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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd. Yazid Idris
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Intrusion detection with deep learning on internet of things heterogeneous network Sharipuddin Sharipuddin; Benni Purnama; Kurniabudi Kurniabudi; Eko Arip Winanto; Deris Stiawan; Darmawijoyo Hanapi; Mohd. Yazid Idris; Rahmat Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp735-742

Abstract

The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).
Attack and Vulnerability Penetration Testing: FreeBSD Deris Stiawan; Mohd. Yazid Idris; Abdul Hanan Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 2: June 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i2.942

Abstract

 Computer system security has become a major concern over the past few years. Attacks, threats or intrusions, against computer system and network have become commonplace events. However, there are some system devices and other tools that are available to overcome the threat of these attacks. Currently, cyber attack is a major research and inevitable. This paper presents some steps of penetration in FreeBSD operating system, some tools and new steps to attack used in this experiment, probes for reconnaissance, guessing password via brute force, gaining privilege access and flooding victim machine to decrease availability. All these attacks were executed and infiltrate within the environment of Intrusion Threat Detection Universiti Teknologi Malaysia (ITD UTM) data set. This work is expected to be a reference for practitioners to prepare their systems from Internet attacks.
IoT Smart Device for e-Learning Content Sharing on Hybrid Cloud Environment Mohd. Yazid Idris; Deris Stiawan; Nik Mohd Habibullah; Abdul Hadi Fikri; Mohd Rozaini Abd Rahim; Massolehin Dasuki
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (697.409 KB) | DOI: 10.11591/eecsi.v4.978

Abstract

Centralized e-Learning technology has dominated the learning ecosystem that brings a lot of potential usage on media rich learning materials. However, the centralized architecture has their own constraint to support large number of users for accessing large size of learning contents. On the other hand, Content Delivery Network (CDN) solution which relies on distributed architecture provides an alternative solution to eliminate  bottleneck  access.  Although  CDN  is   an  effective solution, the implementation of technology is expensive and has less impact for student who lives in limited or non-existence internet access in geographical area. In this paper, we introduce an IoT smart device to provide e-Learning access for content sharing on hybrid cloud environment with distributed peer-to- peer communication solution for data synchronization and updates. The IoT smart device acts as an intermediate device between user and cloud services, and provides content sharing solution without fully depending on the cloud server.
IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning Susanto Susanto; Deris Stiawan; M. Agus Syamsul Arifin; Mohd. Yazid Idris; Rahmat Budiarto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2080

Abstract

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.
Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm Kurniabudi Kurniabudi; Abdul Harris; Albertus Edward Mintaria; Darmawijoyo Hanapi; Deris Stiawan; Mohd. Yazid Idris; Rahmat Budiarto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2099

Abstract

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.
The trend malware source of IoT network Susanto Susanto; M. Agus Syamsul Arifin; Deris Stiawan; Mohd. Yazid Idris; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp450-459

Abstract

Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.
Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks Nurhadi Nurhadi; Deris Stiawan; Mohd. Yazid Idris; Saparudin Saparudin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4104

Abstract

Augmented reality (AR) has been used in maintenance, simulation, and remote assistance, among other applications. In AR systems, one of the significant issues is the placement of objects in augmented physical environments. Given the importance of object placement in AR systems, we proposed deep learning-based object placement, covering both object detection and object segmentation, to address relevant issues. Deep learning can help users complete tasks by providing the right information effectively, with the method taking into account dynamically changing environments and users’ situations in real time. The problem is that it is rarely used in AR, thereby prompting the combination of deep learning-based object detection and instance segmentation with wearable AR technology to improve the performance of complex tasks. This challenge was addressed in this work through the use of convolutional neural networks in the detection and segmentation of objects in actual environments. We measured the performance of AR technology on the basis of detection accuracy under environmental conditions of different intensities. Experimental results showed satisfactory segmentation and accurate detection
Co-Authors Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Albertus Edward Mintaria Darmawijoyo, Darmawijoyo Deris Stiawan Deris Stiawan Eko Arip Winanto Kurniabudi, Kurniabudi Massolehin Dasuki Mohd Rozaini Abd Rahim Nik Mohd Habibullah Nurhadi Nurhadi Purnama, Benni Rahmat Budiarto Saparudin, Saparudin Sharipuddin, Sharipuddin Susanto Susanto Susanto Susanto Syamsul Arifin, M. Agus Yundari, Yundari