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Alshahrani H, Anjum M, Shahab S, Al Reshan MS, Sulaiman A, Shaikh A. Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT. Sci Rep 2024; 14:18075. [PMID: 39103381 DOI: 10.1038/s41598-024-67956-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 07/17/2024] [Indexed: 08/07/2024] Open
Abstract
The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder's activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method's consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.
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Affiliation(s)
- Hani Alshahrani
- Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
- Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Mohd Anjum
- Department of Computer Engineering, Aligarh Muslim University, Aligarh, 202002, India
| | - Sana Shahab
- Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, PO Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mana Saleh Al Reshan
- Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
- Department Information Systems, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Adel Sulaiman
- Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.
- Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.
| | - Asadullah Shaikh
- Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
- Department Information Systems, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
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Hu G, Zheng Y, Houssein EH, Wei G. DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation. Comput Biol Med 2024; 178:108780. [PMID: 38909447 DOI: 10.1016/j.compbiomed.2024.108780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/06/2024] [Accepted: 06/15/2024] [Indexed: 06/25/2024]
Abstract
Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.
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Affiliation(s)
- Gang Hu
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China.
| | - Yixuan Zheng
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Guo Wei
- University of North Carolina at Pembroke, Pembroke, NC, 28372, USA
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3
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Zhao D, Wang Z, Chen Y, Wei G, Sheng W. Partial-Neurons-Based Proportional-Integral Observer Design for Artificial Neural Networks: A Multiple Description Encoding Scheme. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:6393-6407. [PMID: 36197865 DOI: 10.1109/tnnls.2022.3209632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This article is concerned with a new partial-neurons-based proportional-integral observer (PIO) design problem for a class of artificial neural networks (ANNs) subject to bounded disturbances. For the purpose of improving the reliability of the data transmission, the multiple description encoding mechanisms are exploited to encode the measurement data into two identically important descriptions, and the encoded data are then transmitted to the decoders via two individual communication channels susceptible to packet dropouts, where Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the packet dropouts. An explicit relationship is discovered that quantifies the influences of the packet dropouts on the decoding accuracy, and a sufficient condition is provided to assess the boundedness of the estimation error dynamics. Furthermore, the desired PIO parameters are calculated by solving two optimization problems based on two metrics (i.e., the smallest ultimate bound and the fastest decay rate) characterizing the estimation performance. Finally, the applicability and advantage of the proposed PIO design strategy are verified by means of an illustrative example.
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4
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Vijayan PM, Sundar S. An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network. PLoS One 2023; 18:e0291872. [PMID: 37792753 PMCID: PMC10550182 DOI: 10.1371/journal.pone.0291872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/06/2023] Open
Abstract
The IoT offered an enormous number of services with the help of multiple applications so it faces various security-related problems and also heavy malicious attacks. Initially, the IoT data are gathered from the standard dataset as Message Queuing Telemetry Transport (MQTT) set. Further, the collected data are undergone the pre-processing stage, which is accomplished by using data cleaning and data transformation. The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA). Further, the optimal features are subjected to the AL-DBN model, where the first classified outcomes are obtained with the parameter optimization of MAOA. Similarly, (ii) Long Short-Term Memory (LSTM) with DBN, in this model, the optimal features are chosen from LSTM with the aid of MAOA. Consequently, the optimal features are subjected into the AL-DBN model, where the second classified outcomes are acquired. Finally, the average score is estimated by two outcomes to provide the final classified result. Thus, the findings reveal that the suggested system achieves outstanding results to detect the attack significantly.
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Affiliation(s)
- P. M. Vijayan
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nādu, India
| | - S. Sundar
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nādu, India
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5
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Mahalingam A, Perumal G, Subburayalu G, Albathan M, Altameem A, Almakki RS, Hussain A, Abbas Q. ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:8044. [PMID: 37836874 PMCID: PMC10575244 DOI: 10.3390/s23198044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023]
Abstract
The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.
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Affiliation(s)
- Anandaraj Mahalingam
- Department of Information Technology, PSNA College of Engineering and Technology, Dindigul 624622, Tamil Nadu, India
| | - Ganeshkumar Perumal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (M.A.); (A.A.); (R.S.A.)
| | - Gopalakrishnan Subburayalu
- Department of Information Technology, Hindustan Institute of Technology and Science, Chennai 603103, Tamil Nadu, India
| | - Mubarak Albathan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (M.A.); (A.A.); (R.S.A.)
| | - Abdullah Altameem
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (M.A.); (A.A.); (R.S.A.)
| | - Riyad Saleh Almakki
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (M.A.); (A.A.); (R.S.A.)
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (M.A.); (A.A.); (R.S.A.)
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6
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Khan NW, Alshehri MS, Khan MA, Almakdi S, Moradpoor N, Alazeb A, Ullah S, Naz N, Ahmad J. A hybrid deep learning-based intrusion detection system for IoT networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13491-13520. [PMID: 37679099 DOI: 10.3934/mbe.2023602] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.
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Affiliation(s)
- Noor Wali Khan
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Mohammed S Alshehri
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Muazzam A Khan
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
- ICESCO Chair Big Data Analytics and Edge Computing, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Naghmeh Moradpoor
- School of Computing, Engineering & The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
| | - Abdulwahab Alazeb
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Safi Ullah
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Naila Naz
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Jawad Ahmad
- School of Computing, Engineering & The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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7
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Alsubai S, Dutta AK, Alnajim AM, Wahab Sait AR, Ayub R, AlShehri AM, Ahmad N. Artificial intelligence-driven malware detection framework for internet of things environment. PeerJ Comput Sci 2023; 9:e1366. [PMID: 37346520 PMCID: PMC10280412 DOI: 10.7717/peerj-cs.1366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/04/2023] [Indexed: 06/23/2023]
Abstract
The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework's performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study's outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.
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Affiliation(s)
- Shtwai Alsubai
- Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Technology, Almaarefa University, Riyadh, Kingdom of Saudi Arabia
| | - Abdullah M. Alnajim
- Department of Information Technology, College of computer, Qassim University, Buraydah, Saudi Arabia
| | - Abdul rahaman Wahab Sait
- Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, Kingdom of Saudi Arabia
| | - Rashid Ayub
- Department of Science Technology & Innovation Unit, King Saud University, Riyadh, Saudi Arabia
| | - Afnan Mushabbab AlShehri
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Kingdom of Saudi Arabia
| | - Naved Ahmad
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Kingdom of Saudi Arabia
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8
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Yue Y, Cao L, Lu D, Hu Z, Xu M, Wang S, Li B, Ding H. Review and empirical analysis of sparrow search algorithm. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10435-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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9
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Qiu Y, Wu Z, Wang J, Zhang C, Zhang H. Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1906. [PMID: 36903027 PMCID: PMC10004319 DOI: 10.3390/ma16051906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Traditional research and development (R&D) on biomedical materials depends heavily on the trial and error process, thereby leading to huge economic and time burden. Most recently, materials genome technology (MGT) has been recognized as an effective approach to addressing this problem. In this paper, the basic concepts involved in the MGT are introduced, and the applications of MGT in the R&D of metallic, inorganic non-metallic, polymeric, and composite biomedical materials are summarized; in view of the existing limitations of MGT for R&D of biomedical materials, potential strategies are proposed on the establishment and management of material databases, the upgrading of high-throughput experimental technology, the construction of data mining prediction platforms, and the training of relevant materials talents. In the end, future trend of MGT for R&D of biomedical materials is proposed.
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Affiliation(s)
| | | | | | - Chao Zhang
- Correspondence: (C.Z.); (H.Z.); Tel.: +86-20-39332145 (C.Z. & H.Z.)
| | - Heye Zhang
- Correspondence: (C.Z.); (H.Z.); Tel.: +86-20-39332145 (C.Z. & H.Z.)
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10
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Ma W, Liu R, Li K, Yan S, Guo J. An adversarial domain adaptation approach combining dual domain pairing strategy for IoT intrusion detection under few-shot samples. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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11
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Abd Elaziz M, Al-qaness MA, Dahou A, Ibrahim RA, El-Latif AAA. Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm. ADVANCES IN ENGINEERING SOFTWARE 2023; 176:103402. [DOI: 10.1016/j.advengsoft.2022.103402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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12
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Lin S, Zhang K, Guan D, He L, Chen Y. An intrusion detection method based on granular autoencoders. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Intrusion detection systems have become one of the important tools for network security due to the frequent attacks brought about by the explosive growth of network traffic. Autoencoder is an unsupervised learning model with a neural network structure. It has a powerful feature learning capability and is effective in intrusion detection. However, its network construction suffers from overfitting and gradient disappearance problems. Traditional granular computing methods have advantages in solving such problems, but the process is relatively complex, the granularity dimension is high, and the computational cost is large, which is not suitable for application in intrusion detection systems. To address these problems, we propose a novel autoencoder: Granular AutoEncoders (GAE). The granulation reference set is constructed by random sampling. The granulation of training samples is based on single-feature similarity in a reference set to form granules. The granulation of multiple features results in granular vectors. Some operations of granules are defined. Furthermore, we propose some granular measures, including granular norms and granular loss functions. The GAE is further applied to the field of intrusion detection by designing an anomaly detection algorithm based on the GAE. The algorithm determines whether the network flows are anomalous by comparing the difference between an input granular vector and its output granular vector that is reconstructed by the GAE. Finally, some experiments are conducted using an intrusion detection dataset, comparing multiple metrics in terms of precision, recall, and F1-Score. The experimental results validate the correctness and effectiveness of the intrusion detection method based on GAE. And contrast experiments show that the proposed method has stronger ability for detecting anomalies than the correlation algorithms.
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Affiliation(s)
- Sihong Lin
- Xiamen Kuaikuai Network Technology Co., Ltd., Xiamen, China
| | - Kunbin Zhang
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Dun Guan
- Xiamen Kuaikuai Network Technology Co., Ltd., Xiamen, China
| | - Linjie He
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yumin Chen
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
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13
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Bahaa A, Sayed A, Elfangary L, Fahmy H. A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach. PLoS One 2022; 17:e0278493. [PMID: 36454861 PMCID: PMC9714761 DOI: 10.1371/journal.pone.0278493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/16/2022] [Indexed: 12/05/2022] Open
Abstract
Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization-whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO-WOA optimization algorithm's fitness value is defined as the validation set's cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO-WOA-CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO-WOA-CNN algorithm against APSO-CNN, SVM, and FNN. The simulation results suggest that APSO-WOA-CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO-WOA-CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO-CNN algorithm, and the APSO-CNN algorithm achieves the best performance, as compared to other algorithms.
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Affiliation(s)
- Ahmed Bahaa
- Faculty of Computers and Artificial Intelligence, Department of Information Systems, Helwan University, Helwan, Egypt
- Faculty of Computers and Artificial Intelligence, Department of Information Systems, Beni-Suef University, Beni Suef, Egypt
| | - Abdalla Sayed
- Faculty of Computers and Artificial Intelligence, Department of Information Systems, Helwan University, Helwan, Egypt
- * E-mail:
| | - Laila Elfangary
- Faculty of Computers and Artificial Intelligence, Department of Information Systems, Helwan University, Helwan, Egypt
| | - Hanan Fahmy
- Faculty of Computers and Artificial Intelligence, Department of Information Systems, Helwan University, Helwan, Egypt
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14
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Intrusion detection model using gene expression programming to optimize parameters of convolutional neural network for energy internet. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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15
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MEMBER: A multi-task learning model with hybrid deep features for network intrusion detection. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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16
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Suo Z, Dong Y, Tong F, Jiang D, Fang X, Chen X. Semiconductor superlattice physical unclonable function based two-dimensional compressive sensing cryptosystem and its application to image encryption. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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17
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Kaveh M, Mesgari MS. Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review. Neural Process Lett 2022; 55:1-104. [PMID: 36339645 PMCID: PMC9628382 DOI: 10.1007/s11063-022-11055-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2022] [Indexed: 12/02/2022]
Abstract
The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.
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Affiliation(s)
- Mehrdad Kaveh
- Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, 19967-15433 Iran
| | - Mohammad Saadi Mesgari
- Department of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, 19967-15433 Iran
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18
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Duan L, Xu C, Zhong S, Zhou H, Duan JA. Optical power auto-alignment method with eugenics sorting for enhancing the alignment speed and robustness of fiber-grating couplers. OPTICS EXPRESS 2022; 30:39544-39560. [PMID: 36298904 DOI: 10.1364/oe.470642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
To auto-couple optical devices, a simple but effective method must have a high success rate, fast scanning speed, and high stability. For coupling accuracy, swarm intelligence algorithms set a large number of particles to find the optimal point, which can introduce accelerated geometric errors in practical engineering. In this study, we proposed a method for auto-alignment between single-mode fibers and grating couplers using the particle swarm optimization algorithm, which introduces a chaotic mapping and eugenics mechanism. With the help of chaotic mapping and eugenics mechanisms, the scanning speed and robustness increased remarkably. A series of simulations and experiments showed that this method could increase the efficiency and robustness by 90% and 50%, respectively, compared to the basic swarm intelligence algorithm.
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19
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A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment. FUTURE INTERNET 2022. [DOI: 10.3390/fi14100301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques.
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20
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Li Y, Qin T, Huang Y, Lan J, Liang Z, Geng T. HDFEF: A hierarchical and dynamic feature extraction framework for intrusion detection systems. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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21
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Li K, Ma W, Duan H, Xie H, ZHU J. Few-shot IoT attack detection based on RFP-CNN and adversarial unsupervised domain-adaptive regularization. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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22
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A novel hierarchical attention-based triplet network with unsupervised domain adaptation for network intrusion detection. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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24
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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Zhao L, Zhang T, Peng X, Zhang X. A Novel Long-term Power Forecasting based Smart Grid Hybrid Energy Storage System Optimal Sizing Method Considering Uncertainties. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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26
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Boopathi M. Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT. INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022. [DOI: 10.1007/s41315-022-00234-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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27
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A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel. AI 2022. [DOI: 10.3390/ai3020024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm optimization will sometimes have particles that become trapped inside local minima, preventing them from identifying a global optimal solution. As a solution to this issue, this paper proposes a science-fiction inspired enhancement of particle swarm optimization where an impactful iteration is identified and the algorithm is rerun from this point, with a change made to the swarm. The proposed technique is tested using multiple variations on several different functions representing optimization problems and several standard test functions used to test various particle swarm optimization techniques.
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28
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Wang Y, Liu W, Liu X. Explainable AI techniques with application to NBA gameplay prediction. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.01.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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29
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IoT authentication model with optimized deep Q network for attack detection and mitigation. INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022. [DOI: 10.1007/s41315-022-00227-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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30
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An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks. ELECTRONICS 2022. [DOI: 10.3390/electronics11040649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.
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31
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32
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Abnormal data detection of guidance angle based on SMP-SVDD for seeker. Sci Rep 2022; 12:1509. [PMID: 35087183 PMCID: PMC8795151 DOI: 10.1038/s41598-022-05565-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/10/2021] [Indexed: 11/09/2022] Open
Abstract
The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, a new abnormal data detection method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed. In the proposed method, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, in which the particle swarm optimization is used to find the optimal kernels for higher detection accuracy. Besides, by using smoothing mechanism, the constrained quadratic programming problem is translated to be smooth and differentiable. Then, this problem can be solved by the conjugate gradient method, which could reduce the computational complexity. In experimental section, abundant simulation experiments were designed and the experimental results verify that the proposed SMP-SVDD method could achieve higher detection accuracy and low computational cost compared with different detection methods in different guidance stages.
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33
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Fatani A, Dahou A, Al-qaness MAA, Lu S, Elaziz MA. Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System. SENSORS (BASEL, SWITZERLAND) 2021; 22:140. [PMID: 35009682 PMCID: PMC8749550 DOI: 10.3390/s22010140] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/16/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022]
Abstract
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
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Affiliation(s)
- Abdulaziz Fatani
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;
- Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia
| | - Abdelghani Dahou
- LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria;
| | - Mohammed A. A. Al-qaness
- Faculty of Engineering, Sana’a University, Sana’a 12544, Yemen
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Songfeng Lu
- School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, China
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt;
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
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34
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Liang W, Ning Z, Xie S, Hu Y, Lu S, Zhang D. Secure fusion approach for the Internet of Things in smart autonomous multi-robot systems. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.08.035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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35
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Cheng S, Wu Y, Li Y, Yao F, Min F. TWD-SFNN: Three-way decisions with a single hidden layer feedforward neural network. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.07.091] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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36
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Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm. ELECTRONICS 2021. [DOI: 10.3390/electronics10202518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value.
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