An Analysis Of Requirements Of Deep Learning Based Classification Of Attacks Over Big Data Security

Authors: Assistant Professor Dr. Banita, Ms. Jyoti Ahlawat

Abstract: The rapid growth of big data across diverse digital ecosystems has made cyber security a critical concern, especially as traditional intrusion detection systems struggle to scale and adapt. This study explores the necessity and advantages of deploying deep learning-based techniques for the classification of cyber-attacks in big data environments. We will analyze the limitations of conventional machine learning models in handling high-volume, high-velocity, and high-variety data streams, emphasizing the unique challenges posed by modern attack vectors. The paper evaluates various deep learning architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models—on their ability to detect complex and evolving threats. Additionally, we will address the infrastructural and computational requirements, such as distributed processing frameworks like Apache Spark and the role of GPU acceleration, to support deep learning at scale.