Consumers’ digital accounts are central to all online activity – whether to sign up for a new service or access an existing one. While increasing consumer activity allows businesses to build relationships with consumers and fuel business growth, the story has a flip side too.
Increasing consumer accounts and multiple touch points mean that many more opportunities exist for bad actors to exploit for monetary gain. In 2021 alone, intelligence from the Arkose Labs global Network revealed an 85% increase in attacks on consumer online logins and sign-ups.
Lack of visibility into attacks hinders prevention
Businesses are making significant investments in strengthening their defense postures, but attackers still manage to outpace them with evolved attack tactics. Attackers have studied the commonly used security measures and leverage this knowledge to develop and deploy very successful countermeasures. The result is an ongoing cat-and-mouse game between the attackers and businesses, leaving fraud and infosec teams feeling like they can never move fast enough to get out ahead of bad actors.
Lack of visibility into attacks hinders fraud prevention efforts of businesses. Cybercriminals deliberately randomize attacks to render the signals inconclusive, leading to increased false negatives or false positives. Inability to analyze these inconclusive signals makes it harder for security teams to tell bad actors from genuine consumers, and can cause long-term damage to enterprises’ revenue and brand equity.
Six essential elements for a future-proof attack detection
Businesses must fight evolving attacks with confidence to ensure consumers’ account security, long-term. It is imperative that they deploy future-proof attack detection that provides graduated security measures to efficiently stop known threats, identify spoofing and evasion techniques, and adapt to fight evolving attack patterns.
For a future-proof attack detection, businesses must consider the six essential elements described below:
- Actionable risk intelligence: In today’s data-driven economy, businesses possess large volumes of data. However, it is the ability to glean actionable insights for prompt data-backed decisioning that makes all this data worthwhile. Businesses must continuously evaluate new, emerging signals and form factors, to be able to incorporate them into their detection engines. They should also focus on identifying device and network characteristics and geography. In addition, analyzing historical traffic patterns can help them identify and detect traffic anomalies, indicative of potential attacks.
- Behavioral analytics: In a world were device and IP characteristics are being spoofed and obfuscated, we need to delve deeper into signals to understand what is truly legitimate. A combination of behavioral biometrics and behavioral analytics to analyze user behavior is a critical additional layer to accurately differentiate between bad bots, approved automated traffic, genuine consumers, and malicious humans.
- Machine learning: Machine learning (ML) helps correlate disparate risk signals and weigh different attributes to correctly classify and assign a risk score. ML is especially useful in anomaly detection and can help raise red flags when critical incidents need attention –whether to resolve a problem or gain insights into ongoing processes. ML not only allows predictive analysis of traffic patterns, but also facilitates automatic update to rules based on newly identified attack patterns, providing protection across use cases and disparate lines of business.
- Human analysis and oversight: While machines can execute routine activities, trained security professionals are vital for their insights and experience. Their expertise proves useful in adapting to the evolving attack patterns and investigating risk signatures that machine learning algorithms identify. Human analysis is critical to proactively monitoring for anomaly detection and analyzing intricate patterns, as attackers attempt to hide amongst good traffic.
- Sharing network intelligence: Attackers launch similar attacks to target multiple companies. When a company that gets attacked, shares intelligence into the attack patterns, it can help other businesses to remain vigilant and identify these attacks across use cases and industries. While sharing data, companies must anonymize data to help maintain data privacy. Companies can also choose to partner with vendors, such as Arkose Labs, that have robust global clientele across industries to benefit each business from the combined intel.
- Threat intelligence from the dark web: Powerful threat intelligence forms the backbone of a good detection engine. Acquiring meaningful threat intel requires actively tracking known attack operations and learning about the tools and techniques from the channels where bad actors communicate. It also means going a step further into the dark web forums and engaging with attackers in the communities on platforms that they use. An attacker turned ‘white hat’ can be an important source of insights on how they launch attacks.
Arkose Bot Manager for future-proof attack detection
Cybercriminals are in the business of making money. They tap into the cybercrime ecosystem for expertise and intelligence to constantly refine their attacks. Arkose Labs’ proprietary detection engine provides fraud and infosec teams with smart detection for long-term attack deterrence to help them confidently detect and stop attacks using a defense-in-depth approach.
Arkose Bot Manager includes a transparent detection engine that focuses on delivering actionable insights with a clear path to remediation. It comes with more than 70 raw risk signals and more than 150 built-in insights that Arkose Labs shares with its customers. Plus it leverages the collective data from some of the world’s biggest companies, to make it easy for digital businesses to detect and stop fraud attacks.
Arkose Bot Manager uses multi-faceted machine learning and 24/7 analysis from our Security Operations Center (SOC) to classify risks in real-time and help security teams decimate large-scale, persistent attacks. It uses a combination of rate limiting, IP and device intelligence, traffic shaping, and behavioral biometrics to ensure attacks never go unnoticed. Using invisible assessments for risk-based decisioning and targeted response, it also helps improve customer experience.
With actionable risk intelligence, a behavioral analytics layer, machine learning, human touch for analysis and oversight, and the advantage of Arkose Labs Global Network, Arkose Bot Manager helps businesses detect even the most advanced attacks coming to their platforms.
To see this smart detection engine in action, please book a demo now.
6 Pillars of Future-Proof Attack Detection
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