PUBLISHER: Grand View Research | PRODUCT CODE: 1301289
PUBLISHER: Grand View Research | PRODUCT CODE: 1301289
The global anomaly detection market size is expected to reach USD 14.59 billion by 2030, registering a CAGR of 16.5% from 2023 to 2030, according to the new reports of Grand View Research, Inc. The growing advancements in deep learning and machine learning technologies support the market growth. The traditional statistical approaches are being replaced with modern methods, such as Generative Adversarial Networks (GAN), variational autoencoders (Vaes), and Recurrent Neural Networks (RNNs), thereby enhancing the identification of anomalies across various systems. Furthermore, a rise in the need for large data storage units and real-time data analysis has boosted the adoption of anomaly detection systems. Online algorithms, incremental learning approaches, and sliding window techniques are among the modern batch-based anomaly detection techniques used by various sectors for efficiently streaming data.
In today's ever-evolving cybersecurity landscape, businesses are facing various cyberattacks, such as malware, ransomware, and other security breaches. It is almost difficult to entirely exclude privacy issues from advanced information systems, including Internet of Things (IoT), and Cyber-Physical systems (CPs). This makes it essential for industries to continuously identify cyber threats and anomalies to track privacy issues and offer security awareness. Cyberattacks on CPs can result in impacting the economical, physical, and environmental safety of the population severely. For instance, in 2021, the attack on the Colonial Pipeline, which is one of the largest and most important oil pipelines in the U.S., disrupted the fuel supply on the East Coast.
Also, in 2022, there was one of the most severely damaging cyberattacks on Germany's domestic fuel distribution system, thereby mostly destroying hacks observed in the oil industry. Henceforth, to detect and mitigate such cyberattacks at early stages, it has become vital for industries to overcome anomalies, thus fueling market demand. Moreover, with the evolving cybersecurity landscape, there has been a huge demand for behavior-based anomaly detection technology across industries. Organizations need to focus on analyzing the behavior and activities of systems, users, and network entities to identify cyber threats at an early stage. Behavior-based anomaly detection focuses on capturing sophisticated cyber-attacks, including advanced persistent threats and insider threats, thereby securing various systems of organizations. The growing demand for analyzing cyber-physical entities' behavior is anticipated to propel market growth among regions.