Article
Computer Science, Information Systems
Moutaz Alazab
Summary: This research proposes a new system for discovering botnet attacks in the IoT context using a wrapper feature selection technique based on the Gray Wolves Optimization algorithm. By using time-variant transfer functions, the algorithm is able to switch between global and local searches, improving optimization performance. Experimental results show that this method can effectively find the best feature subset within a reasonable running time, making it suitable for integration into IoT networks as an intrusion detection algorithm.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Yassine Akhiat, Kaouthar Touchanti, Ahmed Zinedine, Mohamed Chahhou
Summary: With the rapid expansion of networks in various areas, network intrusions have increased. Intrusion detection systems (IDS) have been developed to deal with these harmful activities. However, the complexity of network data poses challenges for machine learning and data mining tools.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Hadeel Alazzam, Ahmad Sharieh, Khair Eddin Sabri
Summary: This paper introduces an intelligent lightweight IDS with a low false alarm rate while maintaining a high detection rate. The proposed system is a fusion of two main subsystems that work in parallel, each trained on normal packets and attack packets, with the results combined to provide judgments for each packet passing through the network.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Sydney Mambwe Kasongo
Summary: In recent years, advances in technologies such as cloud computing, vehicular networks systems, and the Internet of Things (IoT) have led to a spike in the amount of information transmitted through communication infrastructures. Consequently, attackers have increased their efforts to exploit vulnerabilities in network systems. Therefore, it is crucial to enhance the security of these network systems. This study implements an IDS framework using Machine Learning techniques and evaluates its performance using benchmark datasets.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Bayi Xu, Lei Sun, Xiuqing Mao, Ruiyang Ding, Chengwei Liu
Summary: In this study, a novel intrusion detection system is proposed to efficiently detect network anomalous traffic in IoT devices using machine learning techniques. By employing feature selection and data oversampling, the detection performance of the model is optimized, and the effectiveness of the proposed method is validated through experiments.
Article
Computer Science, Artificial Intelligence
S. Priya, K. Pradeep Mohan Kumar
Summary: The new generation of IDS requires automatic and intelligent network ID strategies to manage security risks posed by advanced attackers. The need for an autonomous IDS solution with minimal human intervention, but capable of effectively handling new threats, is increasing. DRL methods have been developed to address the limitations of existing RL methods. This article introduces a BBAFS-DRL system for IDSs, which combines the Binary Bat Algorithm-based Feature Selection and Deep Reinforcement Learning. Experimental analysis shows that the BBAFS-DRL methodology outperforms existing approaches.
Article
Computer Science, Information Systems
Shubhra Dwivedi, Manu Vardhan, Sarsij Tripathi
Summary: The EFSGOA method, a combination of ensemble feature selection and grasshopper optimization algorithm, achieved excellent performance in intrusion detection, with high detection rates, accuracy, and low false alarm rates. The method significantly improved accuracy and reduced false alarms, outperforming other existing techniques.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Mathematics
Rajakumar Ramalingam, Dinesh Karunanidy, Sultan S. Alshamrani, Mamoon Rashid, Swamidoss Mathumohan, Ankur Dumka
Summary: This paper proposes an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm to solve the Economic Load Dispatch (ELD) problem, by employing Oppositional-Based Learning (OBL) to improve the quality of solutions and global search capability. The experimental results demonstrate that the OPIO algorithm outperforms the conventional PIO algorithm and other state-of-the-art approaches in terms of accuracy, convergence rate, computation time, and fuel cost.
Article
Computer Science, Artificial Intelligence
Vitali Herrera-Semenets, Lazaro Bustio-Martinez, Raudel Hernandez-Leon, Jan van den Berg
Summary: The research proposed a novel feature selection algorithm for intrusion detection scenarios, which reduces the dimensionality of the training data set by using qualitative information provided by multiple feature selection measures, achieving greater efficacy than other feature selection algorithms for intrusion detection purposes. Future research should continue to improve the algorithm.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
K. Narayana Rao, K. Venkata Rao, P. V. G. D. Prasad Reddy
Summary: The study found that machine learning has shown good results in intrusion detection systems. The two-stage hybrid methodology proposed by the authors significantly improves the detection of attacks, especially achieving excellent accuracy and detection rates on the UNSW-NB15 dataset.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Chandan Kumar, Soham Biswas, Md. Sarfaraj Alam Ansari, Mahesh Chandra Govil
Summary: Software Defined Networks (SDN) is a new networking architecture with centralized management and high efficiency. However, security measures are still a challenge. By employing network intrusion detection systems and machine learning techniques for feature selection, the security and performance of SDN can be enhanced.
COMPUTERS & SECURITY
(2023)
Article
Chemistry, Analytical
Abdulaziz Fatani, Abdelghani Dahou, Mohammed A. A. Al-qaness, Songfeng Lu, Mohamed Abd Elaziz
Summary: In this study, a new intrusion detection system was developed utilizing swarm intelligence algorithms for feature extraction and selection. The system employed neural networks and the Aquila optimizer for this purpose. Performance evaluation on four public datasets demonstrated the competitive nature of the developed approach.
Review
Computer Science, Artificial Intelligence
Javier Maldonado, Maria Cristina Riff, Bertrand Neveu
Summary: This paper presents a review of recent advances in wrapper feature selection techniques in the field of intrusion detection. It is difficult to determine the current level of research in this area due to the large number of published papers. By providing a classification taxonomy, evaluation metrics, and discussion on attack scenarios, this paper offers a comprehensive overview of the existing research, as well as identifies open challenges and new directions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Kezhou Ren, Yifan Zeng, Zhiqin Cao, Yingchao Zhang
Summary: This paper presents a network intrusion detection model based on RFE feature extraction and deep reinforcement learning. The model improves the efficacy of intrusion detection systems through feature selection and deep reinforcement learning, and demonstrates good performance in experiments.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Hardware & Architecture
Heather Lawrence, Uchenna Ezeobi, Orly Tauil, Jacob Nosal, Owen Redwood, Yanyan Zhuang, Gedare Bloom
Summary: This article introduces the CUPID dataset, which aims to address the limitations of existing datasets in network intrusion detection research. The CUPID dataset includes human-generated traffic with accurate labels, providing a valuable resource for training and testing machine learning algorithms used in network intrusion detection systems.
JOURNAL OF SYSTEMS ARCHITECTURE
(2022)
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
Summary: This paper introduces an intelligent lightweight IDS with a low false alarm rate while maintaining a high detection rate. The proposed system is a fusion of two main subsystems that work in parallel, each trained on normal packets and attack packets, with the results combined to provide judgments for each packet passing through the network.
APPLIED INTELLIGENCE
(2022)
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
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)