SECURITY IN IGAMING
WHAT IS THE MOTIVATION FOR DEPLOYING BOTS? 1. Money
According to the State of Fraud and Account Security report, the ROI for committing online fraud is higher than ever. “Fraud as a Service” is rising, with marketplaces on the dark web enabling amateur fraudsters to attack at scale. Cybercriminals use fake identities to claim bonuses, open new accounts, and place fraudulent bets. 2. Efficiency
Bot swarming can create thousands of accounts in a short time and quickly abuse incentives. These accounts can be used to manipulate survey and game results, leaving the victim exposed to financial and legal liability. 3. Intelligence
Bots are becoming smarter, creating greater detection complexity for businesses. They can mimic human behaviour with high accuracy. The number of data points that need to be collected, reviewed, and correlated to form a single attack signature makes it harder to find them.
A COMPREHENSIVE DEFENCE ECOSYSTEM
In light of these multifaceted challenges, gambling sites require a comprehensive defence strategy to prevent potential bot
swarming. Traditional measures like denial-of- service protection, web application firewalls, and content delivery networks are elemental, but prove insufficient when considered in isolation. To effectively counter these threats, a more nuanced approach is required, involving the integration of machine learning (ML) algorithms for anomaly detection.
ML algorithms scrutinize intricate behavioural patterns – such as typing speed, session duration, and IP address deviations – to unmask coordinated attacks and expose credential stuffing endeavours. Furthermore, collecting hidden (from the user standpoint) data, such as behavioral analytics and patterns, adds an additional layer of protection.
Defending against bot attacks must begin with proactive attention to prevention. The growing number of connected devices amplifies the risk of botnet attacks, putting any internet- connected device in danger.
Gaining enhanced visibility into network operations also makes a significant difference. Network monitoring and analytics tools provide insights into devices and traffic patterns. AI-powered network monitoring can establish baseline usage and identify anomalies, thus enabling the early detection of attacks and providing security personnel with time to respond effectively.
ML emerges as a crucial tool in predicting
fraudulent behaviour at both transactional and behavioral levels. This predictive capability makes it difficult for fake gaming accounts to go unnoticed. When a group of users exhibits anomalous behaviour across multiple accounts, this indicates a coordinated attack. Moreover, ML algorithms are capable of uncovering suspicious login attempts, such as credentials stuffing attacks, by monitoring the frequency of failed logins from the same IP address. Yet, cybercriminal behaviour is not isolated; it’s interconnected. This reality necessitates adaptive and collaborative efforts from businesses to effectively compete against fraud. Addressing fraud isn’t a challenge that any individual organization, industry, or government can tackle in isolation. An ecosystem approach is required to effectively combat the threat. Although traditional identity verification solutions remain essential, they must be supplemented with an extra layer of protection to elevate fraud detection capabilities. Forrester recommends adopting a holistic solution that can not only detect sophisticated bots but also keep pace with evolving bot strategies and employ a diverse range of responses to thwart attacks.
For online gaming operators, staying informed about the latest casino bot trends and maintaining a robust security posture is vital to ensuring 100% protection against attacks.
GIO DECEMBER 2023 9
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