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A Novel Image Encryption Optimization Technique

A Novel Image Encryption Optimization Technique

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Gollagi, Shantappa G Srividya, R Santhosh Kumar, G Pareek, Piyush Kumar Hirasugar Institute of Technology Computer Science and Engineering Nidasoshi India Cmr Institute of Technology Information Science and Engineering Bengaluru India East West College of Engineering Bengaluru India Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665420051

Image file confidentiality is a difficult issue these days for a variety of reasons in a range of industries, including medical, defence, academia, and many more. A range of practical ways have been studied in recent years to safeguard image data, however owing to technological improvements, such existing solutions have significant limits in current systems. Several academics have investigated a number of methodologies and strategies for maintaining picture secrecy and shielding top-secret information from attackers while images are sent via communications infrastructure. In this paper, an unique picture encryption technique based on a chaotic map and DNA encoding is jointly used for image encryption in a more pragmatic approach, as necessary in today's world to safeguard private data. This approach was tried and confirmed on a personal laptop with a 64-bit operating system, Windows 10, with 16 GB RAM and the MATLAB R2020a version loaded (Random Access Memory). The results of this suggested approach reflect the best NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changed Intensity) scores for a variety of selected photos for testing. This method works with a variety of image formats and is in great demand in today's world for safeguarding sensitive data or information while being transmitted across a communications infrastructure. More research is needed in the future to identify practical answers, as current communication networks are quickly changing throughout the world owing to technology breakthroughs. © 2021 IEEE.

关键词： Cryptography

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A New Method of Secure Image Encryption by Using Enhanced RSA Algorithm

A New Method of Secure Image Encryption by Using Enhanced RS...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

ISBN: (纸本)9781665420051

The images are playing a key role in diverse areas worldwide as these images contains vast amount of essential data. The secrecy as well as privacy of the images has become an immeasurable challenge that demands more attention in order to offer well-organized and faster way of transmission form security perspectives as the images comprising gigantic amount of essential data in diverse images formats. In last decade, multifarious researchers have investigated pragmatic methods to secure the essential data contained in the multifarious images formats but due to constant technological advancements in modern communication systems such existing methods have numerous limitations. In order to, effectively resolve these threats related to the secrecy as well as privacy of the diverse images format, an enhanced RSA (Rivest-Shamir-Adleman) algorithm is investigated to provide required security to the image data during communication over diverse channels. This proposed algorithm is selected due to diverse reasons such as easy implementation as well as less computational complexity in juxtaposition with several existing algorithms explored in last decade by researchers. The selected picture encryption in the testing procedure was done in a pragmatic manner with high degree of precision in order to avoid the chances of the errors and images were secured via the XOR diffusion for confusing the hackers in transmission of the images. Although, extensive research is conducted in this field in past many years but still there is a mammoth scope of more investigation to explore full potential of this domain. © 2021 IEEE.

关键词： Cryptography

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Improved Gated Recurrent Unit Based Network Traffic Anomaly Detection

Improved Gated Recurrent Unit Based Network Traffic Anomaly ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Layth Hussein Jasim P Nagarathna Yada Sunitha V Lekha R. Padmavathy Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The detection of unpredicted events or rare patterns in traffic data plays an important role in intelligence traffic management, that offers alerts of incidents. To address the problem of less accuracy in industrial network traffic anomaly detection, in this manuscript developed the Improved Gated Recurrent Unit (GRU) for multi-type anomaly detection. The pre-processing included label encoding and non-string numerical process. The Synthetic Minority Over-Sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) technique is utilized for balancing the data by oversampling and under sampling. The improved GRU is assigned for extracting linear, non-linear and temporal features from network traffic data. These features are given to classifier developed based on Softmax for obtaining high performance detection on traffic attacks. The efficiency is evaluated based on UNSW-B15 dataset with the detection accuracy of 0.97 for DoS attacks, 0.96 for Fuzzers attacks and 0.94 for Worms attacks while compared to existing algorithms like conventional GRU for network traffic anomaly detection.

关键词： Integrated circuits Accuracy Telecommunication traffic Traffic control Feature extraction Encoding Real-time systems Noise measurement Grippers Anomaly detection

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Design of Software engineering Approach’s for web learning applications using Cloud Computing

Design of Software Engineering Approach’s for web learning ...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： N. Sreenivasa P. Ramesh Naidu Naresh E Chandrakala B M Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Information Science and Engineering Dayanandasagar College of Engineering Kumaraswamy Layout Bengaluru

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Over the years, Cloud computing has significantly influenced various sectors, including Web learning, as education is considered crucial for individual and national growth. The primary objective is to develop an Application Model that supports Web learning Services, addressing the limitations of existing web learning systems in terms of infrastructure and effectiveness. Cloud technology provides a platform to host web learning applications on a service basis, accessible to end users through the internet via cloud infrastructure. This approach offers cost-effective packages for educational organizations, benefiting both trainers and learners. To achieve this objective, a combination of technologies is required. The paper further emphasizes the importance of Web learning Design features and analyzes the significance of cloud computing. It explores the implementation of a cloudbased application model for web learning systems, focusing on its working methodology, architecture design, development tools, and external interfaces. Additionally, it explores the use of software engineering approaches in this context. The paper highlights the importance of utilizing cloud environments for institutes and learners, emphasizing the potential benefits and opportunities in terms of design.

关键词： Learning systems Cloud computing Computational modeling Education Organizations User interfaces Real-time systems Service-oriented architecture Internet of Things Software engineering

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Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Ramya I M Jagadeesh B N LeenaShruthi H M Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

There has been a lot of focus on agricultural image dispensation research in the previous years. computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision.

关键词： computers Image segmentation Computational modeling Pipelines Crops Real-time systems Convolutional neural networks

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Ensuring Dataset Accountability in Machine Learning: Insights from Software engineering

Ensuring Dataset Accountability in Machine Learning: Insight...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Veeranna Kotagi Vinay Kumar Nassa Dipti Patil Rajashree Gadhave Sri Bhargav Krishna Adusumilli Pitcheri Praveen Kumar Information Science and Engineering NITTE Meenakshi Institute Of Technology Bangalore Computer Science Engineering Bharat institute of Engg. & Technology Hyderabad Telangana Computer Engineering Pillai HOC College of Engineering and Technology Rasayani Maharashtra Computer Engineering Pillai HOC college of Engineering and Technology University of Mumbai Mumbai Maharashtra Computer Science Engineering San Francisco Bay University Fremont CA USA Electronic and Communication Engineering Anurag University Hyderabad Telangana

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Machine learning is facing a crisis of accountability. On some tasks, deep learning can match or surpass human performance. It is extensive This work may be reproduced in whole or in part, for educational or personal purposes, in hard copy or digital format without monetary compensation as long as copies are made or distributed for non-commercial purposes and carry this notice and the whole citation on the first page. Conversely, the datasets that are crucial to machine learning (ML) are typically produced using opaque creation processes, poor maintenance, poor documentation, and a lack of answerability, which frequently results in errors. The study focused on several problems related to software engineering tasks, including performance evaluation metrics, software metrics, failure prediction, and problems with data quality. The paper draws attention to many methodological problems and difficulties associated with these software fault prediction tasks. Feature extraction and classification are commonly used to explore the excessive dimensionality of data and data class imbalance linked to software quality issues.

关键词： Support vector machines Performance evaluation Electric breakdown Software quality Documentation Feature extraction Maintenance Software measurement Informatics Software engineering

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An Innovative Method for Snatch Theft Detection Using Convolutional LSTM-Auto Encoder Approach

An Innovative Method for Snatch Theft Detection Using Convol...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Konda Reddy, Gondi Reddy Esanakula, Jayakiran Prajna, K.B. Chandra Purohit, Kamlesh Kaliappan, S. Maranan, Ramya Sreenidhi Institute of Science and Technology Department of Mechanical Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara144411 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350300857

Urban areas frequently experience snatch stealing, a severe threat to public safety. It entails forcibly seizing a victim's personal items, including purses or cell phones, before scurrying away from the scene. Due to how quickly they happen, it is difficult to identify snatch theft instances in real-time. The systems now in use to identify the snatching occurrences mainly rely on human participation, which may result in lengthy delays and mistakes. Consequently, there is a need for an automated method that can quickly and effectively identify these situations. Preprocessing, segmentation, feature extraction, and model training are all used in the suggested method. Gaussian Model is utilized for preprocessing. Canny edge is utilized for segmentation. LSTM-AE is used to train the model. Two further models, LSTM and AE, are contrasted with the suggested method. When compared to two existing approaches, the proposed strategy performs exceptionally well. © 2023 IEEE.

关键词： Feature extraction

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Implementation Of The NIST Algorithm For IPsec

Implementation Of The NIST Algorithm For IPsec

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Devesh Mirani Harsh Kumar Harshil Dhingra Harshitha SA Vijaya Shetty S SarojaDevi H Amitabh Srivastav Deepak Honahalli Computer Science and Engineering Department Nitte Meenakshi Institute of Technology Bengaluru India Unisys Bengaluru India

From ancient times cryptography has been used for encoding and delivering messages safely with advancements in technologies cryptography is used now all over the internet and over all types of files for encryption. With the advent of Quantum computing, present-day cryptographic methods are insufficient as quantum computers possess exponential computation and can crack present cryptographic methods. But right now quantum computing and its related resources are still under development so its required to develop post-quantum cryptography in advance. This paper deals with the implementation of NIST(National Institute of Standards and technology) shortlisted algorithms till the third round which includes Classic McEliece, Kyber, NTRU, Saber, Dilithium, Falcon, and Rainbow for making quantum-safe IPsec protocols using socket programming and strong swan between two virtual machines.

关键词： Encapsulation Quantum computing Protocols Sockets Programming NIST Virtual machining

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Sliced Bidirectional Gated Recurrent Unit with Sparrow Search Optimizer for Detecting the Attacks in IoT Environment

Sliced Bidirectional Gated Recurrent Unit with Sparrow Searc...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sureka R. Gondkar Siva Balan R V Kavitha S Raju Ramakrishna Gondkar Department of Electronic and Communication Engineering BMS Institute of Technology and Management Bangalore India Department of Computer Science CHRIST (Deemed-to-be-University) Bangalore India Department of MCA Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively.

关键词： Training Refining Logic gates Traffic control Search problems Internet of Things Task analysis

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Smart Agriculture in the Era of Big Data: IoT-Assisted Pest Forecasting and Resource Optimization for Sustainable Farming

Smart Agriculture in the Era of Big Data: IoT-Assisted Pest ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula Shwetha K S Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HW ACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption.

关键词： Smart agriculture Adaptation models Accuracy Biological system modeling Crops Pesticides Predictive models Prediction algorithms Cotton Random forests

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