@article{ author = {Teymouri, Ahmad and Deypir, Mahmoo}, title = {Two-level intrusion detection system for Internet of Things network based on deep learning}, abstract ={Along with the growth in the use of Internet of Things networks for various applications, threats and attacks related to these types of networks have also increased. Intrusion detection systems are designed and used to detect and identify attacks in this type of networks, and to identify intrusions or abuses that are going to take place from the network, and to inform the relevant authorities about this issue. In most intrusion detection systems, various methods and algorithms are used, including deep neural networks (DNNs), support vector machines (SVM), or multilayer perceptron (MLP), and other traditional machine learning models. Each method has advantages and disadvantages, but it usually has a lower accuracy rate than combined methods. In recent years, the idea of combining classifications has been used for anomaly-based diagnosis. In this research, to reach better accuracy, we used the combination of principal component analysis (PCA) and convolutional neural network (CNN) algorithms to design our intrusion detection system. In the initial step of the proposed method, after preprocessing including conversions and normalizations, valuable features for classification are extracted. In this study, the NSL-KDD dataset, which has been mentioned in many scientific articles as a valid reference dataset in the field of intrusion detection, has been used. In fact, due to the high number of data dimensions and the high dispersion of feature values, we used a dimension reduction method. The dimensionality reduction method used in this research is principal component analysis (PCA). In the PCA method, the dimensions of the data are reduced in such a way that the reduced dimension data also includes the vital information of the dataset. We used PCA in order to reduce the size and volume of the input data to help increase the efficiency of our main algorithm and the new data generated with this algorithm is provided to the CNN classifier. A convolutional neural network is a special type of neural network with multiple layers that processes data that has a grid arrangement and then extracts important features from them. Here, accurate pattern learning and deep insight from the given data are our two main reasons for using CNN. In the proposed approach, we have two level classification including binary CNN and multi-class CNN, for detecting attacks and exact type of them, respectively. That is, firstly attacks and normal data are identified by binary classification and then by multi-class classification, the types of attacks are identified and separated. In fact, the type of attacks which includes one of DoS, U2R, R2L and Probe cases is determined using second convolutional neural network. Based on the obtained results, we have witnessed the growth of the accuracy rate of the proposed method compared to many other popular methods. In the evaluation of accuracy parameter values for different phases of training and testing, competitive results are observed for binary classification phase. Here we consider the number of 15 rounds. As it is clear from the graph related to training, the accuracy values in the final courses have reached 0.94. The accuracy of the test has also approached the value of 0.9 in the last round. Also, the results obtained in multi-class CNN are such that the accuracy value is 0.99 in the classification of the training data samples and 0.97 in the classification of the test data samples. Moreover, the cost graphs for training and testing courses of multi-class CNN are shown. The cost of training and testing in the final round is 0.06 and 0.09, respectively.}, Keywords = {Intrusion detection system, Convolutional neural network (CNN), Binary classifier, Multi-class classifier, Principal component analysis (PCA).}, volume = {21}, Number = {3}, pages = {3-22}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.3}, url = {http://jsdp.rcisp.ac.ir/article-1-1388-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1388-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Fateh, Haleh and Rezvani, Mohsen and Tahanian, Esmaeel}, title = {A systematic review of the foundations, applications, and challenges of federated learning.}, abstract ={Federated Learning (FL) is an innovative machine learning paradigm that tackles the challenge of data island while safeguarding data privacy. It enables decentralized model training by allowing multiple clients—such as mobile devices, institutions, or organizations—to collaboratively build models without transferring local data to a central server. This paradigm gained significant attention following Google’s 2016 initiative to predict user text input on Android devices while maintaining the privacy of locally stored data. A core feature of FL is its distributed and encrypted framework, enabling participants to contribute to a collective learning process without revealing their original data to a central entity or other participants. In recent years, FL has evolved to encompass a broader spectrum of decentralized machine learning techniques, while still maintaining privacy as a central tenet. This evolution has positioned FL as a critical technology in sectors where data privacy, security, and sovereignty are paramount. This paper presents a systematic review of the literature on federated learning, synthesizing insights from review articles, Books, key documents, and published research. The review is structured as follows: Overview of Federated Learning: This section introduces the foundational concepts of FL, detailing its origins, core principles, and operational processes. The decentralized structure and privacy-preserving techniques employed in FL are examined, along with real-world applications as examples. Algorithms and Evolution: This section explores the state-of-the-art algorithms driving FL and traces their development over time. Key innovations in aggregation techniques, optimization methods, and client-server communication protocols are highlighted, demonstrating how they have enhanced FL's scalability and efficiency. Classification and Applications of FL Architectures: Federated learning architectures are categorized into three main types: horizontal federated learning, vertical federated learning, and federated transfer learning. This section analyzes the application of these architectures across various domains, highlighting their distinctive features and associated challenges. Applications in IoT, Smart Cities, and Healthcare: Using selected case studies, this section evaluates the deployment of FL in the Internet of Things (IoT), smart cities, and healthcare. It assesses how FL enhances data privacy, security, and operational efficiency in these domains, focusing on practical implementations. Comparative Analysis: This section offers a comparative evaluation of the various methods and algorithms used in the aforementioned fields, identifying their relative strengths and weaknesses. Special attention is given to the challenges posed by large-scale FL deployments, including communication overhead, data heterogeneity, and model convergence. Federated Learning and Related Technologies: This section explores the integration of FL with related technologies, such as federated deep learning and federated blockchain, particularly within the context of the Industrial Internet of Things (IIoT). The potential of these technologies to improve storage, data management, and resource optimization is discussed in detail. Challenges and Future Directions: The final section addresses the ongoing challenges facing FL, including scalability, model accuracy, communication costs, and compliance with regulatory frameworks. Additionally, it proposes future research directions aimed at improving the practicality and widespread adoption of FL in industrial and commercial applications. This systematic review provides a comprehensive examination of federated learning’s current state, including its foundational concepts, applications, and challenges. It also outlines a forward-looking perspective on the advancements needed to establish FL as a key technology in privacy-centric, decentralized machine learning.}, Keywords = {Federated learning, decentralized machine learning, privacy-preserving, Distributed artificial intelligence, Internet of Things}, volume = {21}, Number = {3}, pages = {23-68}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.23}, url = {http://jsdp.rcisp.ac.ir/article-1-1389-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1389-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Jahani, Seyyed Ali and Mohebbi, Keyvan and ZamaniBoroujeni, Fars}, title = {Improving Scene Recognition in Remote Sensing Using Deep Learning and Feature Selector}, abstract ={Remote sensing images as a valuable data source in Earth observation can help in measuring and observing detailed structures on the Earth's surface. Scene detection in remote sensing images has many applications in various fields such as urban planning, natural hazard detection, environmental monitoring, vegetation mapping, and geographic object detection. One of the key problems in the interpretation of remote sensing images is the scene classification of remote sensing images. Feature extraction is very important in scene detection and classification. Convolutional neural networks are one of the deep learning methods that have significantly increased the performance of tasks such as object recognition and scene classification, but their performance is highly dependent on the number of labeled images available, which are not available enough especially in the field of remote sensing. Recently, transfer learning, especially for the fine-tuning of pre-trained convolutional neural networks, has attracted more attention from researchers as a practical strategy for scene classification in remote sensing. However, the lack of use of local features and global deep model that is trained on the target data set is one of the limitations of current methods. Also, if these networks are not deep enough and the images do not pass through multiple filters, they cannot extract more semantic information, and the extracted features do not have high discrimination power, and as a result, scene recognition is not performed well. On the other hand, the features extracted through local features are very large, and not using feature selector methods reduces the accuracy of the model. In this research, to solve the mentioned limitations, a hybrid approach of feature extraction has been proposed in which three types of features including two types of deep local and global features and one type of manual local feature are combined with each other. To extract deep features, pre-trained convolutional networks have been used. The pre-trained networks used are: ResNet, InceptionNet, GoogleNet and EfficientNet_b0. In order to extract as much information as possible from the images, a convolutional network with 20 fully connected layers is proposed. Also, a combined feature selection stage consisting of two categories of filtering and packing algorithms is included in this model. Finally, scene detection is performed using several different classification algorithms. The different structure of pre-trained convolutional networks and their appropriate depth can be effective in improving the extraction of deep features. In addition, the combination of three categories of different features can provide a more comprehensive knowledge of images. The evaluation of the proposed solution on the UCM, AID, RSSCN7 and NWPU-RESISC45 datasets has obtained the accuracy of 99.27%, 97.91%, 99.09% and 93.09% respectively in identifying and classifying images. As a result, this solution has shown a better performance compared to the models that used the manual extraction of features, as well as the methods that use normal convolutional models.}, Keywords = {Remote Sensing, Deep Learning, Deep Feature, Hybrid Learning, Pre-Trained}, volume = {21}, Number = {3}, pages = {69-84}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.69}, url = {http://jsdp.rcisp.ac.ir/article-1-1398-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1398-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Ebrahimi, Abolufazl and Shamsi, Mahboubeh and Mohajjel, Mortez}, title = {Improvement of missing vital signs data estimation algorithm in wireless body sensor networks based on deep neural networks}, abstract ={In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. Many methods are used to reconstruct lost sensor data based on temporal correlation, spatial correlation, interpolation method, or sparse theory. Due to the characteristics of vital signs data, they can be considered as a series of sequential information. So far, various methods have been developed to estimate missing data in time series data in different fields. These methods can be divided into two categories: statistical methods and machine learning-based methods. In order to predict missing values, a missing data estimation model based on LSTM recurrent neural network whose network weights are optimized by particle swarm algorithm (PSO) is presented in this paper. In this paper, we use the MIMIC-III Waveform database to test the algorithm and determine the algorithm parameters. However, due to the large volume of data and the difficulty of testing the algorithm on all data, we suffice to test 500 patients with this data, whose vital signs included heart rate, respiration, blood oxygen, and so on. After data preprocessing, network training, predicting lost values and calculating error values, it is observed that the proposed technique of sgdm-LSTM By combining the PSO algorithm is a suitable method for estimating lost values. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 1.5898 with the best LSTM network hyperparameters.}, Keywords = {WBAN, Deep Learning, Artificial Neural Network, Missing Data, Estimation}, volume = {21}, Number = {3}, pages = {85-96}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.85}, url = {http://jsdp.rcisp.ac.ir/article-1-1277-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1277-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Naghavi, Mehdi and HassaniAhangar, Mohmmad Reza and AmiriJezeh, Ali}, title = {Identify the Named Entities Using Deep Learning and Reinforcement Approach}, abstract ={Named Entity Recognition (NER) has emerged as a critical and highly applicable task in the field of Natural Language Processing (NLP). Its significance stems from its essential role in numerous NLP applications, such as machine translation, question answering, text summarization, and information extraction. Recent studies highlight the substantial impact of advancements in Artificial Intelligence (AI), particularly Deep Neural Networks (DNNs), on improving the performance of NER systems. Deep Neural Networks, with their ability to learn complex patterns and extract rich features, have opened new horizons in addressing NLP challenges. These methods leverage advanced language models like BERT and GPT to enable deeper comprehension of linguistic structures and semantic relationships. One of their prominent capabilities is to capture long-term dependencies in complex sentences while reducing the reliance on manually engineered features. This research introduces a novel hybrid approach for Named Entity Recognition in both Persian and English languages, based on deep neural networks and semantic language models. To address the dependency on large datasets, the proposed method employs an iterative logic mechanism that facilitates effective learning with limited data. The proposed system was evaluated on three datasets: The CoNLL 2003 dataset for English, Two Persian datasets, Arman and Peyma. Experimental results demonstrate that the proposed method achieves F1-scores of 95.3, 96.32, and 94.72 on the CoNLL, Arman, and Peyma datasets, respectively. These scores reflect significant improvements over previous methods. The findings of this study suggest that combining advanced language models with deep neural networks can significantly enhance the accuracy and efficiency of NER systems. These achievements pave the way for developing effective NLP tools for low-resource languages, particularly Persian, and enable the application of this technology in both industrial and research contexts.}, Keywords = {Named Entity, Named Entity Recognition, Language Model, Deep Learning}, volume = {21}, Number = {3}, pages = {97-110}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.97}, url = {http://jsdp.rcisp.ac.ir/article-1-1232-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1232-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Samimi, Navid and Nejatian, Samad and Parvin, Hamid and BagheriFard, Karamolah and Rezaei, Vahideh}, title = {Presenting a Method based on Genetic Algorithm for finding the most Stable Clusters in Ensemble Clustering}, abstract ={Clustering is one of the fundamental tools in data analysis and data mining, enabling the extraction of hidden and meaningful structures from large datasets by grouping data based on intrinsic similarities. However, selecting optimal clusters in conventional clustering algorithms poses challenges, especially when clusters are dense or heterogeneous. In this study, a novel genetic algorithm-based method is proposed to identify the most stable clusters in ensemble clustering. By leveraging cluster stability criteria and a correlation matrix, the proposed approach improves the accuracy and stability of the final clustering results. The proposed method involves generating initial partitions of the data using six different clustering algorithms. Next, the Fisher criterion is applied to identify more stable clusters. These selected clusters are then evaluated and optimized using a genetic algorithm to construct an optimized correlation matrix. This matrix is subsequently fed into a hierarchical clustering algorithm, which produces the final consensus clustering. The proposed method was tested on standard datasets. Results demonstrated improvements of 12% and 5% in NMI and ARI metrics, respectively, compared to previous methods. The use of a genetic algorithm enabled the identification of clusters with higher stability and diversity, reducing the impact of noise and increasing the accuracy of the final clustering. Moreover, the method outperformed individual base clustering algorithms in providing more precise clustering results. Due to its ability to enhance the accuracy and stability of clustering, the proposed method holds potential for applications in domains such as big data analysis, machine learning, and information retrieval. The use of the Fisher criterion for selecting stable clusters and genetic algorithms for optimization are among the strengths of this research. This method not only preserves diversity among clusters but also significantly enhances clustering accuracy. Future studies could explore the combination of this approach with more advanced algorithms to assess its applicability to more complex datasets.}, Keywords = {Ensemble clustering, Cluster Stability, Fisher Criterion, Correlation matrix, Genetic Algorithm}, volume = {21}, Number = {3}, pages = {111-136}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.111}, url = {http://jsdp.rcisp.ac.ir/article-1-1217-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1217-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Ghasemi, Masoume and horri, abbas and basiri, Mohammad Ehs}, title = {Detecting Android malware with offloading approach in cloud computing}, abstract ={Today, the mobile phone is one of the smart devices that have become a necessity in everyday life and are used for various tasks such as shopping, banking, communicating with friends, family, etc. In recent years, the Android operating system has been able to gain more popularity than other mobile phone operating systems. The number of software related to this operating system is also expanding at a remarkable speed. Unfortunately, this issue is not hidden from the profit-seeking people, and the production of malware of this operating system has also grown in parallel with its development. Third-party Android app stores that have emerged in recent years have become a very strong source of malware distribution, as these stores have weak to non-existent measures to prevent malicious apps from being uploaded and distributed to users' devices. Therefore, one of the challenges that programmers are dealing with in this field is to find solutions to establish security in these types of devices, in such a way that it provides powerful security analysis capabilities while consuming few resources on the device itself. Software products such as Lookout, Norton, and Comodo Mobile Security mainly use signature-based methods to detect malware threats. However, malware attackers use techniques such as repackaging and obfuscation to circumvent signatures and defeat attempts to analyze their internal mechanisms. The ever-increasing sophistication of Android malware requires new defense techniques that can protect users against new threats while not using up all of a mobile device's processing and storage resources. Therefore, in the current research, a computational offloading method is presented in the cloud structure to identify Android malware. The solution proposed by this research first extracts the features of Android applications during installation and execution on the mobile phone, then sends these extracted features to the cloud servers. On the cloud server side, these features are analyzed and using machine learning algorithms, malware is distinguished from clean programs. The proposed approach is trained and tested using the Drebin dataset. The obtained results show that the proposed approach has achieved 96.44% accuracy for malware detection.}, Keywords = {Android Malware, Machine Learning, Cloud Computing}, volume = {21}, Number = {3}, pages = {137-148}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.137}, url = {http://jsdp.rcisp.ac.ir/article-1-1336-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1336-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {HassanPourAskari, Abbas and KhatibiBardsiri, Amid and MohammadiGhanatGhestani, Mokhtar}, title = {IoT privacy for the transmission of data in the field of health using blockchain}, abstract ={Data transmission and storage through blockchain is something that many studies have suggested for various security issues. Due to the sensitivities in securing information from patients and medical professionals, the healthcare system has a favorable context for deploying a powerful blockchain system for privacy. Blockchain application in the healthcare system allows physicians to store patient records with high security and make them available to other hospitals and clinics as needed. Besides increasing data transmission and storage security, this reduces data management risks and expenses. The present study emphasizes the confidentiality of healthcare data in the cloud-based Internet of Things (IoT) using blockchain and edge computing. Also this method uses SHA2 hashing and PKI encryption. Therefore, it can optimally provide data confidentiality in medical settings, especially for patients under care. The system proposed in this study includes five parts: users, IoT devices, edge devices, security server, and cloud computing. The proposed method uses the Diffie – Hellman key exchange in the authentication process for anomality case to achieve the goal of essential security compliance. This method is a cryptographic (encryption) protocol allowing two people or two organizations to create a shared password key without the need for any prior acquaintance and exchange it through an insecure connection path. This study used data sensed by IoT network sensors with medical data for loading on IoT and cloud simulated networks. These data were related to monitoring patients with cardiovascular disease and were sampled on 300 patients. The research dataset belonged to the Cleveland Clinic Foundation for Heart Disease Dataset. The study simulation software was NS-2.35, which used C++ and TCL programming languages. The research findings revealed that if there were no method for data encryption, much of the data would be exposed. This rate reaches 50% for 50 attackers. Encryption using the proposed method causes the percentage of data disclosed to be very slight, and it equals zero, even though attackers may guess the password or data. As network traffic grows, the throughput difference between blocking and non-blocking access methods increases. It suggests that by blocking the attacking nodes’ access, network traffic will not have a detrimental effect on attacking nodes by detecting and preventing their activity. However, if the access of these nodes is not blocked, the destructive impact is very high, and the network traffic will grow slightly. According to these results, in terms of SLA violation, the system is in a situation where even in case of an attack, there is no SLA violation and the efficiency is maintained. Also, the percentage of access to useful information by the hacker will be close to zero. By preventing the entry of malicious nodes, the throughput increases by about 30%. Some other advantages of this method are its high flexibility and comparability, robustness, and relatively low execution time and delay, which is caused by the use of cloud edge. It is estimated that the improvement rate of the proposed method is more than 5% compared to other related approaches. In the design presented in this study, the processes are highly simplified, and there will be a relatively low processing overhead. At the same time, the steps meet all the requirements for cloud and IoT data centers in healthcare applications.}, Keywords = {cloud-based IoT, health record system, blockchain, data confidentiality, edge computing}, volume = {21}, Number = {3}, pages = {149-178}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.3.149}, url = {http://jsdp.rcisp.ac.ir/article-1-1314-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1314-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} }