Businesses need a better solution to combat account takeover attacks. Traditional protection against credit card fraud is insufficient to tackle this new, more dangerous threat.
Bad actors steal credentials and sell access to online accounts for monetary gain. Social engineering, malware, and brute force are a sinister trifecta.
Automated Detection
Account takeover (ATO) attacks are a rising digital threat; cyber criminals will do anything to get their hands on your data. Fraudsters typically begin hacking into a victim’s account using stolen login credentials. After gaining access, they can change everything associated with the account, including security questions, passwords, encryption settings, and more. This unauthorized activity can lead to financial fraud, fraudulent transactions, and even identity theft.
Cybercriminals acquire these credentials in various ways, including phishing, malware attacks, spoofed emails, data breaches, and credential sales on the dark web. They then deploy bots to test username-password combinations on travel, retail, banking, eCommerce, and other websites. This type of attack is known as brute force or credential stuffing.
Robust account takeover detection software has the ability to identify spoof email addresses and highlight odd patterns of behavior. Moreover, it can ask for more authentication on dubious devices and request geolocation updates, preventing account takeover attempts before they start.
In addition to orchestration solutions, a comprehensive security strategy should include advanced ATO detection tools. These can include device fingerprinting, which identifies unique user behavior patterns to detect anomalies that may indicate account takeover. Behavioral biometrics can also help strengthen these detection methods by analyzing mouse movements, keystrokes, and touch gestures to determine whether an individual is a human or a bot.
Machine Learning
Machine learning plays a crucial role in numerous cybersecurity systems. It can detect cyber-attacks by analyzing data from preprocessed datasets, including replay, man-in-the-middle, impersonation, password guessing, session key leakage, malware injection, distributed denial of service (DDoS), etc., in the online/offline mode.
Moreover, it can also identify anomalies in the transactional data by comparing them against the historical patterns to recognize suspicious activities and flag them for further investigation. It can also assign risk scores to transactions and user accounts based on the probability of fraud, enabling security teams to prioritize their efforts and resources.
ATO is a form of identity theft where attackers gain access to the account credentials of a natural person, typically to steal money and personal information. These credentials are stolen through a brute force attack with bad bots, where they are repeatedly sent to an online account (typically an e-commerce account) to try to guess the login details to take over the account.
With advanced machine learning, these attacks can be prevented by detecting the repeated attempt to use a specific brute force attack from a particular device, IP address, or location in a short period. It is essential in preventing ATO and protecting users from fraudulent activity.
Adaptive Detection
One of the most critical aspects of detecting account takeover (ATO) attacks is the ability to detect anomalies. However, users have more variance in their behaviors than ever before, making it harder for heuristics or rule-based approaches to find them. Machine learning is an excellent solution to this problem, but it requires a lot of data to work correctly.
Adaptive detection combines advanced machine learning with a robust risk engine to identify fraud patterns at scale. It analyzes user behavior and activity, device health, network security, threat reputation, and more to create a risk score. When higher-risk activities are detected, they automatically trigger additional authentication measures.
To protect against ATO attacks, monitoring all steps a criminal takes to steal and use an account is essential. It means a system that watches a user’s activity, not just the last few transactions. A system that knows a cybercriminal has to do other things first to move money, such as creating a payee.
A sound system can catch these activities and make them impossible for attackers to execute, thus protecting an organization’s customers. This approach also makes addressing regulatory investigations and private lawsuits that can follow a breach easier. It is one of many ways a service provider can strengthen its ATO defenses.
Reporting
As cybercriminals evolve and attack techniques become increasingly sophisticated, security solutions must be able to keep pace. Otherwise, businesses could face investigations by government agencies or lawsuits filed by disgruntled customers following a data breach.
One common attack strategy involves gaining unauthorized access to customer accounts through account takeover (ATO) attacks. This type of fraud allows attackers to change account information and passwords, steal financial data, and sabotage transactions. ATO attacks are often executed through social engineering, malware, and brute force tactics.
ATO attacks can be brutal for organizations to detect, especially since they resemble legitimate login attempts. That is why monitoring account activity and having a granular view of user behavior is essential to detect patterns indicative of a malicious attack.
Look for cybersecurity software that reviews the small signals in every request to your website, app, or API to root out suspicious activity on autopilot.
To further strengthen your protection against ATO, consider leveraging behavioral biometrics to identify patterns that align with human behaviors.