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Application of Hybrid Approach towards Multi Aspect Classification and Analysis of Malware 21

Application of Hybrid Approach towards Multi Aspect Classifi...

引用

21st OITS International Conference on Information technology, OCIT 2023

作者： Das, Debashreet Satapathy, Shashank Mouli Ajitha, D. Agarwal, Aman Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350358230

Malicious software is what the term "malware"refers to. Malware is any type of software or document that compromises the security of a computer's operating system, data, or both. These malicious applications are built to carry out a wide range of actions, including monitoring user activities on the computer without their consent, stealing, encrypting, deleting sensitive data, and altering key operating system computational capabilities. Trojan Horses, Ransomware, Spyware, Worms, and Viruses are the most frequently available types of malware that attackers utilize to harm the target machines. Using various static and dynamic analysis tools, malware analysis is the process of aggregating metadata about the malware, such as its behavior, signatures, etc. In this study, we've put forth a method that combines statically and dynamically analyzing malware files using a Python-based model. Generally, a variety of tools are utilized to analyze specific types of malware. This method analyses the numerous static and dynamic aspects of practically all varieties of malware in order to make it convenient © 2023 IEEE.

关键词： Static analysis

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Correlation based Feature Selection and Hybrid Machine Learning Approach for Forecasting Disease Outbreaks 4

Correlation based Feature Selection and Hybrid Machine Learn...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bhunia, Swayon Abirami, S. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350300093

According to WHO, Dengue is a viral infection transmitted to humans through the bite of infected mosquitoes i.e., Aedes aegypti mosquitoes. There is currently no known cure for dengue or severe dengue. Artificial Intelligence (AI) in the form of Machine Learning (ML) allows software programs to predict outcomes more correctly without explicit instructions. Machine learning algorithms use historical data as input to forecast new output values. The aim of this study is to identify, evaluate and interpret suitable hybrid algorithms/approaches relevant to the application of machine learning in limiting the spread of deadly disease outbreaks. It focuses on finding a way of predicting the next dengue fever local epidemic by comparing the bench mark approaches available until now. For this the study proposes the use of XGBoost coupled with Moving Average Rolling Features in order to learn the long-term temporal relations in the features to get accurate predictions. The dataset used for evaluating the proposed approach contains number of cases in the two locations: San Juan and Iquitos and it includes information on temperature, precipitation, humidity, vegetation, and what time of the year the data was obtained. A correlation analysis-based feature selection along with Moving Average Rolling Features has been used for getting more precise data implemented with ML approach resulting in MS E 11.37 in San Juan and MSE 6.37 in Iquitos. © 2023 IEEE.

关键词： Forecasting

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Resource Scheduling and Load Balancing Algorithms in Cloud Computing 3

Resource Scheduling and Load Balancing Algorithms in Cloud C...

引用

3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Vijay, Rahul Sree, Thankaraja Raja School of Computer Science and Engineering Vellore Institute of Technology Chennai India

A technology called cloud computing makes it possible to offer computing resources including storage, software, and services over the internet. Users can access these resources whenever they need them and just pay for what they actually use. There is a strict need to maintain track of all the resources which are being used by machines to perform certain activities such as scheduling of tasks. Resource scheduling and load balancing are important techniques used in modern computing systems to improve performance and resource utilization. Resource scheduling algorithms are used to allocate resources such as CPU, memory, and storage to different tasks or processes. Load balancing algorithms, on the other hand, distribute incoming traffic or workloads across multiple servers or resources to ensure that no single resource becomes overloaded. These techniques are essential for maintaining the stability and efficiency of large-scale systems, such as cloud computing environments, web services, and distributed systems. There are various kinds of load-balancing algorithms that differ in terms of their complexity, flexibility, and performance. Selecting the right algorithm for a specific system depends on the system's specific requirements and constraints. Overall, resource scheduling and load-balancing algorithms play a crucial role in ensuring the smooth operation of modern computing systems and are an active area of research and development. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cloud computing

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SQL Injection and Its Detection Using Machine Learning Algorithms and BERT 3rd

SQL Injection and Its Detection Using Machine Learning Algor...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Lodha, Srishti Gundawar, Atharva Department of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031289743

SQL Injection attacks target the database of applications to extract private information or inject malicious code. In this paper, we attempt to present a well-researched and practiced methodology to detect SQL Injection attacks accurately. These kinds of attacks are a very common means of network security attacks which can cause inestimable loss to the database. Building measures against them is a current research hotspot. Considering the possible complexity of queries involved and the need for a quick and efficient detection system in place, turning to machine learning techniques to combat and detect such attacks is the right choice. This is why we have undertaken the task of analyzing a number of machine and deep learning algorithms on a vast dataset of 41,770 points (consisting of both malicious and normal queries). We aim at finding a system that is accurate and fine-tuned for the best possible results and test each of the algorithms on various performance metrics to identify the one that performs the best. BERT outperforms the rest with a validation accuracy of 99.98%. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Learning algorithms

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Network Intrusion Detection Using Machine Learning 1

引用

Virtual International Conference on Futuristic Communication and Network Technologies, VICFCNT 2021

作者： Prajapati, Pratik Kumar Singh, Ishanika Subhashini, N. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9789811983382

ISBN: (纸本)9789811983375

With the rapid growth in the use of computer networks, the issues of maintaining a network also increase. Moreover the attackers keep changing their tools and techniques. With this the necessity to adopt the right type of intrusion detection system (IDS) increases. An IDS monitors the network for suspicious activity and the computer network is protected from unauthorized access. In the proposed work, a network intrusion detection system was developed using various machine learning classifiers on the KDD99 data set, which is a predictive model that can distinguish between intrusions and normal connections. In the early stage of our implementation, we found the correlation between the attributes and further mapped the classification characteristics as part of data pre-processing. Subsequently, we implemented several models, such as Naive Bayes, Decision Tree, Random Forest, Support Vector Classifier, Logistic Regression and Ensemble Voting Model. All the models were analysed based on their scores on metrics such as accuracy, recall, f1-score and precision along with training and testing times. The implemented models demonstrated that Naive Bayes classifier achieved the lowest accuracy, whereas Random Forest had the highest accuracy and best scores on all the metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Smart Traffic System with Green Time Optimization Using Fuzzy Logic 1st

Smart Traffic System with Green Time Optimization Using Fuzz...

引用

1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Kavin, A.K. Radha, R. Prasad, Vishnu Murali, Bharathwaj School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031350771

Traffic Congestion has been a very big issue due to the large number of vehicles being utilized in cities. It is necessary to control the number of vehicles getting in and out of the crossing such that the city traffic congestion decreases in an efficient way. In this paper, we try to improvise the timings of green signals using fuzzy logic based on the number of vehicles, speed limit, and length of the crossing. We find out the number of vehicles using computer vision dynamically. We validate our system in two different locations in Chennai against the manual green signal duration that is set manually and we prove that our method improves the waiting time significantly. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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Real-time CCTV Footage Violence Detection with Alarm System using Deep Learning 6

Real-time CCTV Footage Violence Detection with Alarm System ...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Nivedita, M. Pawar, Rohan Kathuria, Hardik Rateneshwar Siddiqui, Ibrahim Ahmad Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

The increasing proliferation of surveillance cameras to keep an eye on human behavior has led to a rise in the need for an automated system that can identify violent incidents and suspicious activities. In smart buildings, this technology is mandatory. Violence Detection in real time will prevent any mishappenings from taking place by raising alerts to the security guards. Our model also has YOLO object detection inculcated in it in order to recognize any weapons or tools that an attacker may be using against people and the security guards will act *** this paper, we have utilized deep learning model and techniques to create a lightweight system for detecting violence that is fully automated and can act as an assistant for law enforcement officers, and can also be deployed in public areas as well as corporate buildings. When the system detects violence, it rings an alarm which can be used to alert security personals that some commotion may be happening in certain area. © 2023 IEEE.

关键词： Deep learning

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Enhancing Cryptanalysis of DES Encryption using Neural Networks and Firefly Algorithms

Enhancing Cryptanalysis of DES Encryption using Neural Netwo...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Balaji, Varshini Krishnamurthy, Vallidevi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350313413

In today's cryptographic landscape, evaluating encryption algorithms' integrity is crucial. Neural networks offer promise for extracting information from Data Encryption Standard (DES) encrypted data. This report explores diverse methods of DES attacks using deep learning techniques. The primary aim is to decode DES-encrypted data with neural networks, extending this to contemporary encryption algorithms. The proposed system uses Long Short-Term Memory (LSTM) networks to improve plaintext reconstruction from DES-encrypted data. It evaluates LSTM network performance metrics and seeks to predict critical patterns. Findings reveal the neural network's impressive ability to predict plaintext with 98% accuracy after 250k training iterations. However, performance varies with the encryption key, and complete plaintext recreation remains challenging. This method also experiments using the Firefly algorithm to enhance key prediction, improving overall efficiency. Artificial Neural Networks (ANNs) for classification tasks on encrypted data show promise for practical applications. Future work can focus on enhancing classification accuracy, exploring diverse data types, and studying the critical impact on performance metrics. © 2023 IEEE.

关键词： Backpropagation Cryptanalysis Cybersecurity Data Encryption Standard - DES Attacks Encryption Algorithms Long Short-Term Memory (LSTM) Neural Networks

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Facial Emotion Depth Index 1

Facial Emotion Depth Index

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Jagadish, Tarun Shabrez, Md Robert, V. Noel Jeygar Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Conventional methods for emotion detection typically entail classifying emotions into predefined categories, potentially yielding restricted and imprecise depictions of an individual's emotional disposition. In this research endeavor, we embark on a fresh approach aimed at discerning emotions and gauging their intensity through the utilization of an innovative emotion depth index. By forecasting continuous values that mirror the magnitude of emotion, this methodology affords a heightened level of precision and a more intricate comprehension of an individual's emotional state. Furthermore, this study delves into the potential real-world applications of this technology, spanning from psychological counseling and customer service to market research. Our findings strongly indicate that the emotion depth index bears the promise to revolutionize the landscape of emotion detection systems, presenting a more precise and finely tuned portrayal of an individual's emotional condition. © 2023 IEEE.

关键词： Data Analytics

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A Comparative Study on Paddy Disease Classification Using Machine Learning 6

A Comparative Study on Paddy Disease Classification Using Ma...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Ilakiyaselvan, N. Dhandapani, Aarthi Thomas Abraham, J.V. Sathyarajasekaran, K. Sherwin, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

In India, rice is grown in a diverse range of environmental conditions and is a significant source of energy and protein for people globally, accounting for 21% of per capita energy and 15% of per capita protein. India ranks second in terms of both area and production among the major rice-producing countries in the world. It produced 124.37 million tons of rice from 44.5 million hectares during the year 2020-2021, which is a massive portion of the 782 million tons of rice produced globally from 167.1 million hectares. Diseases that impact crop quality and yield are a problem for paddy farmers. Diseases that impact crop quality and yield are a problem for paddy farmers. Low production rates are caused by problems such as a lack of professional availability, ignorance about diseases and pests, and a lack of expertise in fertilizer management. By using captured images of infected leaves, farmers can quickly identify diseases and take appropriate action to prevent crop loss. Various image processing and machine learning algorithms have been developed to diagnose diseases in rice plants, and this study examines the different techniques used for feature extraction and classification in disease identification. © 2023 IEEE.

关键词： Image classification

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