1.87%. It can be used to predict the future closing stock prices in stock market prediction in FBM KLCI stock market. The results are expected to provide an accurate prediction for a better profit. Thus, prediction in stock market investment can support long-term economic growth, or in other words, it can help economic sustainability.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129378508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Homes in The Age of IoT","authors":"M. Ati, Arooba Khalid","doi":"10.1109/ICOCO56118.2022.10031788","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031788","url":null,"abstract":"With lifestyle changes, digital transformations, and increasing use of the internet, technology has become a part of our daily lives. It has become an essential factor used in all fields such as industry, commerce, education, and entertainment. Technology has also touched areas directly related to human life, such as health, medicine, and food. We are constantly engaging with technology: through smartphones and applications or even in-home devices and systems. We can control and give orders to various electrical smart devices from inside or outside the home; they all work in an interconnected way. For example, we can control the temperature of the house and water during the shower, raise the curtains, or turn on and off modern media and communication devices. This technology contributes to facilitating the user’s daily life and saving time and effort. The smart home contains remote control devices to operate and monitor electrical and electronic devices such as interior lighting, outdoor garden lighting, electric blinds, air conditioning, and TV, in addition to controlling audio systems, cameras, and electric doors using smartphones or screens on the wall. Also, this house contains remote control devices to protect against theft and fire. This study focuses on exploring the idea of smart homes and the related security techniques whilst shedding light on some work related to privacy and security in smart homes.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130405189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ATT&CK Behavior Forecasting based on Collaborative Filtering and Graph Databases","authors":"Masaki Kuwano, Momoka Okuma, Satoshi Okada, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10032036","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10032036","url":null,"abstract":"Cyber attacks are causing tremendous damage around the world. To protect against attacks, many organizations have established or outsourced Security Operation Centers (SOCs) to check a large number of logs daily. Since there is no perfect countermeasure against cyber attacks, it is necessary to detect signs of intrusion quickly to mitigate damage caused by them. However, it is challenging to analyze a lot of logs obtained from PCs and servers inside an organization. Therefore, there is a need for a method of efficiently analyzing logs. In this paper, we propose a recommendation system using the ATT&CK technique, which predicts and visualizes attackers’ behaviors using collaborative filtering so that security analysts can analyze logs efficiently.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Conversion Process Framework to Generate Individual-Level Nutrition Data from Household-Level Grocery Data","authors":"Nuraina Daud, Nurulhuda Noordin, N. Teng","doi":"10.1109/ICOCO56118.2022.10031274","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031274","url":null,"abstract":"This paper presents a data conversion process involving household grocery data. The household grocery data were gathered from the primary source which is directly from 50 selected household in Shah Alam for 5 consecutive months. The data transformation was done to convert the grocery data into the nutrition data. The converted nutrition data will be tested using data mining classification algorithms, and the patterns generated from it will be explored for obesity prediction purposes. In the data transformation process, the raw grocery data has undergone several data pre-processing and conversion methods. These processes have been done by the nutritionists as the knowledge on nutrition field are needed in performing this task. The processes involved are calorie conversion, macronutrient grouping, food pyramid grouping, and food categorization. There were five methods have been conducted to perform the conversion task which are food composition database, offline and online market survey, food pyramid and knowledge theory on nutrition. The conversion process has been gathered to form Data Conversion Process Framework. This paper also introduced the use of estimation formula using BMI weightage as a method to generate the individual-level nutrition data. The nutrition data generated from the grocery data processing and the conversion process using the BMI weightage highlight the significance of the study. The output from this study (nutrition data) will be used in the later stage of the study as the input data in the development of obesity prediction modelling.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131366245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}