Account takeover, also known as account hijacking, occurs when cybercriminals gain unapproved or illegitimate access to a user’s online account either through theft or intelligent guessing of a person’s credentials. This form of digital fraud is among the most prevalent and ruinous. With an expanding attack surface, ATO attacks have been increasingly rising in frequency, diversity, and overall negative impact.
This guide explores how artificial intelligence can help deal with such ATO attacks.
What is Account Takeover?
Account takeover is a form of identity fraud where nefarious elements obtain unauthorized access to users’ login credentials to get into their accounts. Cybercriminals often obtain passwords and usernames from website breaches or malware attacks, then use automated brute-force login tools to systematically check these stolen credentials across other popular websites and apps. Once the criminals gain access through credential stuffing, attackers find themselves in a favorable position to carry out a myriad of fraudulent activities via the accounts they hijacked.
Common methods generally employed in account takeover attacks include:
- Phishing sites that use deceit and fraud to entice users into revealing their credentials
- Keylogging malware that tracks keyboard input on devices
- Brute force attacks that are employed to guess password combinations
- Social engineering schemes that manipulate users
Attackers typically target accounts with financial data, purchase history, loyalty rewards, or personal information that can facilitate further theft and fraud.
Some of the common ways of account takeover attack include:
- Email accounts that are used for password resets
- Retail accounts where payment cards are saved
- Bank accounts and digital wallets
- Profiles on popular social media platforms
The Impact of Account Takeover
As and when cybercriminals are successful in account takeovers, the results can be rather far-reaching in terms of financial loss and personal trespassing. With access obtained to an online account, cybercriminals can:
- Lead to recurring financial losses by making unauthorized and illegal purchases with stored payment cards
- Indulge in illegally transferring money from a victim’s account to another account.
- Gain access to sensitive emails to further their nefarious agenda.
- Steal personal information to carry out identity fraud
- Access or delete valuable or sensitive data like videos, records, and photos.
Victims of such attacks often endure lengthy and challenging processes to retake control of compromised accounts, reset new passwords for hacked emails, assess identity thefts, and reverse fraudulent financial transactions.
Addressing Account Takeover Threat With AI/ML
AI/ML-powered IAM collects multiple risk signals per user and per authentication event. These may include multiple risk signals, such as user behavior patterns, location, time of day they sign in, device information, browser, and operating system used. AI can read whether the account access granted is based on the baseline user behavior pattern.
The analytics engine will use multiple signals simultaneously to generate a risk score for each access event.
These risk scores can then be grouped into low, medium, and high-risk categories. You can then set different outcomes for each category and serve these per your organization’s security policies.
How AI Helps Defend Against Account Takeover
Though account takeover attacks are sophisticated in nature, it is possible to proactively identify and prevent them using advanced artificial intelligence and machine learning capabilities. AI-enhanced defense capabilities are loaded with the following features:
- Behavioral Analytics—AI can flag any deviation from baseline behavior that may indicate an account takeover. They do this by evaluating users’ normal access patterns. Such suspicious events may include sudden geographical account access from unfamiliar locations, unknown devices, and others.
- Credential Stuffing Protection—Cloud networks and security systems are trained on huge datasets containing historical data, especially involving credential stuffing. This allows AI models to immediately identify patterns in how bots try to guess login information.
- Anti-bot Defenses—AI models trained on relevant data can differentiate between human and automated bot login attempts. They do this by tracking mouse movement and analyzing micro-interactions with pages and other signals. The system can allow legitimate logins while blocking fraudulent logins.
- User Identity Verification –Once AI and machine learning models identify any suspicious activity, they initiate additional identity verification for extra validation to prevent threat actors and allow real account owners. This involves using visual puzzles to check for human traits.
Adoption of Zero Trust Security Frameworks
What makes a real difference in this fight against ATO attacks is the integration of Zero Trust security. The principle underpinning this model is “never trust, always verify,” so that every request for access is treated with complete agnosticism regarding origination—inside or outside an enterprise. The Zero Trust approach eliminates use-case-agnostic implicit trust rooted in network location or device ownership and lowers this risk factor against ATO attacks by a high order of magnitude.
Under Zero Trust, every user or device is authenticated, and their identity is verified before granting access to the resources. Therefore, all Sensitive data and systems are granularly safe from unauthorized access using misused credentials or devices.
Case Studies: AI/ML in Account Takeover Prevention
Now that we have discussed strategies to defend against ATO attacks let us illustrate these concepts with real-world examples.
- A Fortune 500 Company Proactively Assists
A multinational company was under siege from a spate of ATOs against its vast customer database. With an AI system trained on millions of data points to identify patterns and anomalies, the company identified threats in real time and responded promptly to forestall such attacks before any material damage could be done.
- Behavioral Analytics in Use at Banking Institutions
A large bank faced ATO attacks. It had an ATO prevention system in place that provided an early warning system, substantially deterring ATO attacks and thus protecting customers’ financial assets and personal information.
How Sensfrx Prevents Account Takeover Attempts
Sensfrx is a powerful ATO prevention tool that uses a multi-layered approach to fight fraud and protect your accounts. Here’s how it works:
- Monitoring Login Attempts: Sensfrx tracks multiple parameters, such as login attempts, usernames, device information, IP addresses, and various factors, to identify and flag suspicious login attempts.
- Risk Analysis Engine: Based on data analytics and an algorithmic model, it employs risk analysis, considering factors like past login history, typical device usage, and location to assign a risk score to each attempt. If the risk score is high, Sensfrx immediately takes action. For example, if someone tries to log in from an unidentified location at 3 AM, Sensfrx considers it suspicious and asks for further verification.
- Precision Fingerprinting: Sensfrx’s precision fingerprinting capability is superior to traditional device identification methods. It’s based on a wide range of attributes, such as browser type, operating system, fonts, plugins, screen resolution, and more to create a unique and highly accurate fingerprint for each device that accesses users’ accounts.
- IP Profiling: Sensfrx employs AI to build detailed profiles of IP addresses and geolocations. It quickly flags suspicious login attempts from new, unknown IP addresses.
- Bot Activity Detection: Sensfrx AI considers mouse movements, keystrokes, and network activity patterns to detect patterns that deviate from normal human behavior. This allows the AI to block login attempts from malicious bots. \
- Real Time Protection: Sensfrx analyzes numerous parameters in real-time, including behavioral data, device data, and internet connection data, to detect anomalies and potential fraud risks.
The Future of Account Takeover Attacks
As AI defense measures grow more sophisticated and popular, it is most likely that nefarious elements with malicious intent will try to evolve their account takeover techniques to circumvent the new defense measures. Potential developments include:
- Increased Phishing Sophistication—Very specific, personalized, and sophisticated phishing lures could coax more users into parting with their specific information and credentials without triggering the alarm for phishing.
- Enhanced Social Engineering – Equipped with information gathered from unauthorized breaches and social media, criminals could better impersonate contacts and manipulate victims.
- Multi-Channel Coordinated Attacks—Mounting multiple attacks, such as phishing, smishing, and vishing, could weaken users’ defenses across multiple channels, allowing hackers to succeed in their illegal activities.
- Synthetic Identity Fraud – Obtaining sufficient data to fabricate fake digital identities enables fraudsters to create more accounts for potential takeover.
Final Thoughts
Finally, however well criminals adapt, defense measures such as AI and identity protection controls with expanded datasets, new detection patterns, and self-learning capabilities will expand in scope and capability to thwart illegal takeover activities. With AI acting as a primary citadel against identity fraud.
With threat actors’ growing sophistication and determination, account takeovers will continue to be potential threats well into the future, but the risk can be mitigated and damages averted through AI and savvy personal security habits.
At Sensfrx, we are at the forefront of integrating AI to prevent various fraudulent activities, including account takeover attacks. This lets you focus on business growth without worrying about such dreadful scenarios. Contact our team for more information.
