Article
Computer Science, Information Systems
Azizjon Meliboev, Jumabek Alikhanov, Wooseong Kim
Summary: In the modern era, studying Intrusion Detection Systems (IDS) is crucial for ensuring network security. Deep Learning (DL) is an essential tool for solving complex system problems. This work proposes an effective and adaptive IDS using DL methods, specifically utilizing architectures such as CNN, LSTM, RNN, and GRU. The experiments demonstrate that CNN and LSTM combination models outperform other models.
Article
Computer Science, Interdisciplinary Applications
Isra Al-Turaiki, Najwa Altwaijry
Summary: Cybersecurity is crucial for protecting and recovering computer systems and networks from cyber attacks as people rely more on technology. This article introduces two deep learning models using convolutional neural network architecture to classify network attacks, along with a hybrid two-step preprocessing approach. The models outperform similar approaches in terms of accuracy and recall, as shown in experimental results using benchmark datasets.
Article
Computer Science, Hardware & Architecture
Houda Jmila, Mohamed Ibn Khedher
Summary: Intrusion detection is a key topic in cybersecurity, and machine learning is widely used in this field. This paper investigates the robustness of shallow machine learning-based intrusion detection systems against adversarial attacks, and evaluates the performance of different classifiers under different attacks.
Article
Automation & Control Systems
Oscar Mogollon-Gutierrez, Jose Carlos Sancho Nunez, Mar Avila Vegas, Andres Caro Lindo
Summary: This article presents a novel perspective on using artificial intelligence to ensure cybersecurity through the study of network traffic. The proposed system constructs a two-stage cyberattack classification ensemble model to address class imbalance and achieve complete multiclass classification of network traffic.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2023)
Article
Telecommunications
Lalit Kumar Vashishtha, Akhil Pratap Singh, Kakali Chatterjee
Summary: The cloud computing model is widely popular, but security is a major concern. This research introduces a hybrid intrusion detection model for cloud based systems, combining signature-based detection and anomaly-based detection to detect known and unknown attacks. The experiments show high detection rates for the proposed model compared to existing models.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Mathematics
Iftikhar Ahmad, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, Rayed A. AlGhamdi
Summary: Intrusion detection in computer networks is important for communication and security domains, but remains a challenging task. This paper compares multiple techniques to develop a network intrusion detection system and proposes an AdaBoost-based approach. Experimental results show that the proposed method effectively detects different forms of network intrusions and achieves 99.3% accuracy on the UNSW-NB15 dataset.
Article
Computer Science, Artificial Intelligence
P. Rajesh Kanna, P. Santhi
Summary: Recent advancements in information and communication technologies have led to a growing number of online systems and services. Therefore, it is necessary to design advanced and intelligent IDS models to ensure the trustworthiness of these systems. However, most existing IDS models based on traditional machine learning algorithms lack efficient feature selection and classification performance for new attacks. Additionally, they struggle with the recognition of known attacks and handling massive amounts of network traffic data. To address these issues, this paper presents an efficient hybrid IDS model built using the BWO-CONV-LSTM network. The model incorporates feature selection by the ABC algorithm and a hybrid deep learning classifier on a MapReduce framework. Performance evaluations demonstrate high intrusion detection accuracy and improved classification coefficients.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Imran, Faisal Jamil, Dohyeun Kim
Summary: The article discusses an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Through performance analysis of the UNSW-NB15 and CICIDS2017 datasets, the proposed model-based intrusion detection accuracy is 98.801 percent for the UNSW-NB15 dataset and 97.02 percent for the CICIDS2017 dataset, showing significant improvement in intrusion detection accuracy with the proposed ensemble model.
Article
Computer Science, Information Systems
Amir Basati, Mohammad Mehdi Faghih
Summary: This paper presents a new and lightweight architecture for intrusion detection in IoT devices based on Parallel Deep Auto-Encoder (PDAE). By separating features using local and surrounding information, the accuracy of the model is improved while reducing the number of parameters and resource requirements. The effectiveness of the proposed model is evaluated and it outperforms state-of-the-art algorithms in terms of both accuracy and performance.
INFORMATION SCIENCES
(2022)
Review
Chemistry, Multidisciplinary
Pierpaolo Dini, Abdussalam Elhanashi, Andrea Begni, Sergio Saponara, Qinghe Zheng, Kaouther Gasmi
Summary: The Intrusion Detection System (IDS) is an effective tool used in cybersecurity systems to detect and identify intrusion attacks. Feature selection is crucial to enhance performance, and the structure and balance of the dataset can impact the efficiency of the machine learning model. This research aims to explore ML approaches for IDS, focusing on datasets, machine algorithms, and metrics.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Yanfang Fu, Yishuai Du, Zijian Cao, Qiang Li, Wei Xiang
Summary: The paper proposes a deep learning model for network intrusion detection (DLNID) that combines attention mechanism and Bi-LSTM network, with CNN for feature extraction and ADASYN for data imbalance. The model achieved high accuracy and Fl score on a public benchmark dataset, outperforming other comparison methods.
Article
Computer Science, Information Systems
Mohammed Amin Almaiah, Omar Almomani, Adeeb Alsaaidah, Shaha Al-Otaibi, Nabeel Bani-Hani, Ahmad K. Al Hwaitat, Ali Al-Zahrani, Abdalwali Lutfi, Ali Bani Awad, Theyazn H. H. Aldhyani
Summary: This paper presents a research model for an intrusion detection system based on Principal Component Analysis feature selection technique and different Support Vector Machine kernel classifiers. The impact of various kernel functions in SVM is investigated, and the performance of the investigation model is evaluated using multiple metrics. The results show that the Gaussian radial basis function kernel outperforms other kernels in terms of accuracy, sensitivity, and F-measure on both datasets.
Article
Computer Science, Artificial Intelligence
Amir Basati, Mohammad Mehdi Faghih
Summary: The use of IoT has increased significantly in recent years, making real-time cyber-threat protection crucial. However, current IoT devices are often lacking security features and are vulnerable to attacks. Therefore, it is important to develop tools for real-time attack detection in IoT networks. This paper proposes a new intelligent network intrusion detection system called APAE, which utilizes an asymmetric parallel auto-encoder to effectively detect various attacks in IoT networks.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Ketan Kotecha, Raghav Verma, Prahalad Rao, Priyanshu Prasad, Vipul Kumar Mishra, Tapas Badal, Divyansh Jain, Deepak Garg, Shakti Sharma
Summary: In order to predict anomalies more accurately, a reasonably good network intrusion detection system requires high detection rate and low false alarm rate. This paper operates on the UNSW-NB15 Dataset to suggest various models for modern attacks. Alongside detailed modeling, comprehensive data analysis on dataset features is done for better modeling. Moreover, hypothetical ponderings on potential network intrusion detection systems including suggestions on prospective modeling and dataset generation are discussed.
Article
Chemistry, Multidisciplinary
Yi Liu, Lanjian Wu
Summary: This paper proposes an enhanced Transformer-based intrusion detection model to address the challenges of lengthy training time, inaccurate detection of overlapping classes, and poor performance in multi-class classification. The proposed model includes a data processing strategy to reduce dimension and balance the dataset, an improved position encoding method for better feature dependency learning, and a two-stage learning strategy for improved accuracy in multi-class classification. Experimental results show that the proposed model achieves higher accuracy and F1-score compared to existing models.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Hadeel Alazzam, Esraa Alhenawi, Rizik Al-Sayyed
JOURNAL OF SUPERCOMPUTING
(2019)
Article
Computer Science, Artificial Intelligence
Hadeel Alazzam, Ahmad Sharieh, Khair Eddin Sabri
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Hardware & Architecture
Hadeel Alazzam, Orieb AbuAlghanam, Ahmad Sharieh
Summary: The pathfinding problem is widely used in various applications and virtual environments, with different goals such as finding the shortest, safest, or optimal path. It involves a large amount of data and depends on the definition of the best path. This paper introduces a parallel A* algorithm using Apache Spark to find the optimal path, evaluated in terms of runtime, efficiency, and cost on datasets of different sizes.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Hadeel Alazzam, Aryaf Al-Adwan, Orieb Abualghanam, Esra'a Alhenawi, Abdulsalam Alsmady
Summary: In this study, a wrapper-based approach for Android malware detection is proposed. By using a new optimizer and classifier, the proposed approach achieves high accuracy and F1 score. It outperforms related approaches in terms of accuracy, precision, and recall.
Article
Computer Science, Information Systems
Orieb Abualghanam, Hadeel Alazzam, Basima Elshqeirat, Mohammad Qatawneh, Mohammed Amin Almaiah
Summary: This study proposes a hybrid DNS tunneling detection system based on packet length and selected features. Experimental results show that the proposed system achieved 98.3% accuracy and a 97.6% F-score in DNS tunneling datasets, outperforming other related techniques. Moreover, including packet length in the hybrid approach improves runtime performance compared to using Tabu-PIO.
Article
Computer Science, Information Systems
Rizik Al-Sayyed, Esra'a Alhenawi, Hadeel Alazzam, Ala'a Wrikat, Dima Suleiman
Summary: Financial investigations in fraud detection require rigorous data analysis. This paper highlights the importance of data visualization in conducting initial assessments and promptly detecting unexpected patterns. Through analysis of the PAYSIM dataset, we demonstrate how visualization can identify compatibility issues and emphasize key findings. Visual analysis is essential in detecting fraudulent activities and improving the accuracy of detection systems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Theory & Methods
Hadeel Alazzam, Abdulsalam Alsmady, Wail Mardini
2020 11TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS)
(2020)
Proceedings Paper
Computer Science, Information Systems
Inas Abuqaddom, Hadeel Alazzam, Amjad Hudaib, Fawaz Al-Zaghoul
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS)
(2019)
Proceedings Paper
Computer Science, Information Systems
Hadeel Alazzam, Wesam Almobaideen
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS)
(2019)
Proceedings Paper
Computer Science, Information Systems
Abdulsalam Alsmady, Tareq Al-Khraishi, Wail Mardini, Hadeel Alazzam, Yaser Khamayseh
2019 IEEE JORDAN INTERNATIONAL JOINT CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY (JEEIT)
(2019)
Proceedings Paper
Computer Science, Hardware & Architecture
Hadeel Alazzam, Abdulsalam Alsmady
PROCEEDINGS OF THE 2017 12TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT 2017), VOL. 1
(2017)
Article
Computer Science, Information Systems
Sherin Hijazi, Nadim Obeid, Khair Eddin Sabri
