In the ever-evolving landscape of cybersecurity, understanding the actors involved is crucial for effective threat detection and mitigation. Whether it is a malicious individual, a coordinated group, or even automated bots, these entities, often referred to as actors, play a significant role in determining the security posture of an organization. This article by Radware delves into the intricacies of such actors, particularly their identifiers (actor IDs) within API and web traffic, and how anomalies in these IDs can be instrumental in detecting Account Takeover (ATO) attacks.

Understanding Actors in Cybersecurity

Actors in cybersecurity refer to entities that interact with systems, networks, or applications, with varying intentions ranging from benign to malicious. They can include individual hackers, organized cybercriminal groups, insiders with malicious intent, or even legitimate users inadvertently engaging in risky behavior. Understanding the motivations, methods, and characteristics of these actors is fundamental in developing effective security strategies.

Actor IDs in API and Web Traffic

Actor IDs serve as unique identifiers associated with entities interacting with systems or applications. In the context of API and web traffic, these IDs manifest in various forms depending on the authentication mechanisms and protocols utilized. Examples of actor IDs include:

API Keys: In API interactions, actors often authenticate themselves using API keys. These keys uniquely identify the entity accessing the API and are typically embedded within requests, for example:
http://api.myorg.org/data/2.5/search?q=value1&APPID=API_KEY

User Sessions: In web traffic, user sessions are commonly used to track and identify actors. Session IDs or tokens are generated upon authentication and persist throughout the user’s interaction with the application, for example:
Cookie: session_id=<SESSION_ID> user_id=1234

User Agents: User agents present in HTTP headers provide information about the client making the request, including the device type, browser, and operating system. While not direct actor IDs, user agents can aid in identifying suspicious activity, for example: User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36

Understanding Actor Anomalies

Actor Anomalies represent a paradigm shift in cybersecurity, focusing on identifying abnormal behaviors exhibited by specific entities or actors within a system. These actors are uniquely identified by various parameters such as source IP addresses and JSON Web Tokens (JWTs). By monitoring and analyzing the behavior associated with these identifiers, security systems can detect deviations from expected patterns, signaling potential security risks.

Identifying Abnormal Behavior

Imagine a scenario where an attacker attempts to exploit vulnerabilities using stolen credentials. With Actor Anomalies, abnormal behavior such as using the same JWT from multiple IPs within a short time frame can be flagged as suspicious. This real-time monitoring and analysis enable organizations to detect and mitigate threats before they escalate, enhancing overall cybersecurity posture.

After we understand what actors and actor anomalies are, let us dive into one of the most common attack vectors: Account Takeover (ATO), and explain how actors and actor anomalies help to detect and mitigate this attack.

Detecting ATO Attacks

Account Takeover (ATO) attacks involve unauthorized access to user accounts, often resulting from stolen credentials or exploitation of vulnerabilities. Understanding the techniques employed in ATO attacks is crucial for effective detection and mitigation. Here’s an example of how an ATO attack might developed:

Credential Stuffing: The attacker utilizes lists of stolen credentials obtained from previous data breaches and systematically tries them on various accounts across different platforms, exploiting users’ tendencies to reuse passwords.

Brute Force Attacks: Using automated tools, the attacker launches brute force attacks, systematically attempting different username and password combinations until a valid one is found.

Anomalies and Correlation in Actor IDs for ATO Detection

Detecting Account Takeover (ATO) attacks requires a keen eye for anomalies and correlations in actor IDs, which serve as unique identifiers associated with entities interacting with systems or applications. By monitoring and analyzing these identifiers, security systems can identify suspicious patterns indicative of potential ATO activity. Let’s explore some key anomalies and correlations along with examples of actor IDs:

  1. Unusual Geographical Locations:

Anomaly: Sudden logins from geographically distant locations or regions not associated with the user’s typical behavior.

Example Actor IDs: Source IP addresses associated with login attempts.

Example Scenario: A user typically logs in from New York but suddenly attempts to access their account from Russia within minutes. This abnormal change in geographical location raises suspicion and warrants further investigation.

  1. Abnormal Access Patterns:

Anomaly: Unusual login/logout sequences, multiple failed login attempts, or access during non-standard hours.

Example Actor IDs: User session IDs or timestamps associated with login/logout events.

Example Scenario: An account experiences a sudden influx of login attempts during the early morning hours when the legitimate user typically does not access the system. Additionally, these login attempts are followed by immediate logouts, suggesting automated bot activity rather than genuine user behavior.

  1. Mismatched User-Agent Signatures:

Anomaly: Inconsistent user-agent signatures across sessions or devices.

Example Actor IDs: User-agent strings present in HTTP headers.

Example Scenario: A user logs in from a device and browser combination that is not typically associated with their account. For instance, a sudden switch from using a Windows desktop with Chrome to a macOS laptop with Safari within a short time frame could indicate potential unauthorized access.

  1. Simultaneous Access from Multiple IPs:

Anomaly: Concurrent logins from multiple IP addresses, especially if geographically distant.

Example Actor IDs: Source IP addresses associated with concurrent login attempts.

Example Scenario: An account is accessed simultaneously from two different countries within seconds, suggesting either account sharing or compromise. Such simultaneous access from disparate locations is highly suspicious and indicative of potential ATO activity.

  1. Unusual API Key Usage:

Anomaly: Anomalous usage patterns such as excessive authentication failures or access to unauthorized endpoints.

Example Actor IDs: API keys associated with API requests.

Example Scenario: An API key, typically used by a trusted application, suddenly starts accessing sensitive endpoints or experiencing a surge in failed authentication attempts. This abnormal behavior may indicate a compromised API key or unauthorized access attempt.

By monitoring and analyzing these anomalies and correlating them with actor IDs, organizations can proactively detect and mitigate ATO attacks before significant damage occurs. Leveraging behavioral analytics can further enhance the effectiveness of ATO detection mechanisms by identifying subtle deviations from normal behavior.

In conclusion, understanding the actors involved in cybersecurity and their identifiers within API and web traffic is essential for detecting and mitigating ATO attacks. By identifying anomalies and correlations in actor IDs, organizations can strengthen their defenses and safeguard against the ever-present threat of account takeover. As the threat landscape continues to evolve, continual refinement and innovation in ATO detection mechanisms are imperative to stay ahead of adversaries and protect sensitive user data.

Your digital assets deserve the best defense – stay informed, stay safe and contact us Radware products offer multiple techniques for anomaly detection and mitigation. Learn more about Radware’s Cloud WAAP services and application protection for Kubernetes.

 

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