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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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Improving Obfuscated Malware Classification: A Machine Learning Approach with Feature Selection and Data Balancing

Improving Obfuscated Malware Classification: A Machine Learn...

引用

International Conference on Advanced computer Control, ICACC

作者： Gundu Bhavana Mokshagna Bhuvan Chowdary Pudota S Saravanan K Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a significant challenge due to its deceptive nature and the dynamic techniques used by attackers to obfuscate code. Existing approaches in malware analysis often struggle to accurately classify obfuscated malware, leading to potential security vulnerabilities and data breaches. In this research, we investigate the application of machine learning algorithms for malware analysis, specifically focusing on multi-class classification of obfuscated malware using the CIC-MalMem-2022, a latest dataset. We apply data preprocessing techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and normalization, to enhance the dataset's balance and consistency. Subsequently, we train and evaluate various machine learning algorithms, including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and Decision Trees (DT), on different datasets comprising SMOTE, normalized, and SMOTE+normalized instances. Our study also explores the effect of feature selection process on the performance of the model. We identify the top 35 features for optimal accuracy, addressing the dimensionality challenge inherent in large-scale datasets. Our findings reveal that the Random Forest (RF) model achieves the highest accuracy of 96.23% on the dataset that is balanced by SMOTE with top 35 features.

关键词： Radio frequency Accuracy Machine learning algorithms Scalability Nearest neighbor methods Feature extraction Solids Malware Random forests Testing

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End-To-End Encrypted Agriculture Chatbot

End-To-End Encrypted Agriculture Chatbot

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Pennabadi Devendra Reddy K Satya Sampath Reddy P Jayanth C R Kavitha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture sector is considered to be one of the most prominent sectors in the Indian economy. Farmers constantly face challenges such as a lack of information on best practices, disease detection, and market information. An End-to-End Agriculture chatbot can solve this problem by providing real-time information and support to farmers to enhance their productivity. This paper introduces a secure End-to-End Agriculture chatbot designed with strong encryption techniques to ensure data security. The proposed system will use the different asymmetric algorithm, a well-established public key cryptography method, to establish a safe and secure platform. Users sensitive data like soil reports and crop information will be encrypted using a widely available public key before sending it to the chatbot. After then, the chatbot uses its safe private key to decode the data. The asymmetric encryption method makes sure that the data being sent can only be accessed by authorized parties.

关键词： Productivity Crops Soil Chatbots Public key cryptography Agriculture Real-time systems Encryption Faces Diseases

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Phase Shift Signaling Reduction for IRS-Aided Internet of Things Networks by Exploiting Global Attention 23

Phase Shift Signaling Reduction for IRS-Aided Internet of Th...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yu, Xianhua Li, Dong Cao, Haotong Lin, Zhi An, Kang Wang, Jiangzhou Ai-Dhahir, Naofal Hong Kong Polytechnic University Department of Computing Hong Kong School of Computer Science and Engineering Macau University of Science and Technology China College of Electronic Engineering National University of Defense Technology Hefei China Sixty-third Research Institute National University of Defense Technology Nanjing China School of Engineering University of Kent Canterbury United Kingdom The University of Texas at Dallas Department of Electrical and Computer Engineering Richardson United States

ISBN: (纸本)9798350325959

The Internet of Things (IoT) has recently received considerable attention due to its ability to enhance high-data rate multiple access for massive IoT devices. However, these devices sometimes suffer from coverage and performance degradation when they are geographically far from each other. Moreover, as IoT devices often operate in power-saving mode, they require energy-efficient transmissions. In response to these challenges, the Intelligent Reflecting Surface (IRS) has been proposed and attracted increasing attention in recent years. Most existing researches on IRS-aided IoT networks assume that the IRS possesses full knowledge of the quantized phase shift (QPS). However, in practice, obtaining the optimized QPS poses significant challenges for the IRS, mainly due to constraints on signaling storm. To address the issue of QPS signaling overhead, we propose a novel deep learning (DL) model known as the global attention phase shift compression network (GAPSCN). Further-more, we introduce a global attention mechanism within the GAPSCN to enhance reconstruction accuracy. This mechanism exploits the correlation between the channel and spatial dimensions, emphasizing a larger number of meaningful features than in existing attention-based works. Simulation results demonstrate the effectiveness of the global attention mechanism compared to conventional attention mechanisms, as well as the proposed GAPSCN, which achieves reliable reconstruction performance relative to existing state-of-the-art compression models. © 2023 IEEE.

关键词： global attention Intelligent reflecting surface Internet of Things phase shift compression

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Integrating Deep Learning with the Gemini API for Improved Pest Management

Integrating Deep Learning with the Gemini API for Improved P...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： Milan Manoj Faraz Ihsan Devadath G K Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This study uses the Gemini API and deep learning algorithms to provide a novel approach to agricultural pest management. Pests pose serious difficulties to agriculture, thus prompt and precise pest management is crucial. The method combines the ability of the Gemini API to generate pertinent information on pesticides, home remedies, and nearby retail locations with deep learning models for pest classification. By using user-uploaded photos to identify pests, the deep learning model is taught to make recommendations for pest management tactics. The Gemini API also provides location-based information for nearby retailers carrying suggested products, as well as comprehensive details on safe pesticides and DIY cures. The strategy provides a comprehensive solution for agricultural pest management through integration, giving farmers practical insights for efficient pest control and increased crop output.

关键词： Deep learning Technological innovation Renewable energy sources Accuracy Pesticides Transformers Real-time systems Stakeholders Usability Sustainable development

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Revolutionizing Morse Code Security: Dual-Layer Encryption with AES and Machine Learning

Revolutionizing Morse Code Security: Dual-Layer Encryption w...

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Communication & computing (WCONF), World Conference on

作者： Samudrala Sai Santhoshi Haneesha Poluru Reddy Jahanve Siwani Karna Kavitha C. R. Department of Computer Science and Engineering Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Morse code is a method used in telecommunication to encode text characters as sequences of two different signal durations, called dots and dashes, or dits and dahs. It is a well-known, traditional method of communication that is frequently connected to radio transmission and telegraphy. In today's world, to protect sensitive data from cyber-attacks and maintain privacy and integrity in digital conversations, transactions, and storage, encryption and decryption are essential. These procedures provide security and build user confidence in this age of cybercrime by ensuring that their data is protected from unauthorized access. Advanced Encryption Standard (AES) is a popular encryption model which to date has been trusted for its effectiveness in safe-guarding sensitive data in digital communications and storage. AES Modern innovations also include other new mechanisms like - artificial intelligence (AI) to support pattern recognition, which later reduces the need for human decryption efforts for decryption and also improves the speed and efficiency of decryption of encrypted data. Various techniques such as artificial intelligence or machine learning are also used in decryption techniques. In the current interconnected digital environment, this integration contributes not only to enhancing the efficiency of encrypting but also allows for faster decryption of data for accurate and timely transmission of data. To make this research more realistic, data is transmitted between two systems using a wireless LAN connection. Besides this, the evaluation of these models is done based on the time taken for the encryption process and decryption process to ensure the efficiency is optimally vested in real life.

关键词： Wireless LAN Data privacy Telegraphy Technological innovation Codes Accuracy Machine learning Data models Encryption Wearable devices

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Deciphering Poxvirus Strains: Feature-Driven classification of Monkeypox

Deciphering Poxvirus Strains: Feature-Driven classification ...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Daripelli Mehul Sasikala. D Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

This research outlines a method for monkeypox virus classification from images, emphasizing the effective management of augmented datasets to ensure balanced class distribution and provide a robust foundation for model training. The study harnesses the capabilities of advanced pre-trained deep learning models, specifically ResNet50, EfficientNetB3, and DenseNet169, utilizing transfer learning techniques to adapt them to the task at hand. Techniques such as batch normalization and dropout are used to reduce overfitting and improve the generalization capacity of the models. Then, using the optimizer and categorical cross-entropy loss, these models are fine-tuned for monkeypox virus classification. One noteworthy addition is the inclusion of interactive learning, which gives users dynamic control over the training process by enabling them to intervene at predetermined intervals. Performance metrics such as accuracy, confusion matrix are used in the evaluation of the model. In order to guarantee a trustworthy evaluation of the models’ performance, an independent test dataset is utilized. The most impressive accomplishment is the ensemble learning method, which combines the predictions from all three models,ResNet50,EfficientNetB3 and DenseNet169 with a simple averaging procedure. The classification report shows that this all-encompassing methodology consistently produces excellent results, with precision, recall, and F1-score consistently exceeding 0.97. In a time when healthcare challenges are constantly changing, this method shows promise as a useful tool for image-based diagnosis.

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Investigating Fake Job Descriptions with TF-IDF and Word Embeddings

Investigating Fake Job Descriptions with TF-IDF and Word Emb...

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International Conference on computing and Networking Technology (ICCNT)

作者： B. Umah Deepa Gupta Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Fraudulent actions have increased as a result of the growth of online recruitment platforms, especially when it comes to the creation of false job descriptions intended to trick prospective employees. This study tackles the important problem of recognizing and categorizing fraudulent employment advertisements. By utilizing an extensive dataset of annotated fraudulent job descriptions, cutting-edge classification methods are utilized to construct a strong model that can discern between authentic and fraudulent job ***-IDF, a feature extraction method is used to assign numerical weights to words based on their frequency. Several word embeddings, such as BERT, GloVe, and Word2Vec, were employed in this study to enhance the job descriptions’ feature representation for fraud detection. To overcome data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is also used in combination with TF-IDF. 5-fold cross validation is applied to the different classifiers to study the performance of the system. By merging numerous embeddings and resolving class imbalance, this ensemble model demonstrated a significant boost in fraud detection accuracy over extended testing, demonstrating the usefulness of doing so for robust categorization of misleading job descriptions.

关键词： Accuracy Computational modeling Merging Employment Feature extraction Fraud Numerical models Testing Recruitment

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Auto-Scaling Anomaly Detection in Cloud computing Models using MAPE-K Loop

Auto-Scaling Anomaly Detection in Cloud Computing Models usi...

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International Conference on computing and Networking Technology (ICCNT)

作者： Nikhil Mahesh M Supriya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

computing paradigms are changing at a faster pace leading to highly complex and dynamic environments, thereby demanding resource management acumen. Pivoting in these paradigms, auto scaling ensures optimal resource allocation, sufficient to accommodate variable demands. However, the dynamic nature of such settings poses challenges in maintaining reliability and efficiency in auto-scaling methodologies. This paper proposal solves this challenge through a dependable anomaly detection system intended for auto-scaling in cloud computing frameworks. This work tries to design, develop, and validate the framework of anomaly detection for resource consumption and workload patterns using a MAPE-K loop. The envisioned framework is meant to use machine learning techniques and methodologies of data analysis to track in real time system parameters for the early identification of potential scalability issues. In terms of scores for all metrics, it has performed better with the MAPE-K loop, and the R2 score for all metrics came out to be 1.

关键词： Measurement Cloud computing Technological innovation Computational modeling Scalability Predictive models Real-time systems Resource management Reliability Anomaly detection

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Weather-Integrated Recommendations for Food Choices with SHAP Explanation

Weather-Integrated Recommendations for Food Choices with SHA...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Nikunj Jain Jawed Hawari Priyanshu Jha K Dinesh Kumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

In the context of today’s rapidly evolving technological landscape and the urbanization of Tier 1 and Tier 2 cities, cloud kitchens have emerged as pivotal players, offering quick and convenient meals without the need for dine-in facilities. The demand prediction for the food has become quite tough and could be considered as one of the significant challenges. Cloud kitchens face challenges in managing stock due to the absence of in-house diners, potentially resulting in wasted food. Moreover, adverse weather conditions often hinder the seamless delivery of online. To address these challenges, this project employs machine learning techniques to predict weather patterns and correlate them with food preferences according to that weather. The key algorithms found to deal with this problem were Random Forest, kNN, Decision tree and Naïve Bayes and Support Vector Machine. This serves the first half of the statement and then for recommendation a suitable system was developed. The pivotal role of the system is to reduce the food wastages for cloud kitchens by fostering adaptive strategies for efficient decision making. The key inputs from the market were found to be that 30-40% of the predicted sales in food industry doesn’t meet due to various reasons. The ultimate goal is to empower cloud kitchen operations, ensuring adaptive and informed decision-making in response to changing weather dynamics. The union of both of these are explained using Interpretability tool SHAP for a convenient study.

关键词： Support vector machines Machine learning algorithms Clouds Urban areas Decision making Forestry Prediction algorithms

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UNNATI: Enhancing Quality Education in Rural Areas through AI, AR & digitalization

UNNATI: Enhancing Quality Education in Rural Areas through A...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： A Padmavathi Bikrant Pandit Gungun Khaitan Sandesh Varma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Quality and accessible education is a basic human right. Article 26 of the Universal Declaration of Human Rights (UDHR) declares that everyone has the right to proper and excellent education. It plays an integral part in an individual’s empowerment, economic as well as social progress. However, the educational system in rural areas is still in developing and deprived state. It leads to a significant disparity in terms of accessing standard education and available opportunities as compared to urban regions. Despite several global efforts, statistical data reveals a huge difference in the quality of education and opportunities between urban and rural regions. Hence, our paper presents an innovative approach to enhance the overall quality of education and provide practical learnings and opportunities in rural areas. The proposed solution leverages AI, Augmented reality (AR) and digitalization to enhance hands-on and interactive learning experiences for the rural students. It also uses Augmented Reality (AR) to address issues like agricultural education, an important aspect of rural life.

关键词： Economics Visualization Electronic learning Education Supply chains Crops Information and communication technology Artificial intelligence Augmented reality Standards

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