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Today, most Network Detection and Response (NDR) solutions rely on traffic mirroring and Deep Packet Inspection (DPI). Traffic mirroring is typically deployed on a single-core switch to provide a copy of the network traffic to a sensor that uses DPI to thoroughly analyze the payload. While this approach provides detailed analysis, it requires large amounts of processing power and is blind when it comes to encrypted network traffic. Metadata Analysis has been specifically developed to overcome these limitations. By utilizing metadata for analysis, network communications can be observed at any collection point and be enriched by the information providing insights about encrypted communication. Network Detection and Response (NDR) solutions have become crucial to reliably monitor and protect network operations. However, as network traffic becomes encrypted and data volumes continue to increase, most traditional NDR solutions are reaching their limits. This begs the question: What detect...

Incident response is a structured approach to managing and addressing security breaches or cyber-attacks. Security teams must overcome challenges such as timely detection, comprehensive data collection, and coordinated actions to enhance readiness. Improving these areas ensures a swift and effective response, minimizing damage and restoring normal operations quickly. Challenges in incident response Incident response presents several challenges that must be addressed to ensure a swift and effective recovery from cyber attacks. The following section lists some of these challenges. Timeliness : One of the primary challenges in incident response is addressing incidents quickly enough to minimize damage. Delays in response can lead to more compromises and increased recovery costs. Information correlation : Security teams often struggle to effectively collect and correlate relevant data. Without a comprehensive view, understanding the full scope and impact of the incident becomes difficu...

In today's rapidly evolving cyber threat landscape, organizations face increasingly sophisticated attacks targeting their applications. Understanding these threats and the technologies designed to combat them is crucial. This article delves into the mechanics of a common application attack, using the infamous Log4Shell vulnerability as an example, and demonstrates how Application Detection and Response (ADR) technology effectively safeguards against such zero-day threats. View the Contrast ADR white paper The anatomy of a modern application attack: Log4Shell To illustrate the complexity and severity of modern application attacks, let's examine an attack against the infamous Log4Shell vulnerability ( CVE-2021-44228 ) that sent shockwaves through the cybersecurity world in late 2021. This attack is a prime example of attack chaining, leveraging JNDI Injection, Expression Language (EL) Injection and Command Injection. Technology note : The CVE program catalogs, which publicly...

Did you know that Network Detection and Response (NDR) has become the most effective technology to detect cyber threats? In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false alerts and efficient threat response. Are you aware of  Network Detection and Response (NDR)  and how it's become the most effective technology to detect cyber threats?  NDR massively upgrades your security through risk-based alerting, prioritizing alerts based on the potential risk to your organization's systems and data. How? Well, NDR's real-time analysis, machine learning, and threat intelligence provide immediate detection, reducing alert fatigue and enabling better decision-making. In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false positives and efficient threat response. Why Use Risk-Based Alerting? Risk-based alerting is an approach where security alerts and responses are prioritized based on the level of risk they pose to an organization's syst...

Behavioral analytics, long associated with threat detection (i.e. UEBA or UBA), is experiencing a renaissance. Once primarily used to identify suspicious activity, it's now being reimagined as a powerful post-detection technology that enhances incident response processes. By leveraging behavioral insights during alert triage and investigation, SOCs can transform their workflows to become more accurate, efficient, and impactful. Fortunately, many new cybersecurity products like AI SOC analysts are able to incorporate these techniques into their investigation capabilities, thus allowing SOCs to utilize them into their response processes. This post will provide a brief overview of behavior analytics then discuss 5 ways it's being reinvented to shake up SOC investigation and incident response work. Behavior Analysis is Back, But Why? Behavioral analytics was a hot topic back in 2015, promising to revolutionize static SIEM and SOC detections with dynamic anomaly detection to uncover t...

In 1999, Bruce Schneier wrote, "Complexity is the worst enemy of security." That was 19 years ago (!) and since then, cyber security has only become more complex. Today, controls dramatically outnumber staff available to support them. The Bank of America has a $400-million cyber budget to hire security staff and implement a broad array of products. But what if your budget and sophistication is just a tiny fraction of the Bank of America's? The remaining 99% of organizations understand that they don't have sufficient protection for their internal network, but they also realize that to be sufficiently secured they need to buy multiple solutions and hire a large team to maintain it – which isn't an option. So they either stay with just an AV or buy a point solution to defend a specific part of their internal environment from particular types of attacks – only to later find out it doesn't meet what they really need. Cynet wants to change all that. ...