With the adoption of cloud and IoT technology, use of the Internet has increased. Every enterprise today, government or commercial, relies on the Internet and computer networks to share the enterprise data. Also, with the increasing adoption of IoT, the number of web-connected devices increases where the majority of devices have little or no security features.
Figure1: Botnet Detection Market, 2018 - 2030 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
Botnets are networks of connected devices such as personal computers, smartphones, and laptop infected with malware which are remotely controlled by bot masters or bot headers. Botnets are a significant threat to networks. The main purpose of botnets is spreading cyber attacks in the form of spam messages, distributed denial of service (DDoS) attacks, and automated identity theft. Zeus, Droid Dream, and Tigerbot are some of the mobile bots which have carried out successful botnet attacks in mobile to mobile platforms
Botnet detection techniques are adopted by both large as well as small & medium-sized enterprises (SMEs) as every enterprise aims to protect its confidential information from being stolen or destroyed from cyber attacks and frauds. BFSI industries primarily adopt botnet detection techniques as the cyber attackers are in constant wait for stealing customer data and accessing money. In the year 2017, White Ops Inc., a cybersecurity provider company detected about 40,000 banking Trojan botnets.
Furthermore, retail & e-commerce industries are also vulnerable to botnet attacks as majority of the transactions take place online. Cyber attackers keep a constant watch on the transactions and customer data to steal, skew and scrape it. If the botnet files are not detected and worked upon, customer satisfaction might get affected resulting in a staggering reduction in the revenue.
However, a major challenge faced by the botnet detection market is the lack of awareness about the latest botnet detection techniques. Majority of the enterprises are still using traditional botnet techniques whereas the severity and sophistication of threats has been constantly increasing which raises the demand for equally complex detection systems.
The global botnet detection market is segmented into various segments on the basis of deployment, organization size, application, detection techniques, and vertical.
By deployment, the market is segmented into on-cloud and on-premise.
By organization size, the market is segmented into large enterprise and small and medium enterprises (SMEs).
By application, the market is segmented into web-based, mobile-based, and API based.
By detection technique, the market is segmented into flow data monitoring, anomaly detection, DNS log analysis, and honeypots.
By vertical, the market is segmented into BFSI, government & defense, IT & telecom, retail, media & entertainment, transportation, healthcare, and others.
The global market for botnet detection is estimated to grow at a significant rate during the forecast period from 2022-2030. The geographical analysis of the botnet detection market is done for North America, Europe, Asia-Pacific, and the rest of the world.
North America is expected to dominate the botnet detection market during the forecast period. The major factors influencing the market in this region are the increasing adoption of cloud and IoT technologies along with connected devices such as smartphones which run a constant threat of botnet attacks. Also, well-established economies such as the US and Canada are spending a huge amount on research and development of botnet detection techniques, which is expected to fuel the market growth in the region. Asia-Pacific is expected to grow at a significant pace during the forecast period due to increasing adoption of botnet detection techniques by enterprises to safeguard the enterprise data.
The global botnet detection market is expected to generate a market value of USD 46.20 Billion By 2030 growing at a CAGR of 7.03%.
The prominent players in the market of botnet detection are
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