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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Yuslinda Wati Mohamad Yusof
Universiti Teknologi MARA

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Genetic algorithm for intrusion detection system in computer network Hamizan Suhaimi; Saiful Izwan Suliman; Afdallyna Fathiyah Harun; Roslina Mohamad; Yuslinda Wati Mohamad Yusof; Murizah Kassim c
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1670-1676

Abstract

Internet connection nowadays has become one of the essential requirements to execute our daily activities effectively. Among the major applications of wide Internet connections is local area network (LAN) which connects all internet-enabled devices in a small-scale area such as office building, computer lab etc. This connection will allow legit user to access the resources of the network anywhere as long as authorization is acquired. However, this might be seen as opportunities for some people to illegally access the network. Hence, the occurrence of network hacking and privacy breach. Therefore, it is very vital for a computer network administrator to install a very protective and effective method to detect any network intrusion and, secondly to protect the network from illegal access that can compromise the security of the resources in the network. These resources include sensitive and confidential information that could jeopardise someone’s life or sovereignty of a country if manipulated by wrong hands.  In network intrusion detection system (NIDS) framework, apart from detecting unauthorized access, it is equally important to recognize the type of intrusions in order for the necessary precautions and preventive measures to take place. This paper presents the application of genetic algorithm (GA) and its steps in performing intrusion detection process. Standard benchmark dataset known as KDD’99 cup was utilized with forty-one distinctive features representing the identity of network connections. Results presented demonstrate the effectiveness of the proposed method and warrant good research focus as it promises exciting discovery in solving similar-patent of problems.   
Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system Shahrani Shahbudin; Murizah Kassim; Roslina Mohamad; Saiful Izwan Suliman; Yuslinda Wati Mohamad Yusof
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1196-1202

Abstract

This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classification tasks using extracted features based on S-transforms methods. The ANFIS model with a five-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal.  Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.
Co-Authors Afdallyna Fathiyah Harun Hamizan Suhaimi Murizah Kassim Murizah Kassim c Roslina Mohamad Saiful Izwan Suliman Shahrani Shahbudin