Article
Multidisciplinary Sciences
Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi
Summary: The paper introduces an intelligent cyber attack detection system for IoT networks, utilizing a novel feature reduction approach to improve detection efficiency. Experimental comparisons demonstrate the model's high accuracy and detection rate in cyber attack detection.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
Seshu Bhavani Mallampati, Seetha Hari
Summary: Expanding internet-connected services have led to an increase in cyberattacks, making it important to develop a fusion-based feature importance method for network protection. This method uses various preprocessing techniques to improve training data quality and ranks each feature using different methods. It achieves high accuracy and requires fewer features, as demonstrated by the experimental results.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Review
Mathematics
Ranjit Panigrahi, Samarjeet Borah, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Moumita Pramanik, Rutvij H. Jhaveri, Chiranji Lal Chowdhary
Summary: This research analyzed the current literature status in the field of network intrusion detection, exploring the performance of various classifiers and proposing J48Consolidated as the ideal classifier for designing IDSs.
Article
Mathematics
Ranjit Panigrahi, Samarjeet Borah, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Moumita Pramanik, Yogesh Kumar, Rutvij H. Jhaveri
Summary: This paper introduces a host-based intrusion detection system using a C4.5-based detector, with improved random sampling and multi-class feature selection mechanisms to efficiently handle class-imbalanced data. The proposed system achieves an accuracy of 99.96% and 99.95% on two popular datasets.
Article
Computer Science, Information Systems
Murtaza Ahmed Siddiqi, Wooguil Pak
Summary: This study proposes an optimal framework for a network intrusion detection system based on image processing, which enhances network security efficiency through steps such as feature selection, image transformation, and enhancement.
Article
Computer Science, Hardware & Architecture
Shie-Yuan Wang, Jen-Chieh Chang
Summary: An intrusion detection system (IDS) is essential for network security and traditionally implemented as a user space program running on a hardware server. With the availability of Extended BPF (eBPF) in the Linux kernel, efficiently checking and filtering arriving packets directly in the kernel has become possible. This work presents an IDS that utilizes eBPF for fast pattern matching and demonstrates higher throughput compared to Snort.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Telecommunications
Mohit Nagpal, Manisha Kaushal, Akashdeep Sharma
Summary: The paper introduces a feature-reduced intrusion detection system with optimized SVM as a classifier and utilizes the BBBC optimization algorithm to find optimal parameters by simulating the big bang and big crunch theory of universe evolution. Additionally, a new fitness function based on weighted F1 score is proposed for evaluating the IDS performance. The study also investigates the effects of under-sampling and oversampling on various traffic classes in the IDS performance.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Engineering, Civil
Seyoung Lee, Wonsuk Choi, Hyo Jin Jo, Dong Hoon Lee
Summary: This study proposes a novel automotive intrusion detection system that leverages residuals to detect vehicle intrusions, including sophisticated attacks that emulate clock skew. It also enables the sharing of parameters between vehicle models, enhancing scalability.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wathiq Laftah Al-Yaseen, Ali Kadhum Idrees, Faezah Hamad Almasoudy
Summary: This paper proposes an efficient feature selection method for intrusion detection systems to reduce redundant and irrelevant features, improve system performance, and decrease processing time. The experiments demonstrate that the proposed method significantly enhances the accuracy and efficiency of IDS.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Shruti Patil, Vijayakumar Varadarajan, Siddiqui Mohd Mazhar, Abdulwodood Sahibzada, Nihal Ahmed, Onkar Sinha, Satish Kumar, Kailash Shaw, Ketan Kotecha
Summary: This paper introduces the importance of intrusion detection systems in cybersecurity and proposes an innovative intrusion detection system using machine learning methods. By selecting appropriate features and applying ensemble technique voting classifier, this system improves accuracy and resolves false positives. Moreover, the system incorporates the XAI algorithm LIME for better explanation and understanding of the intrusion detection process.
Article
Computer Science, Information Systems
Yijie Xun, Yilin Zhao, Jiajia Liu
Summary: Intelligent and connected vehicles have become mainstream in the automobile industry, but some communication interfaces used in these vehicles are vulnerable to attacks. This article introduces VehicleEIDS, an intrusion detection system based on vehicle voltage signals, which can monitor message transmission in real time and protect the security of the CAN bus.
IEEE INTERNET OF THINGS JOURNAL
(2022)
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
Abubaker Jumaah Rabash, Mohd Zakree Ahmad Nazri, Azrulhizam Shapii, Mohammad Kamrul Hasan
Summary: This paper proposes a novel framework for dynamic feature selection in intrusion detection system using multi-objective optimization based meta-heuristic searching algorithms. The proposed framework demonstrates superiority over existing benchmarking algorithms in terms of accuracy and F-measure.
Article
Computer Science, Information Systems
Deris Stiawan, Ahmad Heryanto, Ali Bardadi, Dian Palupi Rini, Imam Much Ibnu Subroto, Kurniabudi, Mohd Yazid Bin Idris, Abdul Hanan Abdullah, Bedine Kerim, Rahmat Budiarto
Summary: This study aims to find the best relevant selected features for intrusion detection system by using six feature selection methods, combining them with four classification methods to generate optimized ensemble IDSs, and evaluating them through various validation approaches to improve performance.
Article
Computer Science, Hardware & Architecture
Tianhao Hou, Hongyan Xing, Xinyi Liang, Xin Su, Zenghui Wang
Summary: This paper proposes a method called DNA-Spatial Information (DNA-SI) for detecting illegal network behavior through network traffic analysis. Experimental results demonstrate the superiority and robustness of the DNA-SI method.
Article
Computer Science, Information Systems
Ahmed Barnawi, Prateek Chhikara, Rajkumar Tekchandani, Neeraj Kumar, Mehrez Boulares
Summary: This paper presents the applications of unmanned air vehicles (UAVs) in the medical field, particularly in tackling the COVID-19 outbreak by delivering medication and emergency kits to hospitals. It also proposes a deep convolution neural architecture for detecting COVID-19 cases and compares its performance with state-of-the-art models.
MULTIMEDIA SYSTEMS
(2023)
Article
Computer Science, Information Systems
J. Senthil Kumar, Akhil Gupta, Sudeep Tanwar, Neeraj Kumar, Sedat Akleylek
Summary: This paper thoroughly investigates the security aspects in vehicle-to-vehicle communication with the backbone cellular network, using a device-to-device communication link that shares spectral resources. The study derives the ergodic secrecy capacity and ergodic capacity for the wiretap channel and D2D link, and emphasizes the importance of optimizing power allocation and security through D2D communications for V2V applications.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Sandhya Sharma, Sheifali Gupta, Neeraj Kumar, Tanvi Arora
Summary: The postal automation system is a major research area in the era of automation. Developing a postal automation system for India is challenging due to its multi-script and multi-lingual behavior. This study focuses on the postal automation of district names in Punjab, India, written in the Gurmukhi script. A segmentation-free technique using Convolutional Neural Network (CNN) and Deep learning (DL) is utilized for recognition. A database of 22000 handwritten images in Gurmukhi script for all 22 districts of Punjab is prepared, and two CNN models achieve validation accuracies of 90% and 98% respectively.
INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS
(2023)
Review
Computer Science, Theory & Methods
Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, Pekka Marttinen
Summary: Emotion recognition technology using EEG signals is crucial in Artificial Intelligence, with applications in emotional health care, human-computer interaction, and multimedia content recommendation. This paper reviews recent representative works in EEG-based emotion recognition research from the perspective of researchers taking the first step in this field. It introduces the scientific basis of EEG-based emotion recognition and categorizes reviewed works into different technical routes, providing readers with a better understanding of the motivation behind these studies. The paper also discusses existing challenges and future research directions.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Hardware & Architecture
Nilesh Kumar Jadav, Tejal Rathod, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Ahmed Alkhayyat
Summary: →Massive population growth and rising environmental issues pose challenges in agriculture, such as land scarcity, pesticide overuse, and global food demand. To tackle this, we proposed a blockchain and AI-powered smart agriculture framework to predict pesticide levels in crops. The blockchain ensures data integrity, storing records securely. Evaluation metrics show that our framework outperforms baseline approaches in accuracy, scalability, and latency.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Review
Computer Science, Theory & Methods
Sudeep Tanwar, Dakshita Ribadiya, Pronaya Bhattacharya, Anuja R. Nair, Neeraj Kumar, Minho Jo
Summary: Scientific publishing systems (SPS) provide a platform for authors, reviewers, and editors to share their research, leading to the advancement of the academic community. However, traditional SPS face challenges in managing large databases, complex queries and retrievals, lengthy publishing processes, and lack of rewarding methods for peer review and storage of unsuccessful articles. In this paper, a fusion of blockchain and IoT technologies is proposed to address these limitations and provide a secure, transparent, and efficient publishing platform.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Arun Singh Bhadwal, Kamal Kumar, Neeraj Kumar
Summary: This paper proposes a new representation called GenSMILES to overcome the limitations of SMILES representation and improve the validity and diversity of generated molecules. GenSMILES relies on derivative rules to address syntactical and semantic issues, and it allows for more efficient generation of valid molecules.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Disha Deotale, Madhushi Verma, P. Suresh, Neeraj Kumar
Summary: This study focuses on physiotherapy video dataset and proposes a deep learning-based neural network framework to address the issues in continuous human activity recognition.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ahmed Barnawi, Shivani Gaba, Anna Alphy, Abdoh Jabbari, Ishan Budhiraja, Vimal Kumar, Neeraj Kumar
Summary: This paper aims to enhance the security of IoT systems by exploring deep learning algorithms. It identifies and evaluates potential security threats and attack surfaces for IoT, and provides a systematic survey of deep learning methods for IoT security. This research opens the door for future studies by highlighting the advantages, disadvantages, and opportunities in this field.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Computer Science, Information Systems
Muhammad Asghar Khan, Neeraj Kumar, Syed Agha Hassnain Mohsan, Wali Ullah Khan, Moustafa M. Nasralla, Mohammed H. Alsharif, Justyna Zywiolek, Insaf Ullah
Summary: Fifth-generation (5G) cellular networks have led to beyond 5G (B5G) networks that can incorporate autonomous services into swarms of unmanned aerial vehicles (UAVs). These networks provide capacity expansion strategies to address massive connectivity issues and ensure high throughput and low latency in extreme or emergency situations. On the other hand, 6G technology integrates AI/ML, IoT, and blockchain to establish reliable, intelligent, secure, and ubiquitous UAV networks, but also poses new challenges for swarm UAVs due to new enabling technologies and unique network design.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Peiying Zhang, Yi Zhang, Neeraj Kumar, Mohsen Guizani
Summary: This study proposes a service provision method based on service function chaining (SFC) to address the resource allocation challenge in the space-air-ground-integrated network (SAGIN). By using network function virtualization (NFV) and a federated learning (FL)-based algorithm, efficient resource allocation and reduced service blocking rate can be achieved.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Iram Bibi, Adnan Akhunzada, Neeraj Kumar
Summary: Distributed Industrial Internet of Things (IIoT) has revolutionized the industrial sector, but threat hunting and intelligence in distributed IIoT is complex due to lack of standard architectures. The authors propose a self-learning multivector threat intelligence and detection mechanism to defend IIoT systems. They introduce a novel ConvLSTM2D mechanism that can efficiently tackle dynamic variants of emerging IIoT threats. The proposed mechanism outperforms benchmark algorithms in detection accuracy with minimal tradeoff in speed efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Abuzar B. M. Adam, Xiaoyu Wan, Mohammed A. M. Elhassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Neeraj Kumar, Mohsen Guizani
Summary: In this paper, the authors investigate UAV-aided RIS communication for next generation communication networks, focusing on optimizing active beamforming, passive beamforming, and UAV trajectory to minimize power consumption. They propose a hybrid semi-unfolding deep neural network (HSUDNN) to handle the constraints during channel state information gain, and apply inception-like multi-kernel convolutional long short-term memory (IL-MK-CLSTM) sub networks to handle UAV trajectory and passive beamforming. The proposed HSUDNN achieves superior performance compared to existing techniques.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Himanshu Sharma, Neeraj Kumar, Ishan Budhiraja, Ahmed Barnawi
Summary: This article proposes a scheme to optimize the secrecy rate of terahertz-enabled femtocells. By utilizing deep reinforcement learning techniques, the optimization problem is successfully addressed, and two schemes are presented to maximize the secrecy rate of the femtocells. Simulation results demonstrate significant improvements in secrecy rate, SINR, and energy-efficiency for the proposed schemes.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Vineet Vishnoi, Ishan Budhiraja, Suneet Gupta, Neeraj Kumar
Summary: Device-to-device (D2D) communication is an emerging technology in 5G and upcoming 6G networks that enhances the overall transmission rate. However, interference and connectivity issues pose challenges. To mitigate these issues, researchers integrated power domain non-orthogonal multiple access techniques (PD-NOMA) on base stations (BS). By reducing interference and optimizing power allocation, the proposed solution improves sum rate and fairness among users.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
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)