Publications

DSEM-NIDS: Enhanced Network Intrusion Detection System Using Deep Stacking Ensemble Model, https://doi.org/10.1109/OJCS.2025.3581036 , 2025.
Interpretable Ensemble Learning Model for Enabling an IDS to Detect DNS Attacks, doi.org/10.1007/978-3-031-82153-0_7 , 2025.
Generating Adversarial Examples Using LAD, doi.org/10.1007/978-3-031-48232-8_15 , 2023.
A Comparative Study of LAD, CNN and DNN for Detecting Intrusions, doi.org/10.1007/978-3-031-21753-1_43, 2022.
Network Security Evaluation Using Deep Neural Network, doi.org/10.23919/ICITST51030.2020.9351326, 2021.
Artificial Neural Networks for Detecting Intrusions: A Survey, doi.org/10.1109/ICRCICN50933.2020.9296181, 2020.

DSEM-NIDS: Enhanced Network Intrusion Detection System Using Deep Stacking Ensemble ModelThe need to deploy a network intrusion detection system (NIDS) is essential and has become increasingly necessary for every network, regardless whether it is wired, wireless, or hybrid, and its purpose is commercial, medical, defense, or social. Since the amount of data transfer over the Internet increases every year, using a single model as an IDS to secure the network cannot be considered enough as it may have many problems like high bias or high variance, which lead to high rates of false negatives and false positives. In this paper, we propose an ensemble learning-based NIDS (DSEM-NIDS); this system is a deep-stacking model with a nested structure that has the ability to score a high performance with low false positive and low false negative rates. Four datasets are used as a benchmark to evaluate the proposed model: The 5G-NIDD, UNR-IDD, N-BaIoT, and NSL-KDD datasets. The results show that the proposed deep stacking model is robust, has good scalability, has the ability to distinguish between classes, and has the flexibility to adapt to different input data. It also performs better than other used models.

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Interpretable Ensemble Learning Model for Enabling an IDS to Detect DNS AttacksMalicious domains pose a significant threat to internet security, with cyber-criminals exploiting the Domain Name System (DNS) to deceive users and host malicious content. The DNS services are very significant, and hence, the DNS traffic cannot be blocked. This...

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Generating Adversarial Examples Using LADNowadays, Machine learning models are widely used in many fields and employed to solve problems from different sectors. However, we often face issues when running these models in case the training data is insufficient. These issues happen when the dataset available...

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A Comparative Study of LAD, CNN and DNN for Detecting IntrusionsIn recent years, with the growth of the Internet and network devices, a significant amount of information has been exposed to the attackers and intruders. Due to vulnerabilities in the system, the adversaries plan new ways of network intrusions. Many Intrusion...

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Network Security Evaluation Using Deep Neural NetworkOne of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.

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Artificial Neural Networks for detecting Intrusions: A surveyNowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.

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