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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mahdi Nsaif Jasim
University of Information Technology and Communications
Electrical & Electronics Engineering

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Fast and accurate classifying model for denial-of-service attacks by using machine learning Mohammed Ibrahim Kareem; Mahdi Nsaif Jasim
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3688

Abstract

A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.
Geolocation based air pollution mobile monitoring system Aya Mazin Talib; Mahdi Nsaif Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp162-170

Abstract

Air pollution is conducted to harmful substances like solid particles, gases or liquid droplets. More pollutants CO, SO2, NOx, CO2.This research is proposed the design and implementation of mobile, low cost and accurate air pollution monitoring system using Arduino microcontroller and gas sensor like MQ2, MQ131, MQ135, MQ136, DHT22, measuring materials mentioned above, smoke, Acetone, Alcohol, LPG, Toluene, temperature, humidity and GPS sensor”NEO-6M” that track the location of air pollution data, and display the analysis result on ESRI maps. The system also save the results on SQL server DB. The data is classified using data mining algorithms, presenting the result on a map helps governmental organizations, nature guards, and ecologists to analyze data in real time to simplify the decision making process. The proposed system uses J48 pruning tree classifier generated using cross validation of fold (10) with highest accuracy 100%, while IBK ≈99.67, Naïve bays ≈90.89, and SVM ≈81.4. It’s found that the common air quality for Baghdad (study area) is between (“Good”, “Satisfactory”, and “Moderately”) for 1835 records of air samples during (January and February 2021) time period.
Machine learning classification-based portscan attacks detection using decision table Mahdi Nsaif Jasim; Ali Munther Abdul Rahman; Muthanna Jabbar Abdulredhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1466-1472

Abstract

Port scanner attackers are typically used to identify weak points or vulnerabilities in an organization's network. When attackers send a detective message to a port number, the response tells them whether the port is open and assists them in identifying potential vulnerabilities. However, machinelearning approaches are the most effective techniques for detecting and identifying port scanner attacks. This attack is regarded as one of the most dangerous internet threats. This research aims to strengthen the detection accuracy and reduce the detection time. Tagged network traffic data sets are used to train the classification machine learning techniques. On the other hand, network traffic analysis is used by unsupervised method to detect attacks. This study modifies the decision table and OneR classification algorithms as a supervised technique for portscan detection. The proposed algorithm uses the CICIDS2017 dataset for both training and testing. The proposed hybrid feature selection methods use and apply multiple training and testing through a sequence of experiments, the proposed method is capable of detecting the portscan attack with 99.8% accuracy, which is competitive in addition to the proposed combination's fast response.
Entropy-based distributed denial of service attack detection in software-defined networking Mohammed Ibrahim Kareem; Mahdi Nsaif Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1542-1549

Abstract

Software defined networking (SDN) is a new network architecture that allows for centralized network control. The separation of the data plane from the control plane, which establishes a programmable network environment, is the key breakthrough underpinning SDN. The controller facilitates the deployment of services that specify control policies and delivers these rules to the data plane using a common protocol such as OpenFlow at the control plane. Despite the many advantages of this design, SDN security remains a worry because the aforementioned chapter expands the network's attack surface. In fact, denial of service (DoS) assaults pose a significant threat to SDN settings in a variety of ways, owing to flaws in the data and control layers. This work shows how distributed denial of service (DDoS) attack detection is based on the entropy variation of the destination IP address. The study takes advantage of the OpenFlow protocol's (OFP) flexibility and an OpenFlow controller (POX) to apply the proposed method. An entropy computation to determine the distributed features of DDoS traffic is developed and it is capable of detecting a user datagram protocol (UDP) flood attack after 0.445 seconds this type of attack occurred.
K-Means clustering-based semi-supervised for DDoS attacks classification Mahdi Nsaif Jasim; Methaq Talib Gaata
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4353

Abstract

Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.
Cluster-based denoising autoencoders for rate prediction recommender systems Ammar Abdulsalam Al-Asadi; Mahdi Nsaif Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1805-1812

Abstract

Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users’ preferences, and they build a model based on all the users’ rates. This paper proposed an optimized clustering-based denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users’ preferences using k-means algorithm combined with a nature-inspired algorithm (NIA) such as artificial fish swarm algorithm to determine the optimal initial centroids to cluster the users based on their similar preferences, and each cluster trains its own denoising autoencoder (DAE) model. The results proved that combining NIA with k-means gives better clustering results comparing with using k-means alone. OCB-DAE was trained and tested with MovieLens 1M dataset where 80% of it is used for training and 20% for testing. Root mean squared error (RMSE) score was used to evaluate the performance of the proposed model which was 0.618. It outperformed the other models that use autoencoder and denoising autoencoder without clustering with 38.5% and 29.5% respectively.
Co-Authors Ali Munther Abdul Rahman Ammar Abdulsalam Al-Asadi Aya Mazin Talib Methaq Talib Gaata Mohammed Ibrahim Kareem Muthanna Jabbar Abdulredhi