By Nikhil Taneja
Managing Director-India, SAARC & Middle East, Radware
Over 50% of web traffic is comprised of bots, and 89% of organizations have suffered attacks against web applications. Websites and mobile apps are two of the biggest revenue drivers for businesses and help solidify a company’s reputation with tech-savvy consumers. However, these digital engagement tools are coming under increasing threats from an array of sophisticated cyberattacks, including bots.
While a percentage of bots are used to automate business processes and tasks, others are designed for mischievous purposes, including account takeover, content scraping, payment fraud and denial-of-service attacks. Often, these attacks are carried out by competitors looking to undermine a company’s competitive advantage, steal information or increase your online marketing costs.
When Would You Need a Bot Detection Solution?
Sophisticated, next-generation bots can evade traditional security controls and go undetected by application owners. However, their impact can be noticed, and there are several indicators that can alert a company of malicious bot activity:
Why a WAF Isn’t an Effective Bot Detection Tool
WAFs are primarily created to safeguard websites against application vulnerability exploitations like SQL Injections, cross-site scripting (XSS), cross-site request forgery, session hijacking and other web attacks. WAFs typically feature basic bot mitigation capabilities and can block bots based on IPs or device fingerprinting.
However, WAFs fall short when facing more advanced, automated threats. Moreover, next-generation bots use sophisticated techniques to remain undetected, such as mimicking human behavior, abusing open-source tools or generating multiple violations in different sessions. Against these sophisticated threats, WAFs won’t get the job done.
The Benefits of Synergy
As the complexity of multi-vector cyberattacks increases, security systems must work in concert to mitigate these threats. In the case of application security, a combination of behavioral analytics to detect malicious bot activity and a WAF to protect against vulnerability exploitations and guard sensitive data is critical.
Moreover, many threats can be blocked at the network level before reaching the application servers. This not only reduces risk, but also reduces the processing loads on the network infrastructure by filtering malicious bot traffic.