https://dspace.qou.edu/handle/194/2880 2026-04-07T16:55:55Z https://dspace.qou.edu/handle/194/3054 Securing Communication Standards of IoT-based Smart Irrigation Systems in Palestine Younis AlShwieki, Samar; Jaloudi, Dr. Eng. Samer The agricultural sector is one of the most vital sectors in Palestine, where water resource management faces increasing challenges due to water scarcity, limited infrastructure, and the continued use of inefficient traditional irrigation methods. In recent years, Internet of Things-based smart irrigation systems have emerged as an effective solution to improve water use efficiency. However, these systems are exposed to growing security threats, particularly at the communication level, especially in environments that rely on low-power wireless networks. This thesis aims to secure communication standards for IoT-based smart irrigation systems in Palestine by designing and implementing a secure and flexible communication framework that considers the specific characteristics of the Palestinian environment, including the geographical distribution of agricultural lands, variable network coverage, and limited energy resources. A practical smart irrigation system was designed and implemented based on LoRa technology, integrating multiple security mechanisms such as data encryption, secure key management using hardware-based secure elements, and built-in LoRa protection features, such as message headers, to mitigate cyber-attacks, including replay attacks. In addition, an alternative system based on the MQTT protocol was designed to operate when direct Internet connectivity is available, enhance system flexibility, and compare different communication standards in terms of performance and security. When deployed over the TLS protocol, MQTT provides encrypted communication, authentication, and data integrity with low latency, making it suitable for connecting smart irrigation systems to cloud servers and data analytics platforms. The results indicate that combining long-range low-power communication technologies such as LoRa with Internet-based protocols like MQTT improves the reliability and security of smart irrigation systems while maintaining efficient energy consumption. This hybrid approach enhances water resource management and supports data-driven decision-making in Palestine and agricultural sustainability. 2025-01-05T00:00:00Z https://dspace.qou.edu/handle/194/3045 Empowering Palestinian Voices Using Text Mining Techniques to Overcome Social Media Expression Restrictions Abd Alqaher Srour, Maab; Dweib, Dr. Mohamed In an era where digital communication shapes political discourse and collective memory, social media platforms have become both spaces of empowerment and instruments of control. This thesis investigates the algorithmic suppression of Palestinian digital expression on major platforms—Facebook, Instagram, and X (formerly Twitter)—through the integration of Natural Language Processing (NLP) and text mining techniques. The study situates itself within the interdisciplinary domains of artificial intelligence, digital rights, and computational social science, aiming to uncover how algorithmic bias operates within automated moderation systems. Data were collected using Apify-based scrapers and analysed in Kaggle through multiple preprocessing and modelling stages. Techniques such as TF-IDF vectorization, sentiment and emotion analysis, and Named Entity Recognition (NER) were employed to identify linguistic and affective patterns correlated with content suppression. Comparative machine-learning experiments—including Logistic Regression, Naïve Bayes, Linear SVC, SGD, and transformer-based models (BERT and XLM-R)—revealed consistent evidence of algorithmic bias. Facebook demonstrated structural filtering and downranking of politically sensitive posts, Instagram exhibited emotional suppression of solidarity content, and X retained partial transparency but reflected selective engagement constraints. The results confirm that algorithmic repression is not incidental but systematically embedded within platform architectures and moderation logic. Beyond quantitative findings, the research advances an Integrated Research Framework that combines computational rigor with ethical reflection, positioning data science as a form of digital resistance. This thesis contributes to the emerging field of algorithmic justice by presenting empirical evidence of digital repression and proposing a context-aware, ethically grounded approach to AI design. It concludes that reclaiming visibility in the algorithmic age is not merely a technical challenge but a moral and political act—one that defines the future of digital freedom and equity. 2026-01-24T00:00:00Z https://dspace.qou.edu/handle/194/3038 Ransomware Detection: The Efficacy of Behavior-Based and Machine Learning Techniques Yousef Amro, Manar; Dweib, Dr. Mohammad Ransomware remains one of the most pervasive cybersecurity threats, exploiting both technological and human vulnerabilities to inflict severe economic and operational damage. This thesis investigates the efficacy of hybrid detection methodologies that integrate behavior-based analysis with machine learning (ML) and deep learning Long Short-Term Memory (LSTM) approaches to improve detection accuracy and generalization across diverse ransomware variants. The proposed framework unifies three behavioral dimensions—File System Monitoring (FSM), Process Behavior Analysis (PBA), and Network Behavior Analysis (NBA)—into a comprehensive dataset of 15,411 instances and 224 features, aligned through a Timestamp-Based Integration process. Multiple classifiers, including Random Forest, Naïve Bayes, Support Vector Machine (SVM), Gradient Boosting, and LSTM, were trained and evaluated. Two integration strategies—decision-level fusion (voting) and model-level stacking- were compared empirically to identify the most robust hybrid configuration. Experimental results demonstrated that the stacking ensemble [BB, XGB, NB] achieved superior macro-average performance (F1 ≈ 0.93, AUPRC ≈ 0.91), validating the advantage of multi-model learning for ransomware detection. Additionally, Synthetic Minority Over-sampling Technique (SMOTE) balancing and probability calibration improved fairness and stability across minority ransomware families such as Ryuk, Sodinokibi, and LockBit. The study also incorporated statistical validation (McNemar’s test) and sensitivity analysis to ensure the reliability of results under variable conditions. Finally, ethical and policy considerations were highlighted to guide the responsible deployment of AI-driven cybersecurity systems. This research bridges a major gap in ransomware detection studies by operationalizing a cross-domain hybrid framework that synchronizes host and network behavioral data, providing a replicable and scalable foundation for intelligent, interpretable, and ethically aligned ransomware defense systems. 2026-01-05T00:00:00Z https://dspace.qou.edu/handle/194/2957 Cybersecurity Knowledge and Skills Applied in the Palestinian Customs Police: A Case Study Shalash, Loai Basem; Awad, Dr. Waleed This study explores the cybersecurity knowledge and skills required within the Palestinian Customs Police and the extent to which these skills are applied in daily operations. The research problem stems from the growing cybersecurity threats facing law enforcement agencies and the limited studies that address the specific context of the Palestinian Customs Police. A descriptive analytical approach was adopted, and data were collected through a questionnaire administered to 146 officers and staff members across various departments. The data were analyzed using SPSS, employing statistical methods to determine the relationship between available knowledge, operational skills, and the practical implementation of cybersecurity measures. The findings indicate significant challenges, including insufficient specialized training, limited technical resources, and gaps in awareness of legal frameworks related to cybersecurity. Nevertheless, the results show notable initiatives and efforts by officers to apply cybersecurity practices despite these constraints. This study provides evidence based recommendations to strengthen training programs, improve technological resources, and develop clear policies that enhance the overall cybersecurity capacity of the Palestinian Customs Police. 2025-07-23T00:00:00Z