Nihar Mudigonda - Academia.edu (original) (raw)

Nihar Mudigonda

Self-motivated high school Junior with a keen interest in Artificial Intelligence and Machine Learning. My goal is to leverage my passion and creativity to solve real-world problems and contribute to making a positive impact on society.

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Papers by Nihar Mudigonda

Research paper thumbnail of Finding Relationship Between Alzheimer's Disease and Noise in Electroencephalogram (EEG) Data Using Novel Machine Learning (ML) Algorithms

Research paper thumbnail of A Method For Network Intrusion Detection Using Deep Learning

Journal of Student Research

In an increasingly digitally reliant world, organizations are facing the ever more challenging pr... more In an increasingly digitally reliant world, organizations are facing the ever more challenging problem of how to best defend their digital information and infrastructure. Current non-machine learning methods for detecting network intrusion, like signature-based and anomaly-based algorithms, are slow and unreliable. Signature based detection holds signatures, or known information and warning signs, about a known attack and compares them to the current flow of data. If a signature matches with the network activity, users and network administrators are notified. Anomaly based detection is where the system monitors current network traffic and compares it to a set baseline traffic. Again, if any unusual traffic occurs, members of the network are notified. In this research, new advancements in deep learning algorithms are used to bolster the defenses of digital networks. Neural networks are used to create a multi-class classifier, which will determine whether the network activity is a cer...

Research paper thumbnail of Finding Relationship Between Alzheimer's Disease and Noise in Electroencephalogram (EEG) Data Using Novel Machine Learning (ML) Algorithms

Research paper thumbnail of A Method For Network Intrusion Detection Using Deep Learning

Journal of Student Research

In an increasingly digitally reliant world, organizations are facing the ever more challenging pr... more In an increasingly digitally reliant world, organizations are facing the ever more challenging problem of how to best defend their digital information and infrastructure. Current non-machine learning methods for detecting network intrusion, like signature-based and anomaly-based algorithms, are slow and unreliable. Signature based detection holds signatures, or known information and warning signs, about a known attack and compares them to the current flow of data. If a signature matches with the network activity, users and network administrators are notified. Anomaly based detection is where the system monitors current network traffic and compares it to a set baseline traffic. Again, if any unusual traffic occurs, members of the network are notified. In this research, new advancements in deep learning algorithms are used to bolster the defenses of digital networks. Neural networks are used to create a multi-class classifier, which will determine whether the network activity is a cer...

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