Achieved 100% protection in 2024
Stop advanced cyber
threats with one solution
Cynet’s All-In-One Security Platform
- Full-Featured EDR and NGAV
- Anti-Ransomware & Threat Hunting
- 24/7 Managed Detection and Response
Key Malware Detection Techniques
Last Updated:
June 2, 2025
Share on:
Threat actors employ malicious software, known as malware, to perform certain malicious activities. There are numerous types of malware, each deployed to achieve different objectives. For example, spyware aims to gather information from computing devices, trojans aim to gain a persistent hold on a computer system, and ransomware aims to encrypt information and extort its owners. With malware detection tools, organizations can mitigate these risks. Let’s see how.
What is Malware Detection?
Malware detection involves using techniques and tools to identify, block, alert, and respond to malware threats. Basic malware detection techniques can help identify and restrict known threats and include signature-based detection, checksumming, and application allowlisting. Advanced malware detection tools employ artificial intelligence (AI) and machine learning to proactively look for and identify new and unknown malware threats.
This is part of an extensive series of guides about malware protection.
Why Do Organizations Need Malware Detection?
Malware is one of the most common and dangerous cyber threats. It is capable of causing data breaches, system downtime, financial loss, and reputational damage. Malware detection tools can identify and alert about malware attempting to attack your systems, strengthening your security posture and ensuring smooth business operations while meeting compliance.
Since “malware” is an umbrella term for various kinds of tactics, techniques, and procedures (TTPs) used for attacks, the security tools designed to detect it need to be able to adapt to this variety. This means that traditional security tools like firewalls or basic antivirus software cannot always prevent all types of malware.
Modern malware detection solutions often use behavioral analysis, machine learning, and threat intelligence to detect and block suspicious activity. This ensures real-time visibility into threats, helping prevent breaches before they escalate. In case of an attack, they enable faster incident response.
Stop advanced cyber
Rated 4.8/5
2025 Leader
Top Malware Detection Techniques
How do malware detection tools work? Here’s a drill-down of the main techniques:
Signature-Based Detection
Signature-based detection uses the unique digital footprint, known as a signature, of software programs running on a protected system. Antivirus programs scan software, identify its signature, and compare it to the signatures of known malware.
Antivirus products use a large database of known malware signatures, typically maintained by a security research team operated by the antivirus vendor. This database is frequently updated and the latest version is synchronized with protected devices.
When an antivirus program identifies software that matches a known signature, it stops the process and either quarantines or deletes it. This is a simple and effective approach to malware detection and is important as the first line of defense. However, as attackers become more sophisticated, the signature-based approach cannot detect a wide variety of newer threats.
Checksumming
This method is a type of signature analysis that involves calculating cyclic redundancy check (CRC) checksums. Checksumming helps verify that files are uncorrupted by generating a numerical value based on the data content. This is a “digital footprint” that changes when the data is altered, indicating malware.
Checksumming addresses the main drawback of signature-based detection: the use of polymorphic malicious advertisements to avoid detection by signature-based identification methods. Polymorphic viruses can change themselves when replicating, eliminating consistent search strings – usually, the hacker encrypts random decryption command sets in the form of non-constant keys in the virus code.
The absence of a detectable signature in the variable code requires other malicious code detection techniques, such as:
- Statistical analysis – analyzes the frequency of processor commands to determine if a file is infected.
- Cryptanalysis – known-plaintext cryptanalysis decodes encrypted viruses using an equation system (like the classic cryptographic technique of decoding text without a decryption key). The cryptanalysis system reconstructs the decryption program’s algorithm and keys, applying the algorithm to encoded fragments to decode the overall body of the encrypted virus.
- Heuristics – a malware detection team scans and analyses behavioral data to identify anomalous activity. The team must search for malicious code associated with suspicious behavior, such as code served to thousands of users within a few minutes. The security team can then prioritize and further investigate suspicious incidents.
- Reduced masks – the malware detection team can use elements within the encrypted virus body to circumvent the need for an encryption key when obtaining static code. The static code produced can reveal the malware’s signature or mask.
Application Allowlisting
Application allowlists (aka whitelists) are the opposite of the attack signature approach. Instead of defining which software the antivirus program should block, it maintains a list of approved applications and blocks everything else.
This solution is not perfect, but it can be highly effective, especially in high-security environments. It is quite common for legitimate applications to have security vulnerabilities or introduce unneeded features that increase the attack surface. In some cases, the application itself is benign, but its use could expose the device to threats – for example, in some environments, there may be a need to block web browsing and email.
Application allowlisting works best with devices that are strictly task-focused, such as web servers and Internet of Things (IoT) devices.
Stop advanced cyber
threats with one solution
Cynet’s All-In-One Security Platform
- Full-Featured EDR and NGAV
- Anti-Ransomware & Threat Hunting
- 24/7 Managed Detection and Response
Achieved 100% protection in 2024
Rated 4.8/5
2025 Leader
Machine Learning Behavioral Analysis
The above techniques are known as “static” detection techniques because they rely on binary rules that either match or do not match a process running in the environment. Static malware detection cannot learn, it can only add more rules or fine-tune its rules over time to increase coverage.
By contrast, new dynamic techniques, based on artificial intelligence and machine learning (AI/ML), can help security tools learn to differentiate between legitimate and malicious files and processes, even if they do not match any known pattern or signature. They do this by observing file behavior, network traffic, frequency of processes, deployment patterns, and more. Over time, these algorithms can learn what “bad” files look like, making it possible to detect new and unknown malware.
AI/ML malware detection is known as “behavioral” detection because it is based on an analysis of the behavior of suspect processes. These algorithms have a threshold for malicious behavior, and if a file or process exhibits unusual behavior that crosses the threshold, they determine it to be malicious.
Behavioral analysis is powerful, but it can sometimes miss malicious processes or incorrectly classify legitimate processes as malicious. In addition, attackers can manipulate AI/ML training processes. In several cases, attackers fed specially-crafted artifacts to a behavioral analysis mechanism to train it to recognize malicious software as safe.
Related content: Read our guide to advanced malware detection (coming soon)
Tips From the Expert
In my experience, here are tips that can help you optimize malware detection in your organization:
- Use deception-based malware detection. Implement deception technologies, such as decoy files or honeypots, to lure malware into revealing itself. These traps can trigger alerts when malware attempts to interact with seemingly valuable files, exposing threats that bypass traditional detection.
- Deploy memory-based malware detection. Since advanced malware can evade file-based detection by operating solely in memory, using tools that monitor in-memory processes is crucial. This ensures that fileless malware, which does not leave a footprint on disk, is caught and neutralized.
- Employ threat intelligence integration into detection tools. Incorporate real-time threat intelligence feeds directly into EPP and EDR solutions to continuously update your detection capabilities with the latest indicators of compromise (IoCs) for emerging malware strains and zero-day attacks.
- Focus on lateral movement detection. Many malware infections start at a single endpoint but aim to spread laterally across the network. Implement lateral movement detection strategies, such as monitoring internal traffic and using EDR’s threat-hunting capabilities to identify unusual patterns.
- Use multifactor entropy analysis on suspicious files. To detect malware using polymorphic techniques, apply entropy analysis to assess the randomness of file structures. Malware often uses encryption or obfuscation to hide its true nature, and entropy analysis can uncover these hidden threats.
Advanced Malware Detection Technologies
Main Signs You Might Have a Malware Problem
If your network isn’t operating as usual and you’re seeing new messages you haven’t seen before and changes in performance, it’s not just you. You might be under a malware attack.
Here are 9 signs to monitor that require taking action to detect and respond to malware.
- Sluggish Performance – Malware hogs system resources. If devices suddenly start running slowly and take a long time to boot, open applications, or load web pages, there might be malware in the background.
- Frequent Crashes or Freezes – Malware can interfere with system processes. This might lead to random crashes, freezes, or the Blue Screen of Death on Windows machines.
- Unexpected Pop-ups and Ads – Malware might be used by attackers as a stepping stone to gain credentials, hijack sessions, and get access to sensitive information. If you’re seeing excessive ads, redirects to strange websites, or pop-ups even when you’re not browsing the web, especially if they’re hard to close or trigger more pop-ups, that’s a red flag.
- Disabled Security Tools – Some malware strains turn off security tools to avoid detection once they’re in the network. If your antivirus, firewall, or any other tool is suddenly disabled and you didn’t do it, malware might have done it for you
- Unknown Programs or Processes – Malware often installs additional software without consent to harvest information, Secrets, credentials, and more. If you notice new toolbars and programs, this could be the malware that installed them.
- Unusual Network Activity – Malware can send data in the background. If your bandwidth usage is unusually high even when you’re idle, this might be malware.
- Browser Redirects and Search Engine Hijacks – Malware can attempt to take over your browser sessions or exploit the browser for phishing. If your default search engine, homepage, or browser settings change on their own, it could be a sign of a browser hijacker or adware.
- Unexplained Account Activity – Malware attempts account takeover for information extraction, phishing, and leveraging credentials to progress laterally. If you see emails you didn’t send, posts you didn’t make, logins from unknown locations, or modified and deleted files, this is a fairly certain sign of malware (or insider threat).
- Ransom Messages or Unreadable Files – Most obviously, if you suddenly can’t open files and get a ransom note instead, you’ve been hit by ransomware.
Advanced Malware Detection Technologies
While many organizations rely on legacy antivirus as their malware detection strategy, mature security organizations typically use two types of advanced solutions to defend against malware: endpoint protection platforms and endpoint detection and response solutions.
Endpoint Protection Platforms (EPP)
Endpoint Protection Platforms (EPP) are deployed on endpoints such as employee workstations, servers, and cloud-based resources. They serve as the first line of defense that can identify threats and block them before they cause damage to sensitive assets.
EPPs use multiple techniques to detect and block malware:
- Static analysis – EPPs leverage traditional static analysis methods to identify known malware strains and allow/deny applications flagged by administrators.
- Behavioral analysis – EPPs add behavioral analysis to detect unknown threats or known malware that uses evasion tactics like mutation or encryption.
- Sandboxed inspection – EPPs can run suspicious content in a sandbox, isolated from the main operating system. This makes it possible to “detonate” a file, observe its behavior, and confirm if it is really malicious or not.
- Content Disarm and Reconstruction (CDR) – EPPs make it possible to remove malicious elements of legitimate content and allow the user to access the content itself. For example, if a Word document has a malicious macro, CDR can remove the macro and allow the user to access the file, instead of blocking it entirely.
In addition to these techniques, once the malware is detected, EPPs can actively protect the environment, for example, by isolating the endpoint from the network.
Endpoint Detection and Response (EDR)
EDR tools complement EPP solutions by allowing security teams to identify and respond to attacks on endpoint devices. If EPP failed to contain a threat, EDR makes it possible to:
- Triage and investigate alerts – EDR provides rich data from endpoints that allows security analysts to identify signs of an attack and investigate them to confirm a security incident.
- Threat hunting – EDR makes it possible to proactively search endpoints and explore relevant data for signs of a breach.
When an analyst confirms a threat on an endpoint, they can use the EDR platform for incident response. For example, analysts can quarantine all devices affected by malware, wipe and reimage infected endpoints, and run automated security playbooks. Security playbooks can be used to coordinate a response to a malware threat across multiple security tools, including firewalls and network segmentation, intrusion prevention systems (IPS), and email security. Many EDR solutions incorporate EPP capabilities.
Advanced Malware Detection and Protection with Cynet
The Cynet All-in-One Advanced Threat Detection and Response platform provides protection against threats, including zero-day attacks, advanced persistent threats (APT), advanced malware, and trojans that can evade traditional signature-based security measures.
Block exploit-like behavior
Cynet monitors endpoints’ memory to discover behavioral patterns that are typical of exploitation such as an unusual process handling request. These patterns are common to the vast majority of exploits, whether known or new, and provide effective protection even from zero-day exploits.
Block exploit-derived malware
Cynet employs multi-layered malware protection that includes ML-based static analysis, sandboxing, and process behavior monitoring. In addition, they provide fuzzy hashing and threat intelligence. This ensures that even if a successful zero-day exploit establishes a connection with the attacker and downloads additional malware, Cynet will prevent this malware from running, so no harm can be done.
Uncover hidden threats
Cynet uses an adversary-centric methodology to accurately detect threats throughout the attack chain. Cynet thinks like an adversary, detecting behaviors and indicators across endpoints, files, users, and networks. They provide a holistic account of the operation of an attack, irrespective of where the attack may try to penetrate.
Accurate and precise
Cynet uses a powerful correlation engine and provides its attack findings with near-zero false positives and is free from excessive noise. This simplifies the response for security teams so they can react to important incidents.
You can carry out automatic or manual remediation, so your security teams have a highly effective yet straightforward way to detect, disrupt, and respond to advanced threats before they have a chance to do damage.
Learn more about Cynet’s EPP and EDR Solutions.
See Our Additional Guides on Key Cybersecurity Topics
Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of Cybersecurity
Network Attacks
Learn about the main cyber attacks that threaten security of modern networks.
- Cobalt Strike: White Hat Hacker Powerhouse in the Wrong Hands
- Mimikatz: World’s Most Dangerous Password-Stealing Platform
- Understanding Privilege Escalation and 5 Common Attack Techniques
Zero-Day Attack
Learn about the risk of unknown threats that can hit organizations before they are discovered by vendors and researchers.
- 5 Ways to Defend Against Zero-Day Malware
- Zero-Day Attack Prevention: 4 Ways to Prepare
- Zero-Day Exploit: Recent Examples and 4 Detection Strategie
XDR
Learn how extended detection and response (XDR) solutions provide a single platform for responding to endpoint, cloud, email, and network-based threats.
- XDR Security Solutions: Get to Know the Top 8
- Cortex XDR by Palo Alto: Architecture & Capabilities Overview
- Cisco XDR: SecureX Suite at a Glance
Advanced Persistent Threat
Learn how organized crime groups and nation state attackers wage coordinated cyber attacks against organizations.
- Advanced Persistent Threat (APT) Attacks
- APT Security: Warning Signs and 6 Ways to Secure Your Network
- How and Why You Need to Protect
EDR
Learn how endpoint detection and response (EDR) solutions can help immediately contain breaches on endpoint devices.
FAQs
How do signature-based detection systems work?
They compare files and activities against a database of known malicious code “signatures.” These signatures are byte sequences, file hashes, or specific behaviors that are extracted from previously identified malware samples. When a file or process matches a known signature, the system flags it as malicious. This method is fast and effective for catching well-documented threats. However, it cannot detect unknown or modified malware.
What techniques detect zero-day or polymorphic malware?
Heuristic analysis, behavior monitoring, machine learning, and sandboxing. Heuristics analyze code structures and logic to detect suspicious traits. Behavioral detection observes how a file behaves once executed, looking for anomalies like privilege escalation or unauthorized encryption. Machine learning models are trained on large datasets to spot previously unseen threats. Sandboxing executes suspicious files in isolated environments to observe their actions safely.
Can antivirus alone protect against all malware threats?
While traditional antivirus tools are useful for blocking known threats, they often lack the behavioral analysis and real-time response capabilities required to detect stealthy or novel attacks. A layered security approach that combines antivirus with EDR, threat intelligence, behavioral analytics, and strong endpoint policies is required instead.
What role do threat intelligence feeds play in malware detection?
Threat intelligence feeds provide real-time, curated data on emerging threats, including new malware signatures, command-and-control (C2) server addresses, malicious domains, and known attacker tactics. Integrating this intelligence into detection systems helps security tools and teams prioritize alerts, hunt for threats more effectively, and stay ahead of evolving attack techniques.
How do EDR solutions enhance traditional malware detection?
EDRs continuously monitor endpoints for suspicious behavior, hunt for threats, run suspicious processes in sandboxes, and run automated incident response. This means EDR not only catches more sophisticated threats but also allows security teams to investigate and contain incidents rapidly.
How can I test and validate my malware detection system’s effectiveness?
Examine how your tool is mapped in recognized standards like MITRE ATT&CK, conduct penetration tests, analyze false positive/negative rates, and monitor detection metrics over time.
