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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.
Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics Atthariq, Muhammad Daffa; Hidayat, Rizky Fauzi Ari; Sadida, Medina Kaulan; Syafa'ah, Lailis; Sumadi, Fauzi Dwi Setiawan
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1691

Abstract

The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
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 Atthariq, Muhammad Daffa 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 Hidayat, Rizky Fauzi Ari Ibrahim, Zaidah 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 Sadida, Medina Kaulan Said imam Baihaqqi Setiyo Kantomo, Ilham Sri Kusuma Aditya, Christian Syafa'ah, Lailis Syafaah, Lailis Syaifuddin Syaifuddin Syaifuddin Syaifuddin Syaifuddin, Syaifuddin Vera Suryani Wahyuli Dwiki Nanda Yoga Pamungkas Yuda Munarko Yusril Izza Rizqony Zamah Sari