Bot detection is a critical security priority for all sizes and types of businesses with an online presence. Threat actors (such as cybercriminals, hacktivists, and even competitors) leverage bots for several nefarious activities such as ad/click frauds, content spamming, DDoS attacks, and so on. So, malicious bots are responsible for many serious security threats that businesses face today, making bot detection vital for business continuity and success.
Bot detection is becoming more complex as bot developers are finding more sophisticated ways to fulfill their motives. Let us delve into ways to detect the rapidly modernizing bots.
The Evolution of Bots
Initially, bots were scripts used for performing automated tasks like crawling websites, retrieving/ scraping information, and so on. They neither accepted session cookies, nor did they parse JavaScript. So, these Gen1 and Gen2 bots were and are very simple to detect. Over time, these bots started maintaining session cookies and parsed JavaScript, but they used lesser dynamic elements than human users. So, their detection was not too difficult.
Gen3 Bots resemble sophisticated browsers such as the scriptable, headless Phantom JS, and Casper JS browsers. These are capable of processing web content in its entirety and herald low and slow attacks. Despite their increased sophistication, we can distinguish these bots from human traffic through challenges, tests, and fingerprinting.
The latest generation of bots, however, is much more sophisticated and mimic human behavior such as clicking on-page elements, hiding inside user sessions, etc. They are almost indistinguishable from human traffic without advanced technical know-how and expertise in bot detection. The next generation of bots will more widely use AI and ML to escape detection.
Learn about how to stop Botnet DDoS Attacks.
Traditional Methods of Bot Detection: Why are They Ineffective in the Current Landscape?
It is evident from the preceding section that bots are continuously and rapidly evolving. Modern-day bot developers are leveraging the latest technology and tools to build sophisticated bots capable of circumventing bot detection solutions.
IP-Based Blacklisting
Security solutions including traditional WAFs filtered and detected bot traffic based on IP reputation. This worked with Gen1 and Gen2 bots which used data center proxies. So, these bots could be prevented by blocking known data center proxies based on public databases.
IP-based detectors became ineffective as bots started using Tor exit nodes, which serve multiple users, making it difficult to detect these malicious bots. Today, attackers can quickly switch to different Tor exit nodes and millions of IP addresses to accomplish their motives.
Static Rule-Based Approach
Web traffic analytics is manually reviewed to detect bot activity on the website. Parameters such as traffic trends, server performance, bounce rates, geo-locations, language sources, etc. are analyzed and bots are prevented.
Today, bots are distributed from reputed residential IPs. They are also capable of mimicking human-like signatures with ease, making it close to impossible to distinguish their request from a legitimate user’s request. So, this approach is ineffective as well.
IP-based blacklisting and static approach are not completely useless. They have a place in bot prevention. That must be used in combination with other robust and sophisticated bot detection solutions along with Security experts who can create targeted custom bot rules based on application workflow and anomaly scoring logic on top of the traditional approach.
How to Detect Bot Traffic in the Fast-Evolving Threat Landscape?
For the effective detection of bot traffic in the evolving threat landscape, Real-time Behavioral and Pattern Analysis is necessary. In Real-time Behavioral and Pattern Analysis, a baseline of normal/ acceptable behavior, activities, patterns, and signatures of users is established. Each visitor is automatically analyzed against the baseline and deviant behavior is flagged, challenged, or blocked.
In advanced solutions like AppTrana, the baseline keeps rapidly evolving based on real-time data, Global Threat Intelligence, historical attack/traffic data, and results of manual security testing. The process is automated and the WAF continuously learns and updates itself. Being a managed bot detection solution, certified security experts at Indusface use security analytics and other insights to tune the intuitive WAF with customized rules. They work with the application team and the business logic along with information of past suspicious alerts. This way not only ensures known signatures are blocked but emerging bot-based threats are detected and prevented too.
Combined with proactive false positive management, you will not turn away even one legitimate user while bots do not cross the network periphery.
Conclusion
Given that bot attacks are damaging to the business, bot detection and prevention are indispensable. By traditional methods to detect bot traffic, businesses run a high risk of blocking legitimate users from accessing their web applications. And that is detrimental! You must leverage futuristic and intuitive security solutions like AppTrana that enable you to put bot detection on autopilot and focus on your core business.
