Reviewing the Optimized Mechanism for Deep Learning Based Bot Detection to Evaluate Genuine Crypto Assets

Abstract

The scope of research in employing deep learning for bot detection in the identification of genuine crypto assets is expansive, encompassing a multitude of challenges and opportunities at the intersection of artificial intelligence, cyber security, and financial technology. Researchers can delve into the intricate dynamics of adversarial interactions between bots and detection systems. Adversarial attacks aimed at undermining the integrity of deep learning models pose a significant challenge, requiring innovative defenses and methods to enhance the resilience of the detection systems against manipulation. A promising avenue of exploration involves investigating the transferability of deep learning models across various crypto currencies. Analyzing the extent to which models trained on one cryptocurrency can be effectively applied to others may streamline the process of identifying patterns indicative of bot-driven activities, especially in cases where labeled data is scarce or non-existent for certain crypto currencies. Temporal dynamics play a crucial role in the evolving landscape of bot behaviors. Researchers can focus on understanding the temporal patterns of bot activities, studying their evolution over time, and developing deep learning models that can adapt to these changes. Real-time detection capabilities become paramount in this context, particularly when dealing with the rapid pace of activities on social media platforms and cryptocurrency exchanges. Scalability is also a key consideration, ensuring that models can efficiently handle the vast amounts of data generated in the crypto space.