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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Resona : Jurnal Ilmiah Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Repositor
Sumadi, Fauzi Dwi Setiawan
Universitas Muhammadiyah Malang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Public Health Social Sciences

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Journal : JOIV : International Journal on Informatics Visualization

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SD-Honeypot Integration for Mitigating DDoS Attack Using Machine Learning Approaches Fauzi Dwi Setiawan Sumadi; Alrizal Rakhmat Widagdo; Abyan Faishal Reza; - Syaifuddin
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.853

Abstract

Distributed Denial of Services (DDoS) is still considered the main availability problem in computer networks. Developing a programmable Intrusion Prevention System (IPS) application in a Software Defined Network (SDN) may solve the specified problem. However, the deployment of centralized logic control can create a single point of failure on the network. This paper proposed the integration of Honeypot Sensor (Suricata) on the SDN environment, namely the SD-Honeypot network, to resolve the DDoS attack using a machine learning approach. The application employed several algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), and Random Forest (RF)) and comparatively analyzed. The dataset used during the emulation utilized the extracted Internet Control Message Protocol (ICMP) flood data from the Suricata sensor. In order to measure the effectiveness of detection and mitigation modules, several variables were examined, namely, accuracy, precision, recall, and the promptness of the flow mitigation installation process. The Honeypot server transmitted the flow rule modification message for blocking the attack using the Representational State Transfer Application Programming Interface (REST API). The experiment results showed the effectiveness of CART algorithm for detecting and resolving the intrusion. Despite the accuracy score pointed at 69-70%, the algorithm could promptly deploy the mitigation flow within 31-49ms compared to the SVM, which produced 93-94% accuracy, but the flow installation required 112-305ms. The developed CART module can be considered a solution to prevent the attack effectively based on the analyzed variable.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Agus Eko Minarno; Ilham Setiyo Kantomo; Fauzi Dwi Setiawan Sumadi; Hanung Adi Nugroho; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Co-Authors - Syaifuddin Abyan Faishal Reza Achmad Irfani Nur Iman Agung Wahyu Purnadi Agus Eko Minarno Agus Eko Minarno Ahmad Akbar Maulana Ahmad Fahri Jamaluddin Ahmad Rizky Habibullah Akbi, Denar Regata Alrizal Rakhmat Widagdo Amrul Faruq Basuki, Setio Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Diah Risqiwati Edi H. Kalabo Flora Salfa Salsabila Hanung Adi Nugroho Hardianto Wibowo Haris Zulfikar Islam Hariyady Ilham Setiyo Kantomo Irfan, Muhammad Irmawati Feren Kilwalaga Jurdan Wahyu Adi Saputra Lailis Syafa’ah Lucky Nurfiqin Mahar Faiqurahman Mauridhi Hery Purnomo Mirza Maulana Azmi Moch Rizky Wibowo Muhamad Fathul Azis Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Nur Ichsan Muhammad Zain Iqbal Muzakki Muhammad Zidan Najmuddin Tsaqib Nur Armita Nuryasin, Ilyas Oxicusa Gugi Housman Said imam Baihaqqi Sri Kusuma Aditya, Christian Syafaah, Lailis Syaifuddin Syaifuddin Syaifuddin Syaifuddin Syaifuddin, Syaifuddin Vera Suryani Wahyuli Dwiki Nanda Yoga Pamungkas Yuda Munarko Yusril Izza Rizqony Zaidah Ibrahim Zamah Sari