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The Implementation of Octave-S in the Risk Management Measurement of Information System in the Department of Communication, Informatics, and Statistics 4th

The Implementation of Octave-S in the Risk Management Measur...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Sukardi, Oggsa Gaol, Ford Lumban Matsuo, Tokuro Master of Information System Management Binus Graduate Programs Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

An information system is an important part in an organization to support business processes and to achieve its vision and mission. The information system nowadays has been one of the assets that ought to be protected by an organization. The Department of Communication, Informatics, and Statistics has the information system supporting its various operational activities. In order to protect the information system, OCTAVE-S method as the management method is used. It is expected that the OCTAVE-S method can manage the risks to the information system as the asset by specifying the possible risks and developing countermeasures and mitigation plans for each of the risks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Automation testing using silk test workbench for website

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Procedia computer science 2023年 216卷 128-135页

作者： Feliks Abedyoga Chandra Parapat Gede Putra Kusuma Muhammad Rizki Nur Majiid Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Automation testing is essential to carry out functional testing quickly and precisely. Software testing is beneficial for testers doing many testing processes according to the existing scenarios. So, there is an urgency to explore a more advanced solution to replace the traditional approach. Silk Test Workbench is a tool for automation testing that can accelerate the functional testing of complex applications. For this research, we proposed automation testing on website applications with various testing scenarios that have been made. The visual test recording method is used for automation testing with Silk Test Workbench. Silk Test Workbench can speed up testing time, make a good test asset testing scenario, make it easier for users to do testing, and speed up the process to get the result in the form of a screen capture of the testing process. A comparison of testing time has been performed. The automatic testing time with the scenarios is 2 hours 46 minutes. The automatic testing time will be 2 hours 36 minutes using the all-in-one scenario. This study also used questionnaires from colleagues in the Quality Assurance field to get maximum User Acceptance Test results.

关键词： Automation Testing Silk Test Workbench Testing Strategy Testing Environment Web Application

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Silaen, Kalpin Erlangga Meyliana, Meyliana Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Hidayanto, Achmad Nizar Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Information System Depok Indonesia

ISBN: (纸本)9798350382914

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity. © 2023 IEEE.

关键词： Machine learning

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BERT base model for toxic comment analysis on Indonesian social media

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Procedia computer science 2023年 216卷 714-721页

作者： Ghinaa Zain Nabiilah Simeon Yuda Prasetyo Zahra Nabila Izdihar Abba Suganda Girsang Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897.

关键词： Toxic Comment MBERT IndoBERT IndoRoBERTa Small Indonesian social media

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Passive Identification of Rogue Industrial Access Points Using a One-Class Machine Learning Model

Passive Identification of Rogue Industrial Access Points Usi...

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Annual Conference of Industrial Electronics Society

作者： André L. de S. Paula Eduardo K. Viegas Altair O. Santin Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Access point (AP) security has become increasingly important as wireless local area networks (WLANs) proliferate in industrial environments. Rogue APs are often used by attackers to conduct man-in-the-middle (MiTM) attacks. They can redirect users to malicious servers or do eavesdropping and manipulation of their *** this paper, we propose a novel one-class machine learning model to passively identify rogue APs in industrial environments. The implementation of the model is twofold. First, we passively extract the hardware and software characteristics of the evaluated AP according to its generated messages. This results in a comprehensive feature set that captures both low-level and high-level behaviors of the evaluated ***, we apply a one-class machine learning model to identify APs that significantly deviate from the previously known profile of legitimate APs. The combined evaluation of hardware and software behaviors integrated with an outlier detection scheme to effectively identify rogue APs is the insight of our proposal. We demonstrate the feasibility of our model, achieving an F1 score of 0.89 and a true positive rate of 0.9 in experiments conducted on our new publicly available dataset of 357 unique AP behaviors.

关键词： Wireless LAN Machine learning Network security Feature extraction Software Hardware Robustness Software reliability Servers Proposals

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Towards a Feasible Palm Vein Verification Scheme Using Deep Autoencoder and Siamese Networks

Towards a Feasible Palm Vein Verification Scheme Using Deep ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Mateus Nunes Eduardo K. Viegas Altair O. Santin Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Palm vein pattern recognition offers a unique personal identification feature. Unfortunately, these techniques typically require a Near Infrared (NIR) camera sensor to extract the individual's venous pattern, challenging their wide deployment. This paper proposes a new feasible palm vein verification scheme using a Deep Autoencoder and a Siamese Network, implemented threefold. First, we capture the individual's palm using a traditional visible spectrum camera sensor and perform preprocessing tasks to correct imprecise positioning, easing palm support accessories requirements. Second, we eliminate NIR sensor requirement by fine-tuning a Deep Autoencoder model to convert images from the visible spectrum to their infrared counterparts. Third, generated images are processed by a lightweight Siamese network using a contrastive loss function for individual verification. Experiments conducted on a publicly available dataset with over a hundred individuals confirmed the feasibility of our proposal. Our scheme reaches up to 0.97 of true-negative rate, with only 0.01 decrease compared to traditional NIR-based approaches. In addition, individual identification can be conducted in less than 6 seconds in a resource-constrained environment thanks to our lightweight model's implementation.

关键词： Training Performance evaluation Image synthesis Databases Autoencoders Machine learning Cameras Feature extraction Pattern recognition Proposals

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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Serverless utilization in microservice e-learning platform

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Procedia computer science 2023年 216卷 204-212页

作者： Bryan Andreas Nday Gede Putra Kusuma Renaldy Fredyan Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances.

关键词： Cloud Computing E-Learning Function as a Service Hybrid Deployment REST API

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A Multi-View Android Malware Detection Model Through Multi-Objective Optimization

A Multi-View Android Malware Detection Model Through Multi-O...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Philipe Fransozi Jhonatan Geremias Eduardo K. Viegas Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Over the past few years, several highly accurate Machine Learning (ML) techniques have been proposed for Android malware detection. Unfortunately, proposed schemes are rarely used in production, a situation usually caused by their limited generalization capabilities, leading to low accuracies when deployed. This paper proposes a new multi-view Android malware detection model, implemented in two stages. First, we extract multiple feature sets from an analyzed Android application package. The feature sets provide a complementary Android app behavioral vector for the classification task, enhancing the system's generalization. Secondly, we conduct a multi-objective optimization to select the optimal feature subset from each view for subsequent ensemble-based classification. Our proposal's insight is to proactively select each feature subset that simultaneously improves accuracy and reduces processing requirements in a multi-view setting. Experiments on our new dataset, comprising over 40 thousand Android app samples, demonstrated the feasibility of our proposal. Our scheme can improve true-positive rates by an average of 4.4 while demanding only up to 65% of inference processing costs.

关键词： Accuracy Costs Computational modeling Production Machine learning Feature extraction Malware Vectors Proposals Optimization

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Yanfi, Yanfi Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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