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A Multi-Functional Web Tool for Comprehensive Threat Detection Through IP Address Analysis

A Multi-Functional Web Tool for Comprehensive Threat Detecti...

引用

2024 IEEE Conference on engineering informatics, ICEI 2024

作者： Tanan, Cebajel Kulkarni, Sameer G. Das, Tamal Hanawal, Manjesh K. Indian Institute of Technology Computer Science and Engineering Department Dharwad India

ISBN: (纸本)9798331505776

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we have noticed the lack of one-stop, free, and open-source tools that can analyse IP addresses. Hence, this work introduces a comprehensive web tool for advanced IP address characterisation. Our tool offers a wide range of features, including geolocation, blocklist check, VPN detection, proxy detection, bot detection, Tor detection, port scan, and accurate domain statistics that include the details about the name servers and registrar information. In addition, our tool calculates a confidence score based on a weighted sum of publicly accessible online results from different reliable sources to give users a dependable measure of accuracy. Further, to improve performance, our tool also incorporates a local database for caching the results, to enable fast content retrieval with minimal external Web API calls. Our tool supports domain names and IPv4 addresses, making it a multi-functional and powerful IP analyser tool for threat intelligence. © 2024 IEEE.

关键词： Internet protocols

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Breast Cancer Segmentation Using UNet and Global Convolutional Networks 27th

Breast Cancer Segmentation Using UNet and Global Convolutio...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Thyagachandran, Anand Ahmed, Yeruru Asrar Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9783031782008

Breast ultrasound (BUS) imaging techniques have become efficient tools for cancer diagnosis. Convolutional neural network (CNN) based encoder-decoder architectures have been widely used for the automated segmentation of tumours in BUS images, assisting in breast cancer diagnoses. However, these models have limitations in capturing long-range dependencies. To overcome this limitation, various deep learning techniques, such as atrous convolution, attention mechanisms, and transformer encoder-based models, have been introduced to capture long-range dependencies in feature maps, improving segmentation accuracy by considering larger receptive fields and global context. As modelling techniques evolve, there is a shift towards more complex and intricate designs. This study proposes a simple yet effective model that combines UNet and Global Convolutional Network (GCN) architectures for breast lesion segmentation. By leveraging the GCN block, our model captures broader receptive fields with a simpler design strategy. We have demonstrated the efficacy of our approach through various experiments, including kernel size analysis, model component evaluation, and data preprocessing assessment. The proposed model has been evaluated using four-fold cross-validation with BUSI and Dataset-B datasets. Additionally, models trained on both datasets have been validated with a blind test dataset, where our model demonstrates better performance compared to state-of-the-art methods, achieving a 4.9% and 6.7% improvement in Intersection over Union (IoU) score, respectively. The robustness analysis and external validation experiments underscore the superior generalization performance of our model in breast lesion segmentation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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SmartAudit: Smart Contract Vulnerability Detection Using Transfer Learning 10th

SmartAudit: Smart Contract Vulnerability Detection Using Tra...

引用

10th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2024

作者： Jain, Ankur Tripathy, Somanath Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819637737

Ethereum being a dominant player in decentralized finance, invites a vast number of smart contracts annually. Though smart contracts play a crucial role in developing decentralized applications that are safe, secure, and efficient, Ethereum lost a huge amount due to various attacks caused by different vulnerabilities within the smart contracts. Therefore, it is crucial to audit the reliability of smart contracts before their deployment. This paper proposes SmartAudit which identifies whether the smart contract is vulnerable. SmartAudit employs BERT (Bidirectional Encoder Representations from Transformers) to identify vulnerabilities in unseen smart contracts. It utilizes optimized opcode sequences derived from a pruned control flow graph as input. The results show that SmartAudit significantly improves accuracy, precision, recall and F1-score compared to the state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Machine Learning based Smart Crop Recommendation System 4

Machine Learning based Smart Crop Recommendation System

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shwetha, A.N. Prabodh, C.P. Siddaganga Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349900

Many challenges are faced by agricultural sector due to various dependent factors like changes in climatic conditions, variation in soil properties and farming practices. What to plant in their agricultural land is the common question for all farmers. Many farmers are failing to identify the crop that suits their agricultural land to get good yields. This results in following the same methods and crops what their ancestors were following. The crop yield always depends on multiple parameters like soil properties, climate conditions, water level etc. So if the farmers get suggestions regarding which crop to grow in their agricultural land would be of great help. The government is also helping farmers in many ways, one such scheme is soil testing. Soil testing is very important because through testing we can identify nutritional level, pH level and moisture level of soil which can optimize crop prediction. To address the above mentioned issues, this paper proposes a Crop Recommendation System(CRS) to help farmers based on machine learning algorithms to take wise decision to select a crop for their agricultural land. The CRS considers historical data, soil type, pH level, season, water level and amount of pesticides to provide crop recommendation for agricultural plots. The CRS system uses Naive Bayes algorithm to select the crop suitability. The CRS is a user friendly system that can be accessed through web or mobile. The user can give necessary input regarding their agricultural land and get crop recommendation which suits their farming land. The proposed system aims to improve crop yield, minimize resources and environmental impact. © 2024 IEEE.

关键词： Smart agriculture

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Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Model

Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Mo...

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

作者： Sasidaran, Keerthana Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

Hate speech detection is crucial for secure online environments, especially with recent policy changes on social media. This study enhances the Llama 2 model by fine-tuning it with a comprehensive Twitter dataset to improve hate speech detection accuracy. The system's frontend allows users to upload or record audio, upload videos, or provide YouTube URLs, with processing managed by the Google Speech Recognition API and the Whisper library. The backend integrates advanced speech recognition and video processing technologies. The refined Llama 2 model achieved an F1 score of 0.94, with accuracy, precision, and recall metrics surpassing other state-of-the-art models. These results demonstrate the model's high effectiveness and reliability in hate speech detection. This research highlights the potential of transformer-based models in enhancing online safety and inclusivity. Future work will focus on expanding the dataset, improving model generalization, supporting additional languages, and enhancing system scalability to better handle evolving hate speech forms. © 2024 IEEE.

关键词： Social networking (online)

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Predictive Modeling of Performance Variability in HPC Applications 31

Predictive Modeling of Performance Variability in HPC Applic...

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sahu, Pratham Malakar, Preeti Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798331509118

Performance variability is common in HPC applications, due to factors such as job scheduling, resource contention, and network traffic. We propose a machine learning-based model to predict performance variability in high performance computing applications. Our model integrates classification and regression techniques to identify jobs with high variability and accurately predict their performance fluctuations. The model incorporates job-specific and system-wide factors such as network counters to enhance prediction accuracy. © 2024 IEEE

关键词： Prediction models

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A New Approach for Prevention of Malicious Attack in Post Disaster Using Delay Tolerant Network

A New Approach for Prevention of Malicious Attack in Post Di...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chakrabarti, Chandrima Bal, Saptarshi Jaiswal, Rishabh Ansari, Arbaz Akhtar Narula Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350375237

Natural disasters still have a crippling effect on our infrastructure notwithstanding society's development. Communication systems, the backbone of our network, sustain the most damage during such crises. When it comes to setting up working communication systems, volunteers working on disaster relief missions face enormous obstacles because of the deteriorating communication infrastructure and extended power outages. Smartphones have become essential tools because they may create a peer-to-peer network, in opportunistic way also called as a Delay Tolerant Network (DTN), through Bluetooth or Wi-Fi connections in a latency mode, which makes communication easier in post-disaster situations. Nevertheless, nodes in this network can act strangely, failing to create routes or send data to other nodes, which would hinder the functioning of the network as a whole. Malicious nodes are those that behave in this way. They have the ability to either deny or destroy messages, which can cause serious disruptions. The performance of the network may be further complicated by some nodes displaying partial malicious activity, which causes interference on a selective basis. In response to these difficulties, this work suggests a novel technique for identifying and mitigating the effects of malicious nodes coordinating assaults inside DTNs. Through simulations with the Opportunistic Network Environment (ONE) simulator, the efficacy of the scheme was assessed, demonstrating its ability to reduce the disruptive impact of malicious nodes on network operations. This suggested method shows promise in strengthening DTNs against malevolent attacks and promoting more dependable communication infrastructures in places affected by disasters. © 2024 IEEE.

关键词： Delay tolerant networks

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Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking 38

Pedestrian Trajectory Prediction with Missing Data: Datasets...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chib, Pranav Singh Singh, Pravendra Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://***/Pranav-chib/TrajImpute. © 2024 Neural information processing systems foundation. All rights reserved.

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Classification of Arrhythmias 12-Lead ECG Signals Based on 1 Dimensional Convolutional Neural Networks

Classification of Arrhythmias 12-Lead ECG Signals Based on 1...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Kurniawan, Arief Triwibowo, Bayu Aditya Fandiantoro, Dion Hayu Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Indonesia

ISBN: (纸本)9798350364101

Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., someone who does excessive activity has a faster heart rate. Then, a diagnosis is needed to classify arrhythmias. One method used is ECG (Electrocardiogram) signal analysis. The ECG signal consists of P, QRS Complex, and T waves. The morphology of the QRS is used for arrhythmia classification. Currently, cardiologists analyze ECG signals by observing directly. This method is depending on the level of expertise of the cardiologist. Previous research classified arrhythmias based on the QRS morphology from a single ECG lead. As 12-lead ECG devices have now become standard in ECG examinations because abnormalities can be observed from multiple angles. This study proposes the classification of arrhythmias in 12-lead ECG signals based on the morphology of QRS complex waves using a deep learning 1-dimensional Convolutional Neural Network. The output of deep learning is the classification of arrhythmias into four classes, namely: Normal, Right Bundle Branch Block, Premature Ventricular Contraction, and Atrial Premature Beat. The outcome of the proposed system is that each QRS segment is used as input for deep learning, which can improve classification performance compared to the classification carried out by each lead. The experimental results show the method can be done well, with an average Accuracy, Precision, Sensitivity, and F1-Score were 98.8%, 99.2%, 99.2%, and 99.2%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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