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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Anbar, Mohammed
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Straggler handling approaches in mapreduce framework: a comparative study Katrawi, Anwar H.; Abdullah, Rosni; Anbar, Mohammed; AlShourbaji, Ibrahim; Abasi, Ammar Kamal
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp375-382

Abstract

The proliferation of information technology produces a huge amount of data called big data that cannot be processed by traditional database systems. These Various types of data come from different sources. However, stragglers are a major bottleneck in big data processing, and hence the early detection and accurate identification of stragglers can have important impacts on the performance of big data processing. This work aims to assess five stragglers identification methods: Hadoop native scheduler, LATE Scheduler, Mantri, MonTool, and Dolly. The performance of these techniques was evaluated based on three benchmarked methods: Sort, Grep and WordCount. The results show that the LATE Scheduler performs the best and it would be efficient to obtain better results for stragglers identification.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.
An efficient intrusion detection systems in fog computing using forward selection and BiLSTM Abu Zwayed, Fadi; Anbar, Mohammed; Manickam, Selvakumar; Sanjalawe, Yousef; Alrababah, Hamza; Hasbullah, Iznan H.; Almi’ani, Noor
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7143

Abstract

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.
Secure map-based crypto-stego technique based on mac address Kasasbeh, Dima S.; Al-Ja’afreh, Bushra M.; Anbar, Mohammed; Hasbullah, Iznan H.; Al Khasawneh, Mahmoud
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7140

Abstract

Steganography and cryptography are spy craft cousins, working differently to achieve the same target. Cryptography is perceptible and observable without understanding the real content, while steganography hides the content so that it is not perceptible or observable and without producing noticeable changes to the carrier image. The challenge is finding the right balance between security and retrievability of embedded data from embedding locations without increasing the required embedded information. This paper proposes a secure map-based steganography technique to enhance the message security level based on the sender and recipient mac addresses. The proposed technique uses rivest-shamir-adleman (RSA) to encrypt the message, then embeds the cipher message in the host image based on the sender and recipient media access control addresses (mac addresses) exclusive or operation "XOR" results without increasing the required embedded information for the embedding location map. The proposed technique is evaluated on various metrics, including peak signal-to-noise ratio (PSNR) and embedding capacity, and the results show that it provides a high level of security and robustness against attacks without an extra location map. The proposed technique can embed more data up to 196.608 KB in the same image with a PSNR higher than 50.58 dB.
Security Issues and Weaknesses in Blockchain Cloud Infrastructure: A Review Article Albaroodi, Hala A.; Anbar, Mohammed
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.324

Abstract

Cloud computing has become an essential technology due to its ability to provide scalable infrastructure and data services at a low cost and with minimal effort. It is widely adopted across various IT sectors and excels in providing flexible and scalable solutions for storage, computation, and networking. However, despite its widespread adoption, information security concerns remain a significant challenge, hampering its full potential. Issues such as data breaches, insufficient access controls, privacy risks, and vulnerability to external attacks persist, making security a critical obstacle for cloud computing’s growth. At the same time, blockchain technology has emerged as a promising solution for addressing these security challenges. Celebrated for ensuring data integrity, authenticity, and confidentiality, blockchain’s decentralized structure offers a potential safeguard against the risks cloud systems face. For instance, blockchain’s ability to maintain an immutable, tamper-proof ledger and decentralized control can mitigate unauthorised access risks, thereby enhancing cloud environments' transparency and security. One of the blockchain’s core components is the consensus protocol, a method through which a network of nodes validates transactions without needing to trust any single entity. In the case of Bitcoin, users follow the Proof of Work algorithm, dedicating hardware and energy resources to solve cryptographic puzzles and verify transactions. This decentralized verification process addresses fraud concerns, but it also brings challenges such as high energy consumption and network centralization, particularly in regions with cheap electricity. These concerns have led to worries about collusion risks and policy changes affecting the stability of the network. Blockchain’s decentralized nature has sparked significant interest, especially in its potential to enhance cloud computing security. Its ability to provide tamper-proof transaction logs, eliminate single points of failure, and grant users more control over data aligns well with the security demands of cloud environments. However, blockchain itself faces challenges, including scalability issues and its association with black-market trading due to its open-access model. Despite these concerns, blockchain’s integration into cloud systems presents a unique opportunity for addressing key security obstacles, thereby offering more robust solutions for corporate and financial applications.
A deep learning approach to detect DDoS flooding attacks on SDN controller Bahashwan, Abdullah Ahmed; Anbar, Mohammed; Manickam, Selvakumar; Al-Amiedy, Taief Alaa; Hasbullah, Iznan H.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1245-1255

Abstract

Software-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically target the controller, disrupting services for legitimate users and depleting its resources, including bandwidth, memory, and processing power. This research aims to develop an effective deep learning (DL) approach to detect such attacks, ensuring the availability, integrity, and consistency of SDN network functions. The proposed DL detection approach achieves 98.068% accuracy, 98.085% precision, 98.067% recall, 98.057% F1-score, 1.34% false positive rate (FPR), and 1.713% detection time.
Name privacy on named data networking: a survey and future research Shah, Mohammad Shahrul Mohd; Leau, Yu-Beng; Anbar, Mohammed; Zhao, Liang
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.pp3039-3053

Abstract

Information-centric networking (ICN) has gained significant interest in recent years, attracting both academic and industry, it represents a paradigm shift and moving away from the host-based IP networks that dominate today landscape. As ICN technology matures and advances towards real-world deployment, the importance of addressing security and privacy concerns has grown exponentially. The ICN paradigm is deliberately designed to encompass numerous security and privacy features, including but not limited to provenance and privacy. These features, which are often lacking in the host-centric paradigm, inherently form a core aspect of ICN. Nevertheless, due to its relatively recent emergence, the ICN paradigm also presents a range of unresolved privacy challenges. This paper offers a comprehensive survey of the existing literature on privacy primarily focuses on major domains name privacy. We delve into the fundamental principles of existing research and evaluate the limitations of proposed methodologies. In name privacy, we also explore strategies to preserve name privacy. We have identified future research directions and highlighted ongoing challenges in the pursuit of enhancing ICN privacy.
Improving firewall performance using hybrid of optimization algorithms and decision trees classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; Alsaaidah, Adeeb M.; Anbar, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2839-2848

Abstract

One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.
Co-Authors Abasi, Ammar Kamal Abu Zwayed, Fadi Abu-Shareha, Ahmad Adel Abualhaj, Mosleh M. Al Khasawneh, Mahmoud Al-Amiedy, Taief Alaa Al-Ja’afreh, Bushra M. Al-Khatib, Sumaya Nabil Al-Shamayleh, Ahmad Sami Albaroodi, Hala A. Alkhatib, Sumaya Nabil Almi’ani, Noor Alrababah, Hamza Alsaaidah, Adeeb M. AlShourbaji, Ibrahim Bahashwan, Abdullah Ahmed Hasbullah, Iznan H. Hiari, Mohammad O. Kasasbeh, Dima S. Katrawi, Anwar H. Leau, Yu-Beng Liang Zhao Manickam, Selvakumar Munther, Alhamza Rosni Abdullah Sanjalawe, Yousef Shah, Mohammad Shahrul Mohd