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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Anbar
Universiti Sains Malaysia (USM)
Computer Science & IT Electrical & Electronics Engineering

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Security schemes based on conditional privacy-preserving vehicular ad hoc networks Mahmood A. Al-shareeda; Mohammed Anbar; Murtadha A. Alazzawi; Selvakumar Manickam; Iznan H. Hasbullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp479-488

Abstract

Recently, vehicular ad hoc networks (VANETs) have been garnering significant inter-est from the people involved in transportation field. Nowadays automotive manufactur-ers have already supplying vehicles with multitude of road sensors that provides many useful characteristics. VANET communication not only offers the drivers and passen-gers with the various safety related services but also provides a wide range of valuable applications. However, the inherent openness of the wireless communication medium used by VANETs exposes vehicles to various security and privacy issues. Researchers have proposed many security schemes to solve the issues mentioned above for the widespread deployment of VANETs. However, these schemes failed to fulfill all as-pects of security and privacy requirements. Besides, these schemes have not provided the performance parameters such as computation and communication costs. The pri-mary emphasis of this paper is on the taxonomy of security schemes based conditional privacy-preserving with respect to strengths and limitations. Besides, a comparison be-tween these schemes related to the model of security and privacy requirements, attacks, and performance parameters is provided. Finally, this paper critically reviews the re-lated works by taking into consideration the design and development of all VANETs security and privacy schemes, this paper could serve as a guide and reference.
Detecting anomalies in MQTT/MQTT-SN traffic using intelligent learning models Nabeel Mustafa Alassaf; Selvakumar Manickam; Ammar Odeh; Mohammed Anbar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11889

Abstract

The widespread adoption of the internet of things (IoT) has heightened demand for secure, efficient communication across constrained devices. Lightweight protocols such as message queuing telemetry transport (MQTT) and its variant MQTT-sensor networks (SN) are widely used for IoT messaging but lack intrinsic security mechanisms, leaving them vulnerable to denial-of-service, spoofing, and injection attacks. This study presents a machine learning (ML)-based anomaly detection framework designed to enhance the security of MQTT and MQTT-SN traffic. We emulate realistic IoT environments to generate both benign and malicious traffic, extracting protocol-specific features such as packet length, topic length, quality of service (QoS) levels, and publish frequency. Three supervised models—random forest (RF), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were trained and evaluated using cross-validation and statistical performance metrics. Experimental findings demonstrate that XGBoost achieved the best overall results, with 97.4% accuracy, 95.9% F1-score, and low false-positive and false-negative rates. Furthermore, the framework was successfully deployed on edge devices such as Raspberry Pi Zero W and ESP32, confirming its real-time feasibility and efficiency. The proposed approach highlights the potential of intelligent learning models to deliver lightweight, deployable, and effective intrusion detection for IoT systems utilizing MQTT and MQTT-SN communication protocols.
Ultra-lightweight hybrid authentication for MQTT/MQTT-SN internet of thing security Nabeel Alassaf; Selvakumar Manickam; Ammar Odeh; Mohammed Anbar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11907

Abstract

The rapid growth of internet of thing (IoT) has increased the need for secure communication among resource-constrained devices using lightweight protocols such as message queuing telemetry transport (MQTT) and message queuing telemetry transport for sensor network (MQTT-SN). Traditional certificate-based solutions introduce significant computational and memory overhead for low-power devices. This paper proposes the hybrid lightweight protocol (HLP), a certificate-free approach combining elliptic-curve key exchange, hash-based message authentication code (HMAC)-based authentication, and ChaCha20-Poly1305 encryption. HLP uses pre-shared keys to reduce handshake complexity while maintaining confidentiality, integrity, and mutual authentication across MQTT and MQTT-SN environments. A Python-based implementation using paho-mqtt was evaluated in a constrained-device testbed. Experimental results show that HLP achieves lower handshake latency (-20–24 ms) and reduced bandwidth overhead (-130 bytes) compared with elliptic curve Diffie-Hellman ephemeral-pre-shared key (ECDHE-PSK) and elliptic curve Diffie-Hellman ephemeral-elliptic curve digital signature algorithm (ECDHE-ECDSA), while still supporting forward secrecy. These findings demonstrate that HLP is an efficient and practical solution for securing IoT communications on constrained devices.
Enhancing spam detection using Harris Hawks optimization algorithm Mosleh M. Abualhaj; Sumaya Nabil Alkhatib; Ahmad Adel Abu-Shareha; Adeeb M. Alsaaidah; Mohammed Anbar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26615

Abstract

This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.
A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms Mosleh M. Abualhaj; Sumaya Nabil Al-Khatib; Nida Al-Shafi; Mohammad O. Hiari; Mohammad Sh. Daoud; Mohammed Anbar; Mahran M. Al-Zyoud
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9887

Abstract

This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Co-Authors Adeeb M. Alsaaidah Ahmad Adel Abu-Shareha Ammar Odeh Ammar Odeh Iznan H. Hasbullah Mahmood A. Al-shareeda Mahran M. Al-Zyoud Mohammad O. Hiari Mohammad Sh. Daoud Mosleh M. Abualhaj Murtadha A. Alazzawi Nabeel Alassaf Nabeel Mustafa Alassaf Nida Al-Shafi Selvakumar Manickam Sumaya Nabil Al-Khatib Sumaya Nabil Alkhatib