Ahmed Jamal Ibrahim

57220990795

Publications - 5

The Impact of Diverse Gateway Implementations on Mesh Network Performance

Jitka Komarkova Tawfik Al-Hadhrami Sándor Répás Ibrahim Alameri Ahmed Jamal Ibrahim Elmustafa Sayed Ali

Publication Name: Lecture Notes on Data Engineering and Communications Technologies

Publication Date: 2026-01-01

Volume: 263

Issue: Unknown

Page Range: 27-38

Description:

Wireless Mesh Networks (WMNs) provide seamless connectivity in rapidly changing and dense environments. But when gateways are integrated to provide network access from outside the current topology, several issues become problems. These include maintaining high levels of performance, energy efficiency, security awareness and interconnection. Traditional routing protocols such as Ad hoc On-Demand Distance Vector Routing (AODV) were not able to achieve these two purposes at all in a hybrid setting where efficient communication with the gateway was needed, especially when faced with high mobility, node density and changing traffic loads. This paper introduces that the problem can be resolved by implementing Fuzzy Control Energy Efficient (FCEE) routing in WMNs The FCEE routing method, characteristic innovative energy-efficient real-time failure mechanism and overall network performance enhancement for wireless mesh networks By integrating fuzzy logic into the AODV framework, the FCEE method adds a short-term memory module that optimizes packet broadcasts based on real-time levels of energy available to each node. The FCEE method not only enhances decision-making for packet forwarding but also reduces unwanted broadcasts thus extending the life of the entire network. Simulations show the FCEE performs better than traditional methods in terms of both energy efficiency, congestion control, and adaptability to dynamic situations. The proposed method thus provides an affordable way to improve the operation of wireless mesh networks, particularly in the case of high traffic areas or long periods where resources are constrained.

Open Access: Yes

DOI: 10.1007/978-3-032-01005-6_3

DDOS Attack Mitigation Based on FPGA Implementation

Sándor Répás Ahmed Jamal Ibrahim Ahmed Jamal Ibrahim

Publication Name: International Conference on Electrical Computer and Energy Technologies Icecet 2024

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

With the widespread use of computer networks daily, network security has become a significant problem in the new technology era. Due to the quick growth of the World Wide Web, it is becoming more challenging for researchers to find new methods to prevent attackers from reaching their targets. The spread of malicious cyber network activity poses a significant risk to numerous organizations and inflicts substantial economic consequences on society. Distributed Denial of Service (DDoS) is a cyberattack technique that disrupts the regular traffic of the target server or system by flooding it with an abnormal flow of internet traffic from different sources. The current article explores the advantages of FPGA-based devices in network security to mitigate the effects of DDoS attacks.

Open Access: Yes

DOI: 10.1109/ICECET61485.2024.10698517

Feature-Optimized Machine Learning Approaches for Enhanced DDoS Attack Detection and Mitigation

Sándor Répás Ahmed Jamal Ibrahim Nurullah Bektaş

Publication Name: Computers

Publication Date: 2025-11-01

Volume: 14

Issue: 11

Page Range: Unknown

Description:

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation.

Open Access: Yes

DOI: 10.3390/computers14110472

Hybrid Data-Fusion Model for Solar-Powered Smart Irrigation with Predictive Decision-Making

Alyaa Abdulhussein Al-Joda Ahmed Jamal Ibrahim Yasir Alabedi Zainulabdeen Khalaf Hashim Alyaa Abdulhussein Al-Joda Mustafa Razzaq Al-Jebory

Publication Name: Chemical Engineering Transactions

Publication Date: 2025-01-01

Volume: 121

Issue: Unknown

Page Range: 139-144

Description:

The proposed system addresses inefficient water use in agriculture by integrating an Arduino microcontroller, sensors, and data analysis. It irrigates only when needed, using weather forecasts and soil moisture sensors to optimize water delivery. The system conserves water, improves irrigation and resource management, enhances crop productivity, and reduces environmental impact. It features a user-friendly interface for remote monitoring and control and uses a Weather Forecast API to obtain real-time and forecast data from Open Weather via the REST protocol, including temperature, humidity, wind speed, and rain probability. This weather data is combined with soil moisture sensor readings in a Hybrid Fusion Algorithm, which checks both current and predicted conditions to decide when to irrigate. For instance, if the soil is dry but rain is likely, the system delays irrigation to save water. If the soil is dry and no rain is expected, irrigation starts automatically. By combining soil and weather data, the system can make more informed and sustainable decisions. In tests, this approach reduced water use by 27 % and cut unnecessary operations by 18 % compared to traditional systems. The system operates on solar energy, ensuring a sustainable and self-sufficient solution. This system represents an innovative advancement in agricultural technology, supporting more efficient and sustainable farming practices.

Open Access: Yes

DOI: 10.3303/CET25121024

FNR-IDS: A Fuzzy-Neural Hybrid with Real-Time RSA Encryption for Intelligent Intrusion Detection

Sándor Répás Ahmed Jamal Ibrahim

Publication Name: International Conference on Electrical Computer and Energy Technologies Icecet 2025

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The rising sophistication of Cybersecurity threats demands advanced Intrusion Detection Systems (IDS) capable of identifying abnormal network traffic with both accuracy and speed. This Research proposed FNR-IDS, a hybrid detection model that integrates Fuzzy Logic, Deep Neural Networks (DNNs), and RSA encryption to enhance both intrusion detection and data confidentiality. Using the UNSW-NB15 dataset, we apply Random Forest-based feature selection to extract key traffic attributes. A fuzzy inference engine evaluates the threat level and triggers RSA encryption for high-risk instances, while the DNN classifier ensures accurate detection. The integration of interpretability, learning capability, and real-time encryption addresses critical gaps in conventional IDS models. Experimental results show that FNR-IDS achieves 87% accuracy with an F1-score of 90% at the optimal threshold, confirming its effectiveness in detecting and mitigating modern cyberattacks. The proposed model of this article offers a robust, explainable, and secure framework for next-generation intrusion detection.

Open Access: Yes

DOI: 10.1109/ICECET63943.2025.11472342