Can anyone assist with a novel problem for this generic topic Cybersecurity issues and solutions for Big Data systems in healthcare

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These topics are generic, and you must produce a focused presentation on a current specific research problem in one of these general areas, in the form of an A1 poster. Your poster should take the form of an Infographic. Note: If the software you use is unable to create an A1 poster, then create the closest you can to A1 size. To do this, you are required to consult current research articles (i.e., journal and conference papers

i am a data analytics student and dont know much about cybersecurity
Apr 19 in Cyber Security & Ethical Hacking by anish
• 400 points
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1 answer to this question.

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Not a problem! I can suggest a specific research problem in the field of data analytics that you can create an A1 poster on.

One current research problem in data analytics is anomaly detection. Anomaly detection refers to the identification of unusual or abnormal data points within a dataset. This can be useful in a variety of industries, such as finance, healthcare, and manufacturing.

One particular approach to anomaly detection that has gained popularity in recent years is deep learning. Deep learning is a subfield of machine learning that involves the use of artificial neural networks to model complex patterns in data.

Your poster could focus on the following aspects of anomaly detection using deep learning:

  1. Overview of Anomaly Detection: Explain what anomaly detection is and why it is important.

  2. Deep Learning for Anomaly Detection: Provide a brief overview of deep learning and its potential benefits for anomaly detection.

  3. Common Deep Learning Models for Anomaly Detection: Describe some of the most common deep learning models used for anomaly detection, such as Autoencoder, LSTM Autoencoder, and Variational Autoencoder.

  4. Dataset Selection: Discuss the importance of selecting the appropriate dataset for deep learning-based anomaly detection, including considerations such as data size, data quality, and data variety.

  5. Evaluation Metrics: Explain how the performance of deep learning-based anomaly detection models can be evaluated, including metrics such as precision, recall, and F1-score.

  6. Case Study: Provide a real-world example of deep learning-based anomaly detection, including a description of the dataset, the deep learning model used, and the evaluation metrics used to assess model performance.

  7. Future Research Directions: Discuss potential future research directions for deep learning-based anomaly detection, including the use of other deep learning models and the development of more sophisticated evaluation metrics.

Overall, your poster should aim to provide a comprehensive overview of deep learning-based anomaly detection, including its benefits, challenges, and potential applications.

answered Apr 19 by Edureka
• 12,530 points

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