Time-Series Analysis on AIDE IoT Attack Data Unraveling Trends and Patterns for Enhanced Security

Abstract

With the growing use of IoT devices, cyberattacks have increased against these devices, using various exploitable vulnerabilities. Cyberattacks are potentially ruinous events for business owners and the cost of a cyber-attack is not only financial, but companies also must spend time on recovering from the attacks. Through applied research we set out the latest findings and trends to provide new insights into the security of IoTs, focusing on the emerging nature of IoT attacks, frequency, and usual vulnerabilities, thus providing significance to cybersecurity and IoT defense, which will be beneficial to researchers, manufacturers, individuals, organizations, and governments. Although the literature on IoTs is quite rich to this day, however, there is currently no study that provides an in-depth analysis of patterns and frequencies of IoT attacks to discover insights into the most vulnerable times and systems. Compared to other related research on the security of IoTs, this applied research encompasses much more technical angles to the security of IoT. It begins with the comprehensive data acquisition from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE) and preprocessing to ensure data integrity and relevance. Followed by a thorough feature selection and engineering process. Python scripts and libraries were used for efficient data analysis. Unraveling Trends and Patterns for Enhanced Security are sensitive areas that have remained untouched by previous research works. Time-Series Analysis on AIDE IoT Attack Data used in this study provided critical insights into attack patterns by fields such as timestamps, attack duration, and login credentials, implying a huge scientific and technological uncover of an intricate and evolving landscape of IoT security threats and attacks.