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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

SN computer science 2024年第8期5卷 1-15页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Potato Leaf Disease Detection Method Based on the YOLO Model 4

Potato Leaf Disease Detection Method Based on the YOLO Model

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Bajpai, Prashant Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University College of Smart Computing Roorkee India Sr Institute of Management & Technology Department of Computer Science & Engineering Lucknow India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350393354

Global potato production is at risk because of potato leaf diseases, which cause huge economic losses. To ensure crop productivity and disease management, their efficient and accurate localization is of utmost importance. The YOLOv7 deep learning model is presented in this paper as a means of disease detection and classification in potato leaves. The suggested approach uses YOLOv7's superior detection accuracy to successfully identify and categorize potato leaf diseases. To train the model, we used a wide variety of images showing both healthy and diseased potato leaves. Verified by the test results, the suggested methodology successfully identified and classified diseases affecting potato foliage with a 98.1% accuracy rate. Through early disease detection and prompt control measure implementation, this method holds great promise for enhancing the yield and quality of potato crops in precision agriculture systems. The proposed framework shows great promise for practical implementation, as the experimental results confirm that it offers enhanced accuracy in detecting and predicting potato crop diseases. © 2024 IEEE.

关键词： Losses

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Breakthrough in breast tumor detection and diagnosis: a noise-resilient, rotation-invariant framework

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Multimedia Tools and Applications 2025年 1-27页

作者： Nosheen, Fariha Khan, Salabat Sharif, Muhammad Kim, Do Hyeun Alkanhel, Reem AbdelSamee, Nagwan Department of Computer Science COMSATS University Islamabad Attock Campus Attock43600 Pakistan Department of Computer Engineering Jeju National University Jeju Special Self-Governing Province Jeju-Si63243 Korea Republic of Big Data Research Center Jeju National University Jeju63243 Korea Republic of Department of Information Technology College of Computer and Information Sciences Princess Nourahbint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The fact that early-stage breast cancer typically presents no signs, poses a global risk to the lives of women. Digital mammography is just one method among many that can detect breast cancer in its early stages. Despite extensive research, most methods for detecting breast cancers still produce a large number of false positives. The difficulty in improving detection accuracy is in reducing false positives by differentiating masses from normal tissues. Using the textural properties of the masses, this study aims to develop a computer-aided diagnosis system that reduces the number of false positive and negative mammography results. The suggested method initially partitions regions of interest (ROI) into small patches to extract an abnormality region and micro-pattern, which permits the extraction of specific information about the image's content in targeted areas. Reducing the computational complexity of an image analysis operation is accomplished by partitioning a ROI into smaller patches rather than processing the complete image at once. Due to the significant impact the noise has on detection accuracy in mammograms, a textural descriptor that is insensitive to noise is introduced to describe image features such as lines, spots, flat areas, and edges. By expanding the rotation-invariant and noise-tolerant descriptor histograms, we generate an improved histogram that preserves the original regional patterns and spatial relationships between masses. To distinguish between "normal" and "mass" and "benign" and "malignant", Support Vector Machines (SVM) is employed with grid search based hyperparameter optimization. Our proposed approach was evaluated using the Digital Database for Screening Mammography (DDSM), which contains over 1024 ROI cases. This database is a well-established benchmark for testing new mammography analysis methods. Each case in the DDSM database has been analyzed and interpreted by qualified radiologists, with comprehensive information provided thr

关键词： Diseases

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Design and Implementation of Solar Powered Smart Irrigation Using IoT 8

Design and Implementation of Solar Powered Smart Irrigation ...

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8th IEEE International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2023

作者： Santhosh Kumar, C. Brindasri, S. Krishnamoorthy, J. Vidhya, B. Saranya, G. Madhan, K. Er.Perumal Manimekalai College of Engineering Department of Computer Science and Engineering Hosur India Hindustan Institute of Technology and Science Department of Computer Science and Engineering India Sri Krishna College of Technology Department of ECE Coimbatore India Mailam Engineering College Department of Computer Science and Engineering Mailam India

ISBN: (纸本)9798350347791

The farming is one concerning the indispensable assets due to the fact of advent of ingredients but assumes sizeable portion about the economic regulation about each and every United States on America via which include after Today, coast owners hold a snack troubles within agribusiness appropriate according to the fact of non-appearance about downpours after shortage about water. So, between that task, we wish raise external computerized wind law case due to the fact bank lords as like intention maintain time, value then after on Manual inclusion is wished due to the fact the farmland strategies. By construction use over soil humidity sensor degrees involving ground dampness/mugginess may additionally keep checked. At some also is an harmony the trade yet sends signal in conformity over the microcontroller or relying upstairs that the cloud provision framework works. Robotized cloud government litigation uses valves in accordance in accordance with Engines work continue to be robotized effectively by using construction use of regulators yet no assistance over guide intervention among accordance about flirt apparatus ON then OFF. © 2023 IEEE.

关键词： Internet of things

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Multi-Label Classification with Weakly Labeled Data Based on Concept-Cognitive Learning 23

Multi-Label Classification with Weakly Labeled Data Based on...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wu, Jia-Ming Tsang, Eric C.C. Zhang, Cheng-Ling Yang, Lan-Zhen School of Computer Science and Engineering Macau University of Science and Technology Taipa China College of Mathematics and Information Sciences Hebei University Baoding071002 China

ISBN: (纸本)9798331528041

With the increasing complexity of datasets in reality, it is difficult to obtain instances with full labels, resulting in weak label problems. The existing methods are mainly based on the low rank and instance manifold regularization assumption of label matrix to restore the ground-real label space, while ignoring the influence of semantic noise caused by label missing on the above assumption. Therefore, we propose a method to reconstruct the label space through concept-cognitive learning to solve the problem of multi-label classification with weak label. Specifically, we construct the relationship between the label and the feature by concatenating the label concept and the feature concept, and recover the label space by the similarity between the instance and the concept. Finally, a multi-label classification model is constructed by using label correlation. Experiments on real-world multi-label datasets verify the stability and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Contrastive Learning

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT 4

Eco-Detect: A Sustainable Solution to Advanced Waste Sorting...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Nandini Prasad, K.S. Manjula, G. Aishwarya, K.J. Chetankumar Prakash, K. N. Bhanu Chandan, V Sai Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering India Dayananda Sagar Academy of Technology and Management Department of Computer Science and Design India Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where te

关键词： Waste utilization

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Open Vuln Scanner with OpenVuln Security Framework 2

Open Vuln Scanner with OpenVuln Security Framework

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Rajheshwar, V. Sharat, N. Vishal, S. Suganthi, N. Kumaraguru College of Technology Computer Science Department Tamilnadu Coimbatore India

ISBN: (纸本)9798350306811

The research paper presents OpenVuln Scanner, a cutting-edge OSINT security tool that aims to streamline the process of performing security assessments for organizations. By combining the power of OSINT research and analysis with a carefully crafted vulnerability assessment framework, the tool provides a comprehensive solution for identifying and mitigating potential security threats. The proposed framework takes into consideration various security domains and provides a systematic approach to evaluating an organization's security posture. The tool is designed to be user-friendly and can be easily integrated into an organization's existing security infrastructure. With the aim of enhancing the overall security posture of organizations, the tool provides a valuable contribution to the field of cybersecurity. © 2023 IEEE.

关键词： Cybersecurity

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Gesture Recognition using Convolutional Neural Network 2

Gesture Recognition using Convolutional Neural Network

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2nd International Conference on Advances in Computational Intelligence and Communication, ICACIC 2023

作者： Shanmuga Priya, G. Nitisha Sree, V. Magisha, K. Pooviga, S. Sri Krishna College of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350318456

Hand gesture detection, which is adaptable and user-friendly, is one of the most active study areas in the field of human-computer interfaces. Using the gesture recognition system, a system is designed that can be deployed to communicate with disabled people or to administer a product. Excellent internal consistency commonalities in hand gesture postures, slight variances, non-uniform surroundings, with user-specific hand form and shape variations all offer considerable obstacles to creating an efficient hand gesture detection mechanism. In this article a hand gesture detection method which is user independent is examined using custom characteristics such convolutional neural networks (CNN). A technology known as Gesture Recognition uses mathematical algorithms to interpret human gestures. The dataset is collected and stored in the form of files and folders. Using CNN algorithm, the collected dataset is compared with the threshold value of the given input and thus the result is presented in the form of text or voice. Gesture Recognition System is an inventive user interface that eliminates the challenges associated with using remote controls for home appliances by replacing the remote-control system with hand gestures. Deep learning and computer vision are two tools that can be used to further the cause. Using the suggested method, an authentic implementation of gesture - based system is constructed and evaluated. © 2023 IEEE.

关键词： Remote control

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Assessing the Teachers’ Readiness for Integrating Augmented Reality in K-12 Education: A Comparative Analysis

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 22-44页

作者： Ewais, Ahmed Dalipi, Fisnik Abualrob, Marwan Ferati, Mexhid Kurti, Arianit Computer Science Department Faculty of Information Technology Arab American University Jenin Palestine Department of Informatics Faculty of Technology Linnaeus University Växjö Sweden Elementary Education Department Faculty of Arts Arab American University Jenin Palestine

The integration of augmented reality (AR) into educational environments will depend on its perceived effectiveness in enhancing teaching practices and the attitudes toward the use of this technology. Therefore, the main objective is to investigate the teachers’ attitude and motivation to adopt AR in educational settings, which also looks at a cross-cultural context. Furthermore, this research reveals different aspects that have an impact on teachers’ attitudes toward adopting AR in the teaching process. To investigate this, we conducted a study with 87 K-12 teachers belonging to two different education systems, i.e., Sweden and Palestine. The mixed-methods approach enhances the validity of the study and provides a broad understanding through numerical data, while qualitative insights offer deeper explanations of the findings. The results indicate a statistically significant difference in teachers’ attitudes about AR, with a mean 3.99 for teachers coming from Palestine showing a more positive attitude towards AR-supported learning. Therefore, it is important for educational institutions and application developers to consider a range of learning and teaching methods, as well as specific needs, throughout the process of developing and incorporating AR into the curriculum. © 2025 by the authors of this article.

关键词： attitudes augmented reality (AR) education learning schools teachers

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