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ReliaQuest's ShadowTalk is a weekly podcast featuring discussions on the latest cybersecurity news and threat research. ShadowTalk's hosts come from threat intelligence, threat hunting, security research, and leadership backgrounds providing practical perspectives on the week's top cybersecurity stories.
February 20, 2024
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Editor’s note: This is part two of a series on threat detection.
In the initial blog of this series, “Detection 101: Top 5 Detection Data Sources,” we covered the data sources foundational to successful SecOps and their corresponding threat detections. In this second installment, we delve into the important detections needed to locate and identify malware and ransomware.
Malware is a broad term that encompasses various types of malicious software designed to harm or exploit computer systems, networks, or users. This includes viruses, trojans, worms, adware, spyware, and of course ransomware.
Ransomware is a specific type of malware that encrypts files or locks down a victim’s computer or network, rendering it inaccessible until a ransom is paid.
Endpoints are the common initial access point for threat actors and are a cornerstone of malware threat detection. Most malware, from ransomware to trojans, attempts to compromise endpoints, which is why endpoint detection and response (EDR) solutions play a major role in protecting against these threats. When it comes to detecting malware attempting to compromise an endpoint, endpoint telemetry can originate from the endpoint antivirus or EDR solution, as well as from the endpoint operating system (OS).
Antivirus and EDR technology use a variety of techniques to locate malware, including behavioral analysis, signature-based detection, machine learning, and AI. Once the EDR technology locates a potential threat, it fires an alert to trigger an investigation. For example, if an EDR or antivirus solution detects a QakBot banking trojan, it will trigger an alert to prompt further investigation and response (refer to the table below).
While EDRs and operating systems are the major avenue for detecting malware, they are not the only data source that can inform detections. Multiple data sources are needed to provide optimal detection coverage for malware. For example, a threat signature for ransomware can be detected in multiple tools including network intrusion detection system or a cloud access security broker (CASB), or within a user behavior analytics system. Having multiple layers of protection can increase the likelihood of detecting a malware threat (refer to the table below).
In addition, combining endpoint data with data from other sources provides a more comprehensive and high-fidelity approach to detecting malware. For example, when malware specifically targets endpoints, it gains initial access and then establishes communication with command-and-control (C2) infrastructure to receive further instructions. This C2 infrastructure (domain or IP address) can be identified in threat intelligence feeds. To provide a high-fidelity alert, detection rules can be created by combining an endpoint antivirus alert with network telemetry (forward proxy, flow data, and firewalls), all of which are informed by threat intelligence (refer to the table below).
An optimal approach to detecting malware uses as many relevant data sources and rules as possible. Deploying detections across all elements of the attack surface that might have visibility to malware or ransomware ensures that you detect and mitigate threats promptly.
Threat intelligence plays a significant role in detecting malware by providing insights and actionable information to security teams. As mentioned above, threat intelligence helps identify known indicators of compromise (IoCs). These IoCs include specific patterns, signatures, or behaviors associated with known malware strains or attack techniques. By leveraging threat intelligence feeds, your security tools can cross-reference these IoCs to proactively detect malware. This enables organizations to quickly identify and block malicious IP addresses, domain names, file hashes, URLs, or other patterns found in network traffic, helping prevent malware infections.
In addition, threat intelligence provides contextual information and analysis about the threat landscape. It goes beyond simple indicators and includes details about the tactics, techniques, and procedures (TTPs) employed by threat actors, their motivations, infrastructure, and target industries. This contextual understanding helps security professionals recognize and interpret the behavior and intentions of attackers. With this knowledge, organizations can tune their detection mechanisms for more proactive malware detection.
Threat intelligence also enhances incident response efforts by providing actionable information about specific malware strains or attack campaigns. This enables incident response teams to effectively detect, isolate, and mitigate malware infections, minimizing the impact of the attacks and enabling faster recovery.
The initial blog of this series explained the importance of measuring the breadth and diversity of data sources along with measuring the mean time to resolve (MTTR) incidents. Beyond those baseline metrics, to better understand how your security operations are performing when detecting malware and ransomware, you should also examine:
The next blog in our series will focus on phishing and business email compromise (BEC) threat detection. We’ll provide actionable guidance and practical tips to improve your phishing and BEC detection capabilities. Security is an ongoing journey, and by continuously improving your detection strategies, you can adapt to the evolving threat landscape and protect your valuable assets.
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