The rapid growth of distributed networks has weakened the efficacy of signature-based security tools such as IPS/IDS in ensuring maximum security in modern networks. For this reason, SecOps teams have acknowledged the need for more sophisticated analysis tools, including Network Detection and Response (NDR) and NDR Sensors, to help detect and counter system-wide threats.
As organizations have addressed this need for more advanced real-time network analysis, SecOps team are shifting focus to the capabilities of NDR Solutions to address threats. Undoubtedly, NDR has become an almost invaluable piece of the cybersecurity ecosystem required to ensure maximum levels of network security.
The mass amounts of data persistently traversing networks have created plenty of areas for malicious users to hide. Tools themselves cannot automatically detect a threat. But Network Detection and Response solutions and their associated NDR sensors leveraging machine learning techniques have become necessary due to the speed and volume of traffic security teams must monitor.
Given the situation, it is worthwhile to understand what NDR and an NDR Sensor are, how they work, and how you can leverage their importance in securing your network.
What is Network Detection and Response (NDR)?
NDR is a cybersecurity solution designed to continuously monitor networks for any cyber threat or suspicious behavior and react at the slightest detection of any potentially malicious activity. It uses behavioral (non-signature-based) techniques such as machine learning, deep learning, and other techniques to detect anomalies on the network.
However, the threat of cyber-attacks is constant, and defending against evolving threats with NDR presents a myriad of challenges.
Security incidents have increased significantly over the past 5 years to a staggering 67%, with 43% targeting small and medium size businesses. In 2021, cyber criminals profited over $20 billion from ransomware attacks.
No solution is 100% effective against malware and botnet campaigns. Hackers are on the lookout, and malware is evolving daily to take advantage of hidden vulnerabilities to infiltrate and attack networks. SecOps teams must be proactive when hunting cyber threats.
What Type of Cyber Attacks Exist Today?
There are several variations of cyber-attacks that NDR systems must detect. NDRs require a deep understanding of potential threats and must be proactive in their defense against them.
- Malware
Malware is malicious code such as viruses, ransomware, and worms that disrupts our computer network, server, and client devices. The intention is to gain unauthorized access to the system, leak or steal sensitive information, or cause total (or partial) service disruption.
A ‘Botnet’ (short for ‘robot network’) is a collection of network-connected devices that have been compromised and work as a swarm. Unlike more basic forms of malware, in which independent singular devices are programed separately to attack, botnets are centrally coordinated by a bot-herder or Command and Control (C2) server. Typically, a botnet attack can get thousands of devices infected without obvious evidence of its existence and can theoretically be operational for years. Once an endpoint becomes part of a botnet campaign, it can be difficult to prevent the download of malicious code throughout your network.
- Distributed Denial of Service (DDoS)
A DDoS attack is a cyber-attack whereby the attacker makes it impossible for legitimate users to access computer resources by flooding the server or network services with traffic. This overwhelms the victim’s network, making it impossible for anyone else to access valuable resources.
- Man-in-the-Middle Attack
A man-in-the-middle attack is when the perpetrator positions himself between the victim and the application servicing request to eavesdrop or impersonate another party. The idea is to steal login credentials or credit card information. Common victims are those in the financial institution, e-commerce, and SaaS businesses.
How Does An NDR Sensor Work
NDR solutions work by continuously gathering and analyzing large volumes of network traffic and comparing this data with threat intelligence feeds and other relevant logs and event information. NDR relies on advanced behavioral techniques such as machine learning, deep learning, statistical analysis, and heuristics to flag anomalous activity.
Typically, NDR makes use of the techniques, and methodologies to form a threat hunting model for tackling security incidents in an organization’s network ecosystem with higher levels of efficiency. NDR can monitor traffic flows throughout a network with strategically placed NDR sensors for deeper network visibility. NDR sensors ingest traffic and extract the required intelligence for the system’s analytics engines. More advanced NDR sensors can generate layer 7 intelligence using encrypted traffic analysis that can enhance a system’s chances of detecting threats at the earliest point.
The key to an effective NDR solution is the ability to not only detect potential threats but to respond and mitigate their effect. NDR solutions are typically integrated with other network security tools like SIEM, SOAR, XDR, etc. to achieve this response.
NDR solutions support the rapid investigation, internal visibility, intelligent response, and enhanced threat detection across on-premises, cloud, and hybrid environments. Detecting attacks at the network layer works so well because it’s extremely difficult for threat actors to hide their activity. While they might switch off or evade endpoint or log data, attackers can’t tamper with network information, and they have no way of knowing if they’re being observed. Any device that communicates across the network can be immediately discovered and their activity monitored.
NetQuest’s NDR Sensor
NDR Sensors are a critical component in the NDR solution because threat hunting is only as good as the data available for analysis. When NDR Sensors are strategically placed within a network, maximum visibility can be achieved. NDR Sensors typically inspect network traffic and generate metadata, logs, or other event flags that can be analyzed by the central computer of the NDR.
NetQuest’s Streaming Network Sensor (SNS) is a market-leading NDR Sensor capable of scaling to telco-sized backbone networks. The SNS product generates unsampled (1:1 sampling) IPFIX metadata across many 100G and 10G network links providing 100% visibility into network flows. In addition to standard NetFlow records, the SNS provides the option to include layer 7 application classification and flow-based protocol-specific metadata for protocols such as DNS, TLS, QUIC, HTTP, BGP, and others. The SNS also provides a unique level of visibility for matching live network flows with indicators of compromise (IoC) using Encrypted Traffic Analysis. This includes matching JA3 fingerprints, exposing TLS handshake information, and other information used in modern threat hunting tactics.
Contact us today if interested in test driving our NDR Sensor in the NQ Cyber Lab.