Botnet detection is a tool or technique that is used to recognize a botnet, which is a computer network under the control of a malicious user. The types of mechanisms of botnet detection are active and passive mechanisms. The botnet detection technique is built upon the traffic analysis between the bot master and the bot. Some botnets are very hard to notice, since they utilize HTTP service for communication with each other, and their communication is generally in encrypted format. It is important for any enterprise to guard against malware assaults, including botnets, by protecting even the weakest link on the enterprise system and network. It is highly important to remove a botnet, after it has been identified on a system with the help of a safety software, with the help of botnet elimination. Furthermore, it is vital to maintain proactive efforts for the detection of botnet after elimination of the botnet. Generally, botnet needs C&C (command and control) server to shut down, which is utilized to control the botnet.
Several factors, such as rise in number of smartphones along with the usage of APIs and mobile applications around the globe, and increase in need for security against bot-generated traffic are expected to boost the botnet detection market. Various governments around the globe are taking coordinated efforts in order to counter botnet attacks. Furthermore, governments are also taking precautionary measures to avoid such instances in the near future. For instance, in May 2018, the Government of India announced plans to set-up a center of cleaning and malware examination for the botnet. This is the part of “Digital India Programme” that targets to make cyber space across the country secure. The center is expected to detect botnets, automatically, which prompt cyber-crimes. Botnet detection tools and techniques have evolved along with the development of the botnets worldwide. Presently, various botnet detection open-source solutions and other types of more comprehensive, security intelligence botnet detection offerings are available to detect uncommon activity of a network in predefined ways. Furthermore, it also detects origin of the network, and analyzes its impact.
Usage of traditional botnet detection methods, such as CAPTCHA, by various enterprises coupled with a lack of awareness is hampering the botnet detection market. However, rise in awareness activities by several leading players in the space of cyber security and increased government initiatives to remove botnets from their online space are expected to overcome this restraint in the near future.
The global botnet detection market can be segmented based on application, end-use industry, deployment type, enterprise size, and region. Based on application, the botnet detection market can be classified into APIs, mobile applications, web, and others. In terms of end-use industry, the botnet detection market can be segmented into healthcare, real estate, financial services, gaming, and others. Furthermore, based on deployment type, the market can be bifurcated into on-premise deployment and cloud-based deployment. Additionally, in terms of enterprise size, the market can be bifurcated into large and small & medium enterprises.
In terms of region, the global botnet detection market can be segmented into North America, Europe, Middle East & Africa, Asia Pacific, and South America. North America and Europe are expected to lead the global botnet detection market due to the presence of established players providing botnet detection tools and novel innovations in order to create highly advanced botnet detection tools as well as techniques. However, increase in demand for web and mobile application security against bot attacks, due to rise in the number of mobile users and web traffic across Asia Pacific, is expected to propel the botnet detection market in Asia Pacific during the forecast period.
Key players operating in the global botnet detection market include Kaalbi Technologies Pvt. Ltd., Integral Ad Science, Inc., Reblaze Technologies Ltd., Oracle Corporation, InfiSecure, Digital Hands, zvelo, Inc., DataDome, Kasada Pty Ltd, INTECHNICA, AppsFlyer, White Diagnostic, Unbotify, Imperva, Distil, Inc., PerimeterX, Inc., and VERACODE.
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