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A robust method for malware analysis using stacking classifiers and dendrogram visualization

International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, R. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models. © Bharati Vidyapeeth's institute of computer applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

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Assessing the Effectiveness of Predictive Maintenance for Internet of Things (IoT) Networks Using Reinforcement Learning 3

Assessing the Effectiveness of Predictive Maintenance for In...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Sharma, Aparna Rastogi, Divya Manwal, Manika Tyagi, Ayushi Sharma, Prashant Kumar Computer Science and Applications Sharda University UP Greater Noida India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Applications Hindustan Institute of Management & Computer Studies UP Mathura India

ISBN: (纸本)9798350342338

Reinforcement getting to know (RL) has emerged as a famous method to predict considerable occasions inside the Net of Things (IoT) networks. RL is a form of artificial intelligence that models conduct from praise and punishment alerts for a given venture or aim. It has been efficaciously used to expect numerous events in dynamic systems and Internet of Things (IoT) networks. Predictive maintenance (PM) aims to stumble on and diagnose screw-ups in IoT structures before they occur. PM aims to expect events and situations in IoT devices to avoid unplanned stoppages or steeply-priced maintenance. Using RL for predictive protection helps with the upkeep of IoT networks by providing a proactive technique. The RL set of rules can study styles from the information accumulated in actual time from the IoT network, after which a prediction algorithm can discover ability screw-ups and endorse preventive preservation tactics. RL can also regulate its mastering process over time based on remarks from the IoT network. The RL approach toward PM provides tremendous benefits over conventional renovation strategies because of its self-getting-to-know and predictive skills. Extensive proof suggests that reinforcement mastering enhances the effectiveness of predictive preservation for IoT networks. RL gives many blessings along with personalized preventive renovation pointers and lowering the value of maintenance, amongst others. As a result, increasingly, groups are adopting reinforcement studying for predictive upkeep to take advantage of its cost financial savings and proactive technique. © 2023 IEEE.

关键词： artificial intelligence dynamic systems IoT predictive maintenance Reinforcement learning

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Moving Target Defense - Challenges, Problems and Suitability for Internet of Things 4

Moving Target Defense - Challenges, Problems and Suitability...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Gurung, Dipendra Gurung, Sandeep Pradhan, Mohan Pratap Sikkim Manipal Institute of Technology Department of Computer Applications Majitar India Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Sikkim University Department of Computer Applications Gangtok India

ISBN: (纸本)9798350323771

Traditionally cyber attackers have always had an upper hand over defenders as the latter can only react in the event of an attack. Moving Target Defense (MTD) has emerged as a potential game changer by reversing the advantage from attackers to defenders. Together with traditional defenses, MTD is expected to take cyber security up a notch. MTD solutions have been proposed for various levels, namely operating system, network, runtime platform, etc. However, improvement of existing solutions and development of new solutions are required. This paper presents an overview of MTD, the various MTD approaches, attacks mitigated, open research problems in MTD and MTD for the Internet of Things (IoT). © 2023 IEEE.

关键词： Internet of things

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Vulnerability Classification for Detecting Threats in Cloud Environments Against DDoS Attacks 13

Vulnerability Classification for Detecting Threats in Cloud ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jyothi, V. Esther Chowdary, N. Sampreeth Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350305463

In order to secure data, infrastructure, and services from potential attacks, cloud security is a wide issue that includes any combination of policies, technologies, and procedures. The cloud has substantially different security needs than traditional settings. Because of its dynamic nature and low client infrastructure ownership, the cloud has decreased traditional security architecture. Internet connectivity and distant servers are used in cloud computing to manage data and applications. It provides users with on-demand software, bandwidth, and dynamic virtualized resources through the internet and ensures that its adapters will share in various financial advantages. The utilization of hardware, software licenses, and system maintenance is decreased for the benefit of the customers. The protocol known as SOAP or Simple Object Access Protocol, it enables communication between various web-based services. Extensible Mark-up Language (XML) and Hyper Text Transport Protocol (HTTP) are both used to develop SOAP communications. A cloud web service that uses HTTP and XML might be bringing down by the new type of Distributed Denial of Service (DDoS) attacks. HTTP and XML Denial of Service (DoS) attacks provide a serious security risk to cloud computing. The Hypertext Extensible-Denial of Service (HX-DoS) attack, which causes the cloud service provider's communication channel to get blocked and go down, is made up of purposefully transmitted HTTP and XML messages. Differentiating between suitable and improper communications is necessary to solve the issue of HX-DoS attacks against cloud web services. To decide and place the packets on the side of the victim, the reconstruct and drop technique is utilized. Detection rate, accuracy, and precision were the performance parameters used to evaluated the model's performance. © 2024 IEEE.

关键词： Denial-of-service attack

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A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment

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Soft Computing 2023年第7期27卷 3817-3828页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804201 Taiwan

The primary aim of Virtual Machine Placement (VMP) is the mapping of Virtual Machines (VMs) to Physical Machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption significantly. The inefficient VMP leads to wastage of resources and excessive energy consumption and increases the overall operational cost of the data center. A Metaheuristic Virtual Machine Placement Framework towards the Power Efficiency of Sustainable Cloud Environment (MV-PESC) approach is suggested to address the issues mentioned above. An Extended Flower Pollination Optimization algorithm is suggested, which combines the concept of the Random Fit algorithm and the Flower Pollination Optimization algorithm. The proposed work’s performance is evaluated using actual workload traces of the benchmark Google Cluster Data set. The obtained results are compared with various state-of-the-art and demonstrate a notable reduction in power consumption, the number of active PMs, and execution time up to 64.89%, 35%, and 21.12%, respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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Detection of Leading CNN Models for AI Image Accuracy and Efficiency

Detection of Leading CNN Models for AI Image Accuracy and Ef...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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mHAR: a novel convolutional recurrent model for recognizing motion-based human activity 12

mHAR: a novel convolutional recurrent model for recognizing ...

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12th International Young Scientists Conference in Computational science, YSC 2023

作者： Kumar, Prabhat Suresh, S. Department of Computer Science Institute of Science Banaras Hindu University Varanasi221 005 India Department of Computer Engineering & Applications GLA University Mathura281406 India Department of Computer Applications National Institute of Technology Kurukshetra136119 India

With sensors built into smartphones and wearable devices, Human Activity Recognition (HAR) makes it possible to identify regular activities. One of the challenges is to access the motion information from activity data and extract the features from this information. A general model trained without motion features may perform poorly while recognizing the contextual patterns in human activities. In this paper, we proposed the mHAR (motion HAR) model for recognizing static, gradual, and dynamic motion activity to address this challenge. We examine the impact of the proposed model against the general non-convolutional and convolutional recurrent networks in terms of accuracy rates and F1 score. As an experimental dataset, the publicly accessible WISDM was employed. Additionally, the findings of the experiments demonstrate that, in contrast to the unbalanced class problem, motion features play a significant role in activity recognition by achieving accuracy rates of 92.29% and an average F1 score of 83.33%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Smartphones

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Fake News Detection Review: Concepts, Tools, Research gaps, and Possible solutions 7

Fake News Detection Review: Concepts, Tools, Research gaps, ...

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7th International Conference on Internet applications, Protocols, and Services, NETAPPS 2024

作者： Yakkundi, Sapana Patil, Rudragoud Goudar, Swetha Indudhar Department of Computer Science and Engineering KLS Gogte Institute of Technology Karnataka Belagavi India Department. of Master of Computer Applications KLS Gogte Institute of Technology Karnataka Belagavi India

ISBN: (纸本)9798331519513

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. The study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. The best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. The models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). This study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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