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8th International Conference on Computational Science and Technology, ICCST 2021

作者： Cheng, Yun-Quan Mansor, Sarina Chin, Ji-Jian Karim, Hezerul Abdul Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9789811685149

Simulators are games that give a sense of realism and simulate of the real world in a virtual world, such as racing games or serious medical training games. These have been widely documented to have positive effects on the specific technical skills training on their respective field of users. However, in the area of simulators for driving education, there has been a lack of a comprehensive survey which encompasses the various contributions in this area. In this paper, we fill that gap by providing a review of previous work in literature that focuses on driver’s education using simulators. In addition, we include also works on Artifical Intelligence (AI) and Computational Intelligence (CI) in games that could be used to enhance the project and the effects of implementation of virtual reality in such studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual reality

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Geoelectrical subsurface characterization: A case study of saltwater intrusion in Kudat, Sabah 14

Geoelectrical subsurface characterization: A case study of s...

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14th Seminar on Science and Technology 2021, S and T 2021

作者： Ismail, Fatin Nabilahuda Musta, Baba Saleh, Hardianshah Yahya, Farashazillah Faculty of Science and Natural Resources Universiti Malaysia Sabah Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

Sa ltwa ter intrusion is one of the prima ry sources of groundwa ter conta mination, especia lly nea r the coa sta l line, a nd it is crucia l to monitor sa ltwa ter intrusion to protect fresh groundwa ter. Due to the proximity of the coa sta l line, the a rea within Kuda t Peninsula tha t wa s formed by the ophiolite ba sement, Kuda t Forma tion Chert-Spilite Forma tion, a nd a lluvium deposit is susceptible to sa ltwa ter intrusion. In this study, two survey lines of geoelectrica l resistivity a nd induced pola riza tion ima ging survey with a tota l length of 200 m were ca rried out. The Wenner configura tion wa s used to cha ra cterize the subsurfa ce of the Kuda t Peninsula. The interpola tion between geoelectrica l resistivity a nd cha rgea bility profiles provided better interpreta tions of the subsurfa ce environment. The sa ltwa ter intrusion zone displa ys a resistivity va lue between 0 – 5.0 Ωm a nd a cha rgea bility va lue between 0 – 3.0 ms. The results showed that sa ltwa ter intrusion wa s detected a t depths a s sha llow a s 1.25 m to 36.90 m a nd intruded a s fa r a s 218.00 m into the ma inla nd, from the coa stline. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Surveys

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CoinChain: Effective Coin Mixing Tool for Anonymous CryptoCurrency 8th

CoinChain: Effective Coin Mixing Tool for Anonymous CryptoCu...

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8th International Conference on Computational Science and Technology, ICCST 2021

作者： Chan, Wai Kok Chin, Ji-Jian Goh, Vik Tor Faculty of Informatics and Computing Multimedia University Cyberjaya63000 Malaysia Faculty of Engineering Multimedia University Cyberjaya63000 Malaysia

ISBN: (纸本)9789811685149

Many anonymous cryptocurrencies have been introduced to address the privacy issue in Bitcoin. Compared to traditional security schemes like hashing and encryption, the security measurement for cryptocurrency is less precise. In hashing, there is a direct security challenge, such as reproducing a hash collision. However, in anonymous cryptocurrency, a direct security challenge is not precise. Many anonymous cryptocurrency proposals address this issue in the form of cryptographic proof. This paper describes a brief evolution of anonymous cryptocurrency, followed by a brief description of CoinChain Protocol. This paper also proves that CoinChain has a high anonymity set. A methodology and code to produce a simulated blockchain from a list of actual transactions for any third party to decipher is also described. If a third party can correctly decipher the simulated blockchain into actual transaction, then the privacy aspect of the cryptocurrency is consider broken. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mixing

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Comparative Analysis of Machine Learning Classifiers for Phishing Detection 6

Comparative Analysis of Machine Learning Classifiers for Phi...

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6th International Conference on informatics and Computational Sciences, ICICoS 2022

作者： Razak, Mohd Faizal Ab Jaya, Mohd Izham Ernawan, Ferda Firdaus, Ahmad Nugroho, Fajar Agung Universiti Malaysia Pahang Faculty of Computing College of Computing & Applied Sciences Pahang Malaysia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (数字)9781665460996

ISBN: (纸本)9781665460996

In recent years, communication over the Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Nevertheless, the rapid increase use of digital platforms has led to a significant growth of Phishing Attacks. Phishing attacks are one of the most common security issues in digital worlds that can affects both individual and organization in keeping their confidential information secure. Various modern approaches can be used to target an individual and trick them into leaking their sensitive information, which can later, purposely be used to harm the targeted victim or entire organization depending on the cybercriminal's intent and type of data leaked. This paper evaluates phishing detection by using Naïve Bayes, Simple Logistic, Random Forest, Ada Boost and MLP classifications. This study discusses the comparative analysis on the effectiveness of classification for detecting phishing attacks. The results indicated that the detection system trained with the Random Forest produce higher accuracy of 97.98% than another classifier method. © 2022 IEEE.

关键词： Random forests

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Iterative Method for Solving Nonlinear Fredholm Integral Equations Using Quarter-Sweep Newton-PKSOR Method 1

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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Ali, L.H. Sulaiman, J. Saudi, A. Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia

ISBN: (数字)9789811984068

ISBN: (纸本)9789811984051

This paper studies the combination of the quarter-sweep approach with the Newton-PKSOR iteration to solve the nonlinear Fredholm integral equations of the second kind. We call them the quarter-sweep Newton-PKSOR (QSNPKSOR) iterative method. For this purpose, the discretization process is conducted using a first-order quadrature scheme with the quarter-sweep approach to form quarter-sweep approximation equation, which leads to a nonlinear system. Then, we apply Newton’s method to convert it into a linear form. Following that, we solve the generated linear system using PKSOR iterative method. The proposed method is motivated by the complexity reduction technique to gain an approximate solution to the nonlinear Fredholm integral equation. Thus, to validate the findings, we compared the results with full- and half-sweep Newton-PKSOR methods on several numerical examples. The comparison of total computing operations and percentage reduction involved in these iterative methods are also included. The numerical results show that the quarter-sweep NPKSOR method has significantly reduced the iteration time required to solve the problem but poses less accuracy compared to the standard form. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Trip Itinerary Generation with 2-Opt Algorithm 22

Trip Itinerary Generation with 2-Opt Algorithm

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Proceedings of the 4th International Conference on Advanced Information Science and System

作者： Omar Challaf Sook-Ling Chua Lee Kien Foo Faculty of Computing and Informatics Multimedia University Malaysia

ISBN: (纸本)9781450397933

Advances in digital technologies and communications are impacting how people travel. However, there are still lacking of personalised digital-based tools to support people in trip planning. Although there are a number of widely used trip planner applications, these applications may not be effective in generating trip itinerary as they do not take into account of user trip preferences and the total travel time to spend. In this paper, we proposed a heuristic search algorithm based on 2-opt to generate trip itinerary. A proof of concept mobile application for trip planning is developed to demonstrate the validity of our method.

关键词： 2-Opt Algorithm Trip Planning Trip Itinerary Generation

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A Secure Model Based on Symmetric Encryption for REST API in Data Integration 27

A Secure Model Based on Symmetric Encryption for REST API in...

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27th International Computer Science and Engineering Conference, ICSEC 2023

作者： Yusof, Mohd Kamir Man, Mustafa Wan Hamzah, Wan Mohd Amir Fazamin Safei, Suhailan Haryani Tasani, Mimi Shah, Nurul Anis Alia Ahmad Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Terengganu Besut22200 Malaysia Universiti Malaysia Terengganu Faculty of Ocean and Engineering K. Nerus Terengganu 21300 Malaysia

ISBN: (纸本)9798350342109

Data integration is currently demanding by organizations to extract the data from different data sources. A unified view of data structure is needs to allow the organization to access this data sources. REST API one of the solutions can be used to allow application send or share the data to other applications via the network. However, the data transmission via network will be exposed to unauthorized users or hacker to access or modified the data. A symmetric encryption can be used as a secure model for data transmission via the network. Three processes are involved in this secure model. There are encrypt plaintext to ciphertext using combination of keys, decrypt the ciphertext to plaintext using combination of keys, and dump data in plaintext. In this paper, two applications have been developed for data exchanges or data transmission purposes. SAP system send the data in JSON format by encrypted into ciphertext using unique key and three different keys to FEMS system. FEMS system will be decrypted ciphertext by using same unique key and three different key to produce plaintext in JSON format. The data in JSON format will be exploded. Then, the data will be dumped into database FEMS. Based on the implementation, REST API and symmetric encryption have been successfully used for data transmission between SAP and FEMS and encrypt the data in JSON to ciphertext using modification 3DES encryption. © 2023 IEEE.

关键词： Data integration

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A Supervised Model to Detect Suspicious Activities in the Bitcoin Network 2nd

A Supervised Model to Detect Suspicious Activities in the Bi...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Al-Hashedi, Khaled Gubran Magalingam, Pritheega Maarop, Nurazean Samy, Ganthan Narayana Rahim, Fiza Binti Abdul Shanmugam, Mohana Hasan, Mohammad Kamrul Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia College of Computing and Informatics Universiti Tenaga Nasional Malaysia Selangor Kajang43000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

ISBN: (纸本)9783031252730

Shortly after its official launch in 2009, Bitcoin has gained rapid popularity worldwide, which in return attracted a variety of people especially malicious attackers, who get the advantage of its pseudo-anonymity to institute un-traceable threats, scams, and criminal activities. Recently, some Bitcoin thefts have been reported costing millions of dollars, causing serious harm and losses to innocent users or companies that lead them to declare bankruptcy. One of the main characteristics of Bitcoin is its anonymity, which makes Bitcoin the preferred choice for criminals to perform illicit activities that pose difficulties for law enforcement and financial authorities to identify suspicious behavior, making the existing fraud detection systems ineffective. In this paper, we propose a model for detecting suspicious activities in the Bitcoin network. We first construct a labeled dataset by collecting a set of illicit transactions from public online Bitcoin forums, as well as datasets from prior research. Next, a verification and filtration process has been performed to verify the gathered illicit transactions with the original dataset and manually marked them as either legal or illegal. Additionally, a new set of features that are based on time-slice was extracted, the skewed dataset was balanced, and three supervised classifiers (LR, NB, and ANN) were used for evaluating the proposed model. Finally, our findings found that the ANN classifier achieved the best performer among others, which attained Precision, Recall, F1 scores, and AUC of 95.2%, 88.7%, 89.8%, and 91.2% respectively. The performance of the supervised classifiers has significantly improved after balancing the training set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Estimating SIR Model Parameters for Predicting Covid-19 Cases in Bandung City 4

Estimating SIR Model Parameters for Predicting Covid-19 Case...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Bintang, Kahfi M. Binti Alias, Norma Kurniawan, Wandi Yusuf Patrianingsih, Ni Kadek Winda Reganata, Gde Palguna Gunawan, Putu Harry School of Computing Telkom University Bandung40257 Indonesia University Teknologi Malaysia Faculty of Science Johor Skudai81310 Malaysia Universitas Bali Internasional Informatics Department Denpasar Indonesia

ISBN: (纸本)9798331506490

The COVID-19 pandemic has profoundly impacted global public health, with Indonesia among the most severely af-fected. In response, the Bandung City Government implemented various control measures, including the Mayor's Circular on COVID-19 prevention and enforcing restrictions on community activities, known as PPKM. This study applies the Susceptible-Infected-Recovered (SIR) model to analyze the progression of COVID-19 in Bandung City, specifically during the first and second waves. The SIR model was calibrated using historical case data and incorporated the finite probability of recovered individuals becoming susceptible again, which is critical for generating oscillatory solutions in epidemic modeling. The study focused on determining the best transmission rate for both waves. The first wave, occurring between July 4th and October 22nd, 2021, exhibited an optimal transmission rate of 0.38 (MAE: 0.0424). In contrast, the second wave, spanning February 1st to May 15th, 2022, required a higher transmission rate of 0.54 (MAE: 0.0142), reflecting the increased transmissibility of the Omicron variant. Meanwhile, the recovery rate was calculated based on an average incubation period of 14 days, equivalent to 1/14, approximately 0.0714. The results demonstrate that the SIR model successfully captures the epidemic dynamics in the region, providing insights into the effectiveness of governmental policies and restrictions in controlling the virus. Future studies may explore the inclusion of additional parameters, such as vaccination coverage and mobility patterns, which could enhance the model's predictive accuracy. Moreover, hybrid modeling approaches integrating SIR with machine learning may offer more adaptive solutions to rapidly changing epidemic conditions. © 2024 IEEE.

关键词： COVID-19

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