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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Benaboud, Hafssa
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Computer Science & IT Electrical & Electronics Engineering

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Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes Yzzogh, Hicham; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp817-826

Abstract

Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.
Enhancing SDN security with a feature-based approach using multiple k-means, Word2Vec, and neural network Yzzogh, Hicham; Benaboud, Hafssa
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8834

Abstract

In the rapidly evolving landscape of network management, software-defined networking (SDN) stands out as a transformative technology. It revolutionizes network management by decoupling the control and data planes, enhancing both flexibility and operational efficiency. However, this separation introduces significant security challenges, such as data interception, manipulation, and unauthorized access. To address these issues, this paper investigates the application of advanced clustering and classification algorithms for anomaly detection and traffic analysis in SDN environments. We present a novel approach that integrates multiple k-means clustering models with Word2Vec for feature extraction, followed by classification using a neural network (NN). Our method is rigorously benchmarked against a traditional NN model to comprehensively evaluate performance. Experimental results indicate that our approach outperforms the NN model, achieving an accuracy of 99.97% on the InSDN dataset and 98.65% on the CIC-DDoS2019 dataset, showcasing its effectiveness in detecting anomalies without relying on feature selection. These findings suggest that integrating clustering techniques with feature extraction algorithms can significantly enhance the security of SDN infrastructures.
Challenges of load balancing algorithms in cloud computing utilizing data mining tools Halima, Anouar Ben; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3449-3457

Abstract

In the cloud computing environment, load balancing plays an important role in the efficient operation of cloud computing, where a multitude of resources serve diverse workloads and fluctuating demands. In the rapidly evolving cloud computing, efficient resource management, and optimization are critical for maximizing performance, scalability, and cost-effectiveness. Load balancing algorithms aim to distribute workloads across cloud resources to ensure optimal utilization and maintain high availability of services. This paper presents a comparative study of load balancing algorithms in cloud computing using data mining tools. It underscores the complexity of selecting algorithms for effective load balancing in scenarios with diverse criteria, emphasizing its critical importance for future research and practical implementations. The experimental results are presented, evaluating the performance of different load balancing algorithms using data-mining tools. The outcomes highlight the substantial difficulties when building a model with unacceptable errors to cover users’ needs while selecting the desired load balancing method.
Privacy and confidentiality in internet of things: a literature review Kandil, Hiba; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4249-4258

Abstract

The internet of things (IoT) is a scalable network of interconnected smart devices that aims to improve quality of life, business growth, and efficiency across multiple sectors. Since the IoT is an expanding network, a large amount of data is generated, collected, and exchanged. However, most of this data is personal data that contains private or sensitive information, which makes it a target for several cyber threats due to poor encryption, weak authentication mechanisms, and insecure communications. Therefore, ensuring the privacy and confidentiality of sensitive information remains a critical challenge. This paper presents a comprehensive literature review focusing on privacy and confidentiality issues within the IoT ecosystem. It categorizes existing research into privacy-preserving techniques, authentication and trust mechanisms, and machine learning-based solutions. Beginning by detailing the review methodology employed to gather and analyze relevant research. The review then explores recent research work related to privacy concerns and authentication and trust mechanisms, emphasizing various approaches and solutions developed to address these challenges. The paper further delves into machine learning-based solutions that offer innovative methods for enhancing privacy and confidentiality.
Co-Authors Halima, Anouar Ben Kandil, Hiba Yzzogh, Hicham