Role of Machine Learning in Thwarting Automated Bot Attacks on Applications


In the rapidly changing field of cybersecurity, combating automated bot attacks has become more critical than ever before. As technology progresses, bots have become increasingly sophisticated, posing significant risks to applications across various industries. In response, Machine Learning (ML) algorithms have emerged as indispensable tools in the ongoing battle against cyber threats, particularly automated bots, due to their capacity to evolve and adapt to new attack techniques.

The Growing Threat of Automated Bot Attacks

Automated bot attacks are not a new phenomenon, but their complexity and frequency have surged in recent years, impacting data integrity, user experience, financial stability, and brand reputation. These attacks range from basic scripts aimed at scraping data to highly advanced bots capable of mimicking human behaviour.

Types of Bot Attacks

  • Data Scraping: Bots designed to extract content or pricing information from websites.
  • Credential Stuffing: Bots using stolen credentials to gain unauthorized access to user accounts.
  • Denial of Service (DoS): Bots flooding websites with traffic, causing service disruptions.
  • Ad Fraud: Bots generating false ad impressions or clicks, resulting in financial losses.
  • Scalping: Bots purchasing high-demand items in bulk, creating artificial shortages and reselling at inflated prices.

These are just a few examples, with the sophistication of such attacks continuing to evolve. Traditional defence methods like rate limiting and IP blocking are no longer adequate, necessitating the intervention of AI and ML for dynamic and adaptive solutions.

Importance of Machine Learning in Cybersecurity

Machine Learning plays a crucial role in addressing ongoing cyber threats in today's digital environment for several reasons:

  • Adaptive Detection: ML algorithms can analyse real-time patterns and behaviours to detect bot activities. Unlike deterministic rule-based approaches, ML models can learn and adjust to new data, enhancing their effectiveness against sophisticated bots.
  • Behavioural Analysis: ML excels in analysing traffic behaviours, distinguishing between human-like interactions and automated bot behaviour based on factors such as click patterns, session durations, and mouse movements.
  • Anomaly Detection: ML algorithms identify anomalies in traffic patterns indicative of bot activities by learning from normal traffic behaviours.
  • Improved Accuracy: ML continuously enhances accuracy by learning from feedback and new data, minimizing false positives and staying ahead of emerging threats.

In summary, Machine Learning plays a crucial role in combating automated bot attacks by offering dynamic, scalable, and adaptive solutions to safeguard applications and networks in today's fast-evolving digital environment. This is precisely where Radware focuses its efforts—to strengthen its bot detection capabilities using ML algorithms, effectively identifying, and mitigating constantly evolving bot behaviours.

Radware Bot Manager: Harnessing the Power of ML-Based Algorithms

Radware’s approach to bot mitigation is multi-layered, combining various techniques to create robust defence. Below section captures a high-level overview of the approach towards harnessing the power of ML-based approach towards bot detection. The idea here is to provide the thinking behind how we look at ML-based approach towards bot detection.

  • Behavioural Analysis: By training the modules from normal human behaviour and analysing the deviations from expected patterns to identify potential anomalies or suspicious activities. One of the key advanced behavioural detection modules that work in this context is the ML-based HTTP Header Anomaly module This works based on identifying anomalies based on HTTP headers. The modules learn what is the expected set of Standard HTTP headers for a major browser version and if an anomaly is seen w.r.t to what has been learnt, it flags that source as anomalous. This could include both absence of standard headers or presence of rare headers (never seen during the learning phase).
  • Anomaly-Based Detection: This method analyses traffic for unusual or unexpected values, patterns, or combinations that may indicate malicious intent or abnormal activity. This is an unsupervised ML module which performs behavioural based anomaly detection to detect bots. The idea behind this is during the training phase, a set of behavioural features are generated for each identity based on a certain number of hits. After generating this feature set, a ML model is trained using Isolation Forest Algorithm. In the prediction phase, the same feature set is generated for every new source identity seen in the traffic and the trained ML model is used to flag the anomalous identities and signature is created to mitigate the attack. Isolation Forest quantifies the anomalous behaviour by giving a score to each identity and lower the score, the more anomalous the source identity is.
  • Suspect Indicator Based Approach: Radware Bot Manager also has an approach of looking at multiple suspect indicators, each being a separate model and then using a combination of these different suspect indicators to decide the anomaly of a source identity and then block that malicious identity if deemed anomalous. One such example of such a Suspect Indicator is Scraping Indicator where the idea is to identity anomaly based on how the path and query parameters are rotated which is a typical bot behaviour related to scraping use case. The idea here is that in learning phase, automatically identify the list of top URLs for the application and learn what is the normal URL access with path and query parameter change and thus set the baseline threshold for this and in the prediction phase, if we see a source identity making much beyond the learnt threshold, the source identity is deemed anomalous.

To Summarize

In the current market landscape, the importance of AI and ML-based algorithms in thwarting automated bot attacks cannot be overstated. At Radware, we continue to invest in more automated ML-based approach towards bot mitigation and as the threat landscape evolves, we continue to evolve to stay ahead of the attackers. What was covered in the above section was just a brief overview of the approach we take towards ML-based approach for bot mitigation. We continue to heavily invest in our multi-layered approach towards Bot Detection and Mitigation and this ML-based detection is a significant part of the Behavioural-based detection layer. For more details on our multi-layered approach towards Bot Detection, you can refer to this blog post Radware Bot Manager’s Multi-layered approach.

Contact us to learn more about proactive application protection strategies and AI-based solutions.

Netravati Hegadi

Netravati Hegadi

Netravati is a product manager at Radware, driving efforts to enhance Radware Bot Manager and elevate the user experience. She has over 14 years of high-tech security solutions experience working in a variety of roles that supported a number of enterprise products, including several for RSA and McAfee. Netravati has strong technical skills and years of successful security experience. Her ability to gain a deep understanding of product functions helps her comprehensively and successfully drive key product management functions.

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