Qasem Abu Al-Haija

Work place: Electrical and Computer Engineering Department, Tennessee State University, Nashville/TN, USA



Research Interests: Computer Architecture and Organization, Computer systems and computational processes, Computer Science & Information Technology, Combinatorial Optimization, Embedded System, Mathematics of Computing


Qasem Abu Al-Haija is a PhD Candidate of Computer & Information Systems Engineering (CISE) program at Tennessee State University, USA. Eng. Abu Al-Haija received his B.Sc. in ECE from Mu’tah University in Feb-2005 & M.Sc. in computer engineering from Jordan university of science & technology in Dec-2009. Research Interests: Cryptography, Cybersecurity, Cyber System Modelling & Optimization, Computer Arithmetic, IoT and Embedded System Design.

Author Articles
Adversarial Deep Learning in Anomaly based Intrusion Detection Systems for IoT Environments

By Khalid Albulayhi Qasem Abu Al-Haija

DOI:, Pub. Date: 8 Aug. 2023

Using deep learning networks, anomaly detection systems have seen better performance and precision. However, adversarial examples render deep learning-based anomaly detection systems insecure since attackers can fool them, increasing the attack success rate. Therefore, improving anomaly systems' robustness against adversarial attacks is imperative. This paper tests adversarial examples against three anomaly detection models based on Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Deep Belief Network (DBN). It assesses the susceptibility of current datasets (in particular, UNSW-NB15 and Bot-IoT datasets) that represent the contemporary network environment. The result demonstrates the viability of the attacks for both datasets where adversarial samples diminished the overall performance of detection. The result of DL Algorithms gave different results against the adversarial samples in both our datasets. The DBN gave the best performance on the UNSW dataset.

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A Methodical Study for Time-Frequency Analysis Model with Experimental Case Study on Chirp Signal

By Qasem Abu Al-Haija

DOI:, Pub. Date: 8 Jun. 2020

In this paper, we are reporting on the comprehensive model design for time-frequency analysis system using Short-Time Fourier Transform (STFT) and Wigner-Ville Distribution (WVD) methods. As a case study, both STFT and WVD based time-frequency transforms have been developed via MATLAB platform and applied for both Chirp and Sunspot signals. The developed model considers the use of hamming moving window of length L=50 with 90% overlapping between the current and previous window positions. The simulation results showed that WVD is more accurate method for time and frequency analysis than STFT since it can provide simultaneous localization in both time and frequency with higher resolution than STFT which can only provide localization in either time or frequency at the same time. Also, the applied techniques provide an adequate distribution of time-frequency analysis only if they used with a non-stationary signal such as Chirp signal.

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A Systematic Expository Review of Schmidt-Samoa Cryptosystem

By Qasem Abu Al-Haija Mohamad M.Asad Ibrahim Marouf

DOI:, Pub. Date: 8 Apr. 2018

Public key cryptographic schemes are vastly used to ensure confidentiality, integrity, authentication and non-repudiation. Schmidt-Samoa cryptosystem (SSC) is a public key cryptosystem, which depends on the difficulty of large integer factorization problem. The implementation of SSC to secure different recent communication technologies such as cloud and fog computing is on demand due to the assorted security services offered by SSC such as data encryption/decryption, digital signature and data integrity. In this paper, we provide a systematic review of SSC public key cryptosystem to help crypto-designers to implement SSC efficiently and adopt it in hardware or software-based applications. According to the literature, the effective utilization and design SSC can place it as a viable alternative of RSA cryptosystems and many others.

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