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Enhancing Performance Using New Hybrid Intrusion Detection System Candra Supriadi; Charli Sitinjak; Fujiama Diapoldo Silalahi; Nia Dharma Pertiwi; Sigit Umar Anggono
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.134

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.

CREDENTIAL ANALYSIS FOR SECURITY CONFIGURATION ON CUSTOM ANDROID ROM Joseph Teguh Santoso; Fujiama Diapoldo Silalahi; Laksamana Rajendra Haidar
Journal of Technology Informatics and Engineering Vol 1 No 3 (2022): Desember: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i3.149

Android is an operating system with open source and consists of several layers, with the different layers its duties and responsibilities. Various parties in the customization chain such as device vendors such as Samsung, Xiaomi, Oppo, Huawei, and others, operators such as Telkomsel, Smartfren, XL, etc., and hardware manufacturers can customize one or more layers to adapt devices for different purposes, such as supporting specific hardware and providing different interfaces and services. The purpose of this study was to investigate systematically for any inconsistencies that arose as a result of the processes involved in this study and to assess their various security implications. This research runs DroidDiff to perform a substantial-balance diverse investigation on images collected by the analytical methodology. DroidDiff found a lot of differences when it comes to the selected features. The method used in this study is the method of five differential analysis algorithms. As a result, by comparing the security configurations of similar figures, important security changes that could be accidentally introduced during customization can be found. The results show that DroidDiﬀ can be used by vendors to check the configuration of various security features in a given image. DroidDiff will extract those features from the image, and compare them to other image configuration sets, then DroidDiff will flag the inconsistent ones for further investigation by vendors who have the source code and tools to check their effect. For future work, improvements to DroidDiﬀ to more accurately detect risky inconsistencies are highly recommended. Improving DroidDiff will help reduce the number of false positives and determine risky configurations more accurately.

Enhancing Performance Using New Hybrid Intrusion Detection System Candra Supriadi; Charli Sitinjak; Fujiama Diapoldo Silalahi; Nia Dharma Pertiwi; Sigit Umar Anggono
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.134

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.

MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.

CREDENTIAL ANALYSIS FOR SECURITY CONFIGURATION ON CUSTOM ANDROID ROM Joseph Teguh Santoso; Fujiama Diapoldo Silalahi; Laksamana Rajendra Haidar
Journal of Technology Informatics and Engineering Vol 1 No 3 (2022): December: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i3.149

Android is an operating system with open source and consists of several layers, with the different layers its duties and responsibilities. Various parties in the customization chain such as device vendors such as Samsung, Xiaomi, Oppo, Huawei, and others, operators such as Telkomsel, Smartfren, XL, etc., and hardware manufacturers can customize one or more layers to adapt devices for different purposes, such as supporting specific hardware and providing different interfaces and services. The purpose of this study was to investigate systematically for any inconsistencies that arose as a result of the processes involved in this study and to assess their various security implications. This research runs DroidDiff to perform a substantial-balance diverse investigation on images collected by the analytical methodology. DroidDiff found a lot of differences when it comes to the selected features. The method used in this study is the method of five differential analysis algorithms. As a result, by comparing the security configurations of similar figures, important security changes that could be accidentally introduced during customization can be found. The results show that DroidDiﬀ can be used by vendors to check the configuration of various security features in a given image. DroidDiff will extract those features from the image, and compare them to other image configuration sets, then DroidDiff will flag the inconsistent ones for further investigation by vendors who have the source code and tools to check their effect. For future work, improvements to DroidDiﬀ to more accurately detect risky inconsistencies are highly recommended. Improving DroidDiff will help reduce the number of false positives and determine risky configurations more accurately.

Error-Free Arduino Communication: Integrating Hamming Code for UART Serial Transmission Raharjo, Budi; Silalahi, Fujiama Diapoldo
Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.187

Serial communication is a fundamental method for data transfer in electronic devices, particularly in Arduino-based systems. However, existing protocols, such as Universal Asynchronous Receiver/Transmitter (UART), often lack robust error detection mechanisms, leading to potential data integrity issues. This study aims to address the knowledge gap regarding error detection in UART communication by implementing Hamming Code, a well-established method for detecting and correcting single-bit errors. The research employs a systematic approach, including data encoding before transmission and decoding with error correction at the receiver end. The results demonstrate that the integration of the Hamming Code significantly enhances the reliability of data transmission, reducing error rates and improving overall system performance. The implications of this research extend to various applications requiring high data integrity, such as industrial control systems and Internet of Things (IoT) devices. By providing a practical solution to the challenges of error detection in serial communication, this study contributes to the advancement of reliable communication systems in modern technology.

Framework-Driven Design: Analyzing the Impact of the Zachman Framework on LMS Effectiveness Silalahi, Fujiama Diapoldo; Nugroho, Setiyo Adi; Hartono, Budi
Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.196

In today's digital era, Learning Management Systems (LMS) play a crucial role in education. Despite the availability of numerous LMS platforms, challenges in designing effective and efficient systems persist, particularly in integrating comprehensive frameworks like the Zachman Framework. This study aims to explore the application of the Zachman Framework in LMS design to enhance system effectiveness and user satisfaction. The research employs a mixed-methods approach, combining qualitative and quantitative methods. Data is collected through a survey involving 100 respondents, including instructors, LMS developers, and students. The study analyzes qualitative data using thematic analysis and quantitative data through descriptive statistical techniques. The findings reveal that 85% of respondents believe that applying the Zachman Framework in LMS design significantly improves system effectiveness. Additionally, the average user satisfaction score for LMS designed using this framework is 4.2 on a 5-point scale, indicating a high level of satisfaction. This research concludes that implementing the Zachman Framework not only aids in identifying user needs and designing essential system functions but also ensures that all elements are well-integrated. These findings provide valuable insights for LMS developers and educational institutions in creating more effective and responsive systems that meet user needs..

Enhancing Performance Using New Hybrid Intrusion Detection System Candra Supriadi; Charli Sitinjak; Fujiama Diapoldo Silalahi; Nia Dharma Pertiwi; Sigit Umar Anggono
Journal of Technology Informatics and Engineering Vol. 1 No. 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.134

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.

MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol. 1 No. 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.

CREDENTIAL ANALYSIS FOR SECURITY CONFIGURATION ON CUSTOM ANDROID ROM Joseph Teguh Santoso; Fujiama Diapoldo Silalahi; Laksamana Rajendra Haidar
Journal of Technology Informatics and Engineering Vol. 1 No. 3 (2022): December: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i3.149

Android is an operating system with open source and consists of several layers, with the different layers its duties and responsibilities. Various parties in the customization chain such as device vendors such as Samsung, Xiaomi, Oppo, Huawei, and others, operators such as Telkomsel, Smartfren, XL, etc., and hardware manufacturers can customize one or more layers to adapt devices for different purposes, such as supporting specific hardware and providing different interfaces and services. The purpose of this study was to investigate systematically for any inconsistencies that arose as a result of the processes involved in this study and to assess their various security implications. This research runs DroidDiff to perform a substantial-balance diverse investigation on images collected by the analytical methodology. DroidDiff found a lot of differences when it comes to the selected features. The method used in this study is the method of five differential analysis algorithms. As a result, by comparing the security configurations of similar figures, important security changes that could be accidentally introduced during customization can be found. The results show that DroidDiﬀ can be used by vendors to check the configuration of various security features in a given image. DroidDiff will extract those features from the image, and compare them to other image configuration sets, then DroidDiff will flag the inconsistent ones for further investigation by vendors who have the source code and tools to check their effect. For future work, improvements to DroidDiﬀ to more accurately detect risky inconsistencies are highly recommended. Improving DroidDiff will help reduce the number of false positives and determine risky configurations more accurately.

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