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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices.

关键词： Deep Learning

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ML) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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Usability Evaluation of Kids’ Learning Apps

Usability Evaluation of Kids’ Learning Apps

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Amer Ibrahim Murad Al-Rajab Khalid Hamid Muhamamd Aqeel Salman Muneer Mehvish Parveen Maryam Saleem College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates Assistant Professor Computer Scienc and IT College of Engineering Abu Dhabi University Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Science NCBAE Lahore Pakistan

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids’ fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically.

关键词： Electronic learning User interfaces Security Usability Standards Business

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MATE: Masked Autoencoders are Online 3D Test-Time Learners

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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International Conference on computer Vision (ICCV)

作者： M. Jehanzeb Mirza Inkyu Shin Wei Lin Andreas Schriebl Kunyang Sun Jaesung Choe Mateusz Kozinski Horst Possegger In So Kweon Kuk-Jin Yoon Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Korea Advanced Institute of Science and Technology (KAIST) South Korea Southeast University China

Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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Quantifying spatial, temporal, angular and spectral structure of effective daylight in perceptually meaningful ways

arXiv

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arXiv 2022年

作者： Yu, Cehao Wijntjes, Maarten Eisemann, Elmar Pont, Sylvia Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Computer Graphics and Visualization Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We present a method to capture the 7-dimensional light field structure, and translate it into perceptually-relevant information. Our spectral cubic illumination method quantifies objective correlates of perceptually relevant diffuse and directed light components, including their variations over time, space, in color and direction, and the environment’s response to sky and sunlight. We applied it "in the wild", capturing how light on a sunny day differs between light and shadow, and how light varies over sunny and cloudy days. We discuss the added value of our method for capturing nuanced lighting effects on scene and object appearance, such as chromatic gradients. © 2022, CC BY.

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A Robust Statistical CNNCTC-Based AI Model for Tracking and Monitoring COVID-19 1st

A Robust Statistical CNNCTC-Based AI Model for Tracking and ...

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Chelliah, Balika J. Arunkumar, S. Prabavathi, R. Aarthi, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Assistant Professor Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (纸本)9789811612435

The world today battles the new coronavirus (COVID-19) spread that can lead to a major endemic or pandemic outbreak with devastating outcomes which has overturned all our usual calculus seemingly. Because of the unavailability of a vaccine and the serious extent of infectiousness, social distancing is perhaps the only way left to fight the infection. The tracking system is still a challenge for monitoring the COVID-19 affected person and the community spread. This leads to an epidemic situation, where the number of secondary infections keeps going higher and higher which intern leads to the difficulty of tracking and treating each infected person. An AI-based mathematical modelling is proposed for tracking and monitoring the pandemic outbreak. A GPS tracking system, geolocation using Fit bit and Wi-Fi tracking modules are developed to locate the secondary infections and the carriers. Calculation of real-time coordinates and the conditional probabilities with respect to time is done followed by the classification based on risk using CNN connectionist temporal classification (CTC) model, a type of neural network which is used to train the recurrent process. Monitoring the affected person using sensors like thermal sensors and a tracking chip inbuilt in a wristband for real-time updates. A comparative study is done on various classification models. Therefore, the system provides a robust model with 96.5% accuracy to combat this pandemic situation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

Majority Voting-based Consensus Mechanism for UAVs Decision ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Riya Kakkar Rajesh Gupta Mohammad S. Obaidiat Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing China The Amity University Noida Uttar Pradesh India

The revolutionary and rapid development of Information and Communication technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput.

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Optimizing Wireless Sensor Network Routing Through Memetic Algorithms: Enhancing Energy Efficiency and Data Reliability

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Procedia computer science 2023年 230卷 150-157页

作者： Aishwarya D Saranya S Sathiya A Samuel Manoharan J Asst. Professor/Dept. of Computer Science and Engineering School of Computing Sathyabama Institue of Science and Technology Chennai India Asst. Professor (SS)/ Dept. of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Research Scholar/Dept. of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur India Professsor/Dept. of Electronics and Communication Engineering Sir Issac Newton College of Engineering and Technology Nagapattinam India

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods.

关键词： Wireless Sensor Networks Energy Efficient Routing Memetic Computing Evolutionary algorihtms

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A Novel Intelligent AI-Based Security to Enhance the Data Communication: An Empirical Review

A Novel Intelligent AI-Based Security to Enhance the Data Co...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Shaik, Amjan Hung, Bui Thanh Chakrabarti, Prasun Siva Shankar, S. Parveen, Nikhat Department of Computer Science and Engineering Dean R&D St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh Ho Chi Minh Viet Nam Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Adjunct Distinguished Professor ITM SLS Baroda University Gujarat Vadodara India CSE K G Reddy College of Engineering and Technology Moinabad India CSE Koneru Lakshmaiah Education Foundation AP Guntur Vaddeswaram India

ISBN: (纸本)9789819947164

The transmission of digital data between two devices is termed data communication. As the world nowadays relies on digital communication, the transferring of data among various devices is vitally important, and hence, the security of the data is a concern. Several works are carried out to secure the data transmission between the devices in various applications like mobile communication, wireless communication, vehicular communication, and more. Incorporated with security, the data classification is also performed to ignore the unwanted or hackers’ data. In this work, we have discussed several secure data communication-related papers from various applications. Works including machine learning-based approaches such as decision tree, linear regression, logistic regression, KNN, K-means, and deep learning-based approaches such as a convolutional neural network, recurrent neural network, and long short-term memory approaches were discussed and listed with their limitations along with merits. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

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computers and Security 2022年 116卷

作者： M .P . , Ramkumar Reddy, P.V. Bhaskar Thirukrishna, J.T. Vidyadhari, Ch. Assistant Professor Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Professor School of Computer Science and Engineering REVA University Karnataka Bengalore India Associate Professor Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Assistant Professor Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

With the rapid expansion of Internet services and increasing intrusion issues, conventional intrusion detection techniques cannot work well with several difficult intrusions. Though several intrusion detection methods have been introduced in the past years, developing an Intrusion Detection Systems (IDS) with prevailing intrusion detection strategy is still very much desirable. Hence, a robust and effective intrusion detection approach, named RV coefficient+Exponential Sea Lion Optimization-enabled Deep Residual Network (ExpSLO-enabled DRN) using spark is devised for the intrusion detection. Here, the unique features are selected using proposed RV coefficient-based hybrid feature fusion, which is designed by the incorporation of wrapper, class-wise information gain (CIG), and Canberra distance in slave node. With the distinctive features selected, the process of data augmentation is done using oversampling for making the data more appropriate to perform the further process in the slave node. Moreover, DRN classifier is utilized for detecting the intrusions in the master node where the DRN training is done using devised ExpSLO algorithm, which is the hybridization of Exponentially Weighted Moving average (EWMA) and Sea Lion Optimization (SLnO). Furthermore, the devised method obtained better performance by considering the evaluation metrics, such as precision, recall, and F-measure with the higher values of 0.8800, 0.8845, and 0.8822 based on without attacks using dataset-2. © 2022

关键词： Intrusion detection

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