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Identification Of Users with Social Behavioural Biometrics On Twitter

Identification Of Users with Social Behavioural Biometrics O...

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international conference on Computing and Networking Technology (ICCNT)

作者： Anudeep Gandla Vamshi Sunku Mohan Sriram Sankaran Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India

Online Social Network (OSN) platforms provide a valuable source of information, which includes user profiles and social connections that can be used to develop behavioural models of individuals. Tweets of four Twitter users are analysed and a method is proposed to identify users. the Twitter text data is pre-processed to get meaningful words to extract features of every user. Term Frequency-Inverse Document Frequency (TF-IDF) is employed to eliminate features with lower weights, extract essential features from the text, and retrieve the corresponding word vectors using a pre-trained Word2Vec model. these word vectors are input to Convolutional Neural networks (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM for classification to generate a model to predict the user. the experimental results achieved an accuracy of 64.41% in identifying a user in a particular category. When the behavioural patterns of users change over time, it could be challenging to develop an accurate model calling for frequent updating of models. the proposed model can identify users on social media platforms by analyzing behavioural patterns from text data. this can enhance the security and trustworthiness of social media platforms. It can also identify attempts at impersonating other users by analysing behavioural patterns.

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

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international conference on communication systems and Network Technologies (CSNT)

作者： Shaharyar Alam Ansari Salman Ali Mohammad Luqman Shahnawaz Ahmad School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the Internet of things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. the research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. the proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. the initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). the system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance.

关键词： Smart agriculture Ciphers Recurrent neural networks Elliptic curve cryptography Sensor systems Encryption Internet of things Data communication Intelligent sensors Monitoring

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MIMO Smartphone Antenna with CPW-Fed Stepped Resonators for sub 6 GHz Cellular networks

MIMO Smartphone Antenna with CPW-Fed Stepped Resonators for ...

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Electrical Engineering (ICEENG), 12th international conference on

作者： Haleh Jahanbakhsh Basherlou Naser Ojaroudi Parchin Libu Manjakkal Chan Hwang See Ahmed Sayed Ismail Amar Ahmed Gamal Salama Mohammad Darwish School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Electronics and Communication Air Defense College Alexandria University Alexandria Egypt Electronics & Communications Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Radar Department Radar Systems Research Center Military Technical College Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

the imperative for cutting-edge multi-port MIMO antennas has surged, driven by the evolving demands of advanced technologies, notably in the context of 5G networks and beyond. this paper introduces a new MIMO antenna utilizing Coplanar Waveguide (CPW) fed resonators, exhibiting desirable characteristics for sub-6 GHz applications. the MIMO array comprises eight stepped-ring slits etched in the same layer as the ground plane, operating within 3.2-3.7 GHz, aligning with the sub-6 GHz cellular bands. Its compact footprint allows seamless integration into smartphones, offering a practical solution for space-constrained devices. Despite its size, the MIMO antenna demonstrates satisfactory performance across critical parameters.

关键词： Cellular networks Performance evaluation Electrical engineering Resonant frequency Bandwidth Coplanar waveguides Resonators

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Optimized Power Quality Monitoring in Electrical systems Using Adaptive Recurrent Neural networks

Optimized Power Quality Monitoring in Electrical Systems Usi...

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international conference on communication systems and Network Technologies (CSNT)

作者： R Muthu Kumar Vaishnavi K Swetha R Seenivasan R Praveenkumar M Department of Electrical and Electronics Engineering Erode Senguthar Engineering College Erode India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the reliable and efficient delivery of electrical power is crucial in modern societies, where a wide range of industries, infrastructure, and everyday life depend on a stable and high-quality power supply. However, the increasing complexity and integration of electrical systems, coupled with the growing use of power electronics and non-linear loads, have led to the emergence of various power quality disturbances that have significant impacts on the performance and lifespan of electrical equipment. To overcome these issues this proposes a leveraging adaptive RNN (Recurrent Neural Network) for enhanced detection of power quality disturbances in electrical systems. the methodology begins with comprehensive data collection from various power system sources to capture a wide range of disturbance patterns. Preprocessing techniques, including data cleaning and remove null values, are applied to enhance data quality and ensure accurate feature extraction. Data analysis and exploration then provide insight into underlying trends and characteristics within the dataset, facilitating the selection of relevant features. these features are then split into training and test datasets to build a robust predictive model. An adaptive RNN model is utilized to detect and classify disturbances, leveraging its ability to handle sequential data efficiently. the model’s performance is assessed through python, followed by accuracy of 91% and F1 Score of 0.89 evaluation to validate its reliability. this approach ensures an effective and adaptive solution for real-time PQDS monitoring, thereby enhancing the resilience and reliability of power systems in dynamic operating environments.

关键词： Adaptation models Recurrent neural networks Adaptive systems Accuracy Power quality Power system dynamics Power system stability Real-time systems Power system reliability Monitoring

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BIOINFORMATICS 2021 - 12th international conference on Bioinformatics Models, Methods and Algorithms;Part of the 14th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2021

BIOINFORMATICS 2021 - 12th International Conference on Bioin...

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12th international conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2021 - Part of the 14th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2021

ISBN: (纸本)9789897584909

the proceedings contain 26 papers. the topics discussed include: coordinate systems for pangenome graphs based on the level function and minimum path covers;the extension of the standard genetic code via optimal codon blocks division;backward pattern matching on elastic degenerate strings;TotemBioNet enrichment methodology: application to the qualitative regulatory network of the cell metabolism;a deep learning method to impute missing values and compress genome-ide polymorphism data in rice;flower pollination algorithm for detection of epistasis associated with a phenotype;possibilities of using neural networks to blood flow modelling;comprehensive statistical analysis on estimated errors of Averagine model for intact proteins;machine learning algorithms for predicting chronic obstructive pulmonary disease from gene expression data with class imbalance;and machine learning studies of non-coding RNAs based on artificially constructed training data.

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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international conference on communication systems and Network Technologies (CSNT)

作者： Mahfooz Alam Zubair Ashraf Priya Singh Bishwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. this paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. the goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services Real-time systems Security Long short term memory

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On Smart Greenhouse Issues 21st

On Smart Greenhouse Issues

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21st international conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and systems, NEW2AN 2021 and 14th conference on Internet of things and Smart Spaces, ruSMART 2021

作者： Schneps-Schneppe, Manfred Lacis, Gunars Ventspils International Radio-Astronomy Centre Ventspils University of Applied Sciences Ventspils3601 Latvia Institute of Horticulture Latvia University of Life Sciences and Technologies Dobele3701 Latvia

ISBN: (纸本)9783030977764

Rapid climate change, the explosion of the population and the reduction of arable land call for new approaches to ensure sustainable agriculture and food supply in the future. Emerging Internet of things (IoT) technologies, which include smart sensors, devices, network topologies, big data analysis and smart decision making, are considered to be the solution to the key challenges facing greenhouse farming. Under the IoT greenhouse environment, numerous sensors and actuators can be utilized for connection throughout the greenhouse, capable of monitoring and detecting the change in the environment. the readings from these sensors can be used in analytics and providing to supervision applications. the paper shows an architecture of mesh network for greenhouse IoT system. A Biene Electronics platform embedded with GSM 2.5G receiver and microcontroller units serves as a local host in the network. Smart greenhouse basics are explained and some emergency issues pointed out. Exampled by plant disease detection, the question: what a method is more efficient - discriminant functions or neural networks, is discussed. In conclusion, some future works are mentioned, mainly relating IoT software. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Home Appliances using loT and Machine Learning: the Smart Home

Home Appliances using loT and Machine Learning: The Smart Ho...

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international conference on Computational Intelligence and communication networks (CICN)

作者： KRITIKA RAthI VANSHIKA SHARMA SWATI GUPTA ASHISH BAGWARI G. S. TOMAR Department OF Electronics and Communication Engineering Women Institute of Technology Uttarakhand Technical University Dehradun INDIA Rajkiya Engineering College Sonbhadra U.P. INDIA

ISBN: (纸本)9781665487726

the term Home Automation describes the technologies used in the home for the essential needs like interconnection, wireless communication and making the quality of the component to use in a smart way just through the internet of things. the term HOME AUTOMATION indicates the quality of the automatic working of the systems and controlling of the different variety of home appliances. In the information communication technology, cloud computing and the IOT are the major types of services used in the model for the advancement of the new generation in which these two are making a great influence for making and deploying new applications. On the other hand, the interfaces between the hardware component, communication, and the programming are a main application for the technology used in the home which works to combine every device through the internet or Wi-Fi. the every device used in the model is connected to the Wi-Fi due to which we can get the output through our mobile phones, it doesn't depends whether we are at home or at the working place or anywhere else in the world, the app in the mobile phone shows the update information of our lights, motor, induction and also face recognize for the safety purpose in the front door.

关键词： Wireless communication Home appliances Cloud computing Face recognition Mobile phones Sensor systems and applications Internet of things

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Realistic Face Masks Generation Using Generative Adversarial networks

Realistic Face Masks Generation Using Generative Adversarial...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Khaled Al Butainy Muhamad Felemban Hamzah Luqman Computer Engineering Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia

ISBN: (纸本)9781665487726

Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. the MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. the model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.

关键词： Deep learning COVID-19 Emotion recognition Computational modeling Generative adversarial networks Face detection communication networks

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Detection and Prevention of Advanced Persistent threat (APT) Activities in Heterogeneous networks using SIEM and Deep Learning

Detection and Prevention of Advanced Persistent Threat (APT)...

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international conference on Computing and Networking Technology (ICCNT)

作者： V S tharunika Shridhar T K Veeresh Senthil Kumar thangavel Karthik Srinivasan Sulakshan Vajipayajula Anjali Tibrewal Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India IBM Security International Business Machines India Pvt. Ltd.

Distributed Denial of Service (DDOS) in IOT based systems has become a vital consideration especially since the quintessential development of IOT based gadgets including small Personal Digital Assistants (PDA) to large computing systems. the stupendous and prolonged presence of such innovations have attracted potential attackers, encouraging them to carry out cyber-attacks and data theft. the key objective of this research is to detect and mitigate botnet-based distributed denial of service (DDoS) attacks in IoT networks. Our proposed model addresses the issue concerning threats from bots. Multiple machine learning models such as K-Nearest Neighbour (KNN), K-Means clustering, Fuzzy clustering and Deep learning models such as Conventional Neural Network (CNN), Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) were used to arrive at the model, wherein the model is trained by Bot-Iot dataset. Various preprocessing methods were equipped and Performance-based selection was done to choose algorithms from a collection using the reference point, (i.e) accuracy percentage. Feature selection and Synthetic minority oversampling technique (SMOTE) were also incorporated alongside Machine Learning (ML) algorithms. the results of the model indicates that the proposed architecture can effectively detect botnet-based attacks and also can be extended with corresponding architectures for unfolding attacks.

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