Social Media Zero-Day Attack Detection Using TensorFlow

Abstract

In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly.