Published on

November 24, 2023


Machine Learning Techniques for Advanced Malware Detection

As cyberthreats grow more sophisticated, traditional malware detection methods like signature analysis are proving limited. Machine learning cam solve it.
Julia Dunlea
VP of Marketing

The cybersecurity landscape has long been relying on traditional methods of malware detection. However, these methods have begun to show their age, proving less effective against sophisticated, ever-evolving cyber threats such as trojans, ransomware, backdoor attacks, adware, and other malicious software.

As a solution, machine learning – with a special emphasis on deep learning – is stepping into the limelight, promising to revolutionize the field of malware detection. 

In this article, we’ll explore malware detection using machine learning, the benefits machine learning brings to the table, and the advanced techniques that can allow us to effectively identify and neutralize malware vulnerabilities.

Let’s dive in!

The limitations of traditional malware detection methods

Traditional malware detection methods have been the cornerstone of cybersecurity efforts in the past. While these conventional approaches, such as signature-based detection, heuristic analysis, sandboxing, and the use of white and blacklists, have their merits, they also come with significant limitations – particularly in the face of increasingly sophisticated cyber threats. And as we’ll see in this article, these are limitations that can only be overcome by leveraging the prowess of artificial intelligence (AI).

Traditional malware detection typically involves two main techniques, which have been historically used by antivirus companies: static and dynamic.

Static malware detection

Static malware analysis, or signature-based detection, is a proactive security measure that scrutinizes software without executing it. It focuses on analyzing the structure, behavior, and content of files to identify any malicious code snippets or patterns that may be lurking within. 

Even though one of the static analysis’ key advantages is speed, this approach is less effective when dealing with polymorphic or obfuscated malware variants, which are advanced forms of malware that can change their code to evade detection.

Dynamic malware detection

More active and adaptive than static analysis, dynamic malware detection involves the execution of suspicious programs within a controlled environment, such as a virtual machine or sandbox, to observe their behavior and identify any malicious activities in real time.

By doing so, dynamic analysis can effectively trick malware into revealing itself through its behavior, even if it employs sophisticated evasion techniques.

Unfortunately, dynamic analysis is not without its drawbacks. It can be resource-intensive and time-consuming, requiring significant computational power and expertise to execute and analyze the malware safely. Even worse, some advanced malware types are designed to detect when they are being run in a sandbox environment, which makes them alter their behavior to appear benign and evade detection.

While static and dynamic malware detection techniques are still being used today, they present a lot of shortcomings, such as:

  • Difficulty coping with the evolving nature of malware: One of the most significant challenges is the rise of polymorphic and metamorphic malware. These sophisticated forms of malware are designed to change their code and behavior to evade signature-based detection. 
  • Limitations of signature-based detection: Signature-based detection methods struggle to detect new, unknown malware, as they rely on databases of known malware signatures (code snippets). These databases need to be frequently updated to remain effective – a task that’s virtually impossible to keep up with as the volume and variety of malware continue to grow. 
  • False positives and negatives: Traditional methods are prone to false positives and negatives. False positives occur when benign software is incorrectly identified as malware, while false negatives occur when actual malware goes undetected, which poses significant security risks. This can also occur with machine learning methods as well, but its adaptive nature can help reduce their occurrence.
  • Scalability and performance issues: As the volume and complexity of malware continue to increase, these orthodox methods can strain the resources of detection systems. This can compromise the efficiency and performance of the systems that the malware detection was designed to protect, leading to slower response times and potential vulnerabilities.

In the face of these challenges, the cybersecurity industry and online businesses are increasingly turning to machine learning. With its ability to learn and adapt from data, machine learning offers a promising solution to overcome the limitations of traditional malware detection methods.

Machine learning for malware detection

Machine learning (ML), a subfield of AI, empowers systems to learn from vast amounts of data, recognize patterns, and make accurate predictions or decisions. This adaptive capability makes it particularly well-suited for the task of malware detection. 

By training algorithms that learn and improve from data on large training datasets of both clean and malicious files, ML models can discern intricate features that distinguish benign software from malicious code. This is particularly invaluable when identifying and reacting to the evolving nature of malware.

For instance, if a user's account has been compromised, it may exhibit unusual network usage patterns or initiate transactions with suspicious servers. ML can identify such anomalies and flag them for review by a security analyst. Even better, this capability is not limited to user behavior only;  ML can also detect anomalies at the system level, such as unexpected privilege escalations or changes in system usage.

Deep learning, a branch of ML, has shown particular promise in malware detection. Deep learning models, such as deep or convolutional neural networks (CNN), can learn hierarchical representations of malware samples. These models capture intricate relationships between features, enabling them to identify complex patterns and correlations that may be missed by traditional methods.

For example, a deep learning model might identify a particular sequence of system calls that is common across different malware samples but rare in benign software. This pattern might be too complex for a signature-based method to detect, but a deep learning model can learn to recognize it from the data.

The key advantages that make ML dominate over traditional methods of malware detection include:

  • Feature extraction and selection: Machine learning algorithms can automatically extract and select relevant features from malware samples. This feature selection, which can include API calls, file characteristics, code snippets, or behavioral patterns, capture the distinguishing characteristics of malware to improve detection accuracy and reduce the occurrence of false positives.
  • Improved detection accuracy: ML can learn complex patterns and relationships from large datasets to identify subtle and previously unknown malware variants. Unlike traditional methods that rely on identifying tabulated signatures of previously identified malware, ML models learn to recognize the malicious patterns that malware use to achieve their nefarious objectives, which is particularly effective against polymorphic viruses that change their code to evade detection.
  • Behavior-based analysis: Machine learning models analyze malware behavior by processing the dynamic data collected from the execution of malware samples. This approach is highly effective in detecting evasive or obfuscated malware that exhibits abnormal behavior, providing an additional layer of defense beyond static characteristics.
  • Scalability and automation: ML algorithms are capable of processing and analyzing vast amounts of data efficiently, making them well-suited for large-scale malware detection. By automating the detection process, they can reduce the manual effort required for analyzing and classifying samples, improving the scalability of detection systems.
  • Adapting to evolving threats: ML models can be continuously trained and updated with new malware samples and threat intelligence. This adaptability allows them to keep pace with the mutant nature of malware, detecting emerging threats and incorporating new patterns or behaviors learned from updated training data.

By leveraging these advantages of ML, organizations can significantly enhance the effectiveness, accuracy, and efficiency of their malware detection across a plethora of industries.

Advanced techniques for malware detection with ML

Let’s look at a few advanced techniques that leverage the power of ML to detect malware, especially malicious code that has altered its form to evade traditional, hash-based static analysis:

String extraction

Extracting strings from files is a common technique used to identify potentially malicious code or suspicious patterns. 

At its core, string extraction refers to the process of isolating and capturing targeted text segments from a larger body of information. It involves a meticulous examination of textual data to extract relevant components that hold the key to meaningful data analysis.

Machine learning algorithms analyze the extracted strings from files to learn patterns and features that distinguish between benign and malicious strings. This can significantly enhance the efficiency and accuracy of malware detection, particularly when dealing with large volumes of data.

Antivirus inspection 

At its core, ML antivirus inspection harnesses the power of artificial intelligence to analyze vast amounts of data, spotting intricate patterns and anomalies that may indicate the presence of malware. By feeding on a constant stream of information, ML algorithms can refine their models, improving their accuracy over time. This continuous learning loop ensures that antivirus solutions remain up-to-date and effective against even the most sophisticated threats.

The benefits of ML-powered antivirus inspection include:

  • Reduction in the false positive rate, which minimizes the disruption of productivity and unnecessary user intervention. 
  • Advanced and accurate detection of previously unseen malware variants, known as zero-day threats. These threats exploit vulnerabilities that are unknown to antivirus vendors, making them particularly difficult to detect using traditional signature-based methods. 
  • Ability to extend to various vectors, including the inspections of network traffic, email attachments, and web downloads to identify malicious activity and prevent it from compromising the system.


Disassembling files involves breaking down the code into smaller components for analysis. This process can provide valuable insights into the internal workings of a piece of software, helping analysts identify malicious behaviors or anomalies. 

Machine learning can be applied to the analysis of disassembled code to identify patterns and features indicative of malware at a quicker pace than human data analysts. 

For example, ML models can learn from labeled disassembled code samples to detect common malicious behaviors, such as malicious code injection, privilege escalation, or suspicious API calls.

But how can you incorporate ML and these advanced techniques easily into your business?

Introducing Akkio for malware detection

Akkio is a leading machine learning platform that empowers businesses and cybersecurity professionals to incorporate advanced techniques into their malware detection models.

Akkio offers outstanding key benefits that you can harness for your ML-based malware detection solution, such as:

  • Effortless forecasting: Leveraging Akkio’s no-code platform, your organization can analyze patterns in historical malware attacks and make accurate predictions for advanced malware detection. This predictive modeling capability can enhance your business’ preventive measures and streamline its response to threats.
  • Rapid insights: This allows quick identification of trends in malware behavior, enabling businesses to make data-driven decisions based on these rapid insights so they can proactively defend against threats and enhance their overall cybersecurity posture.
  • Live data integration: By integrating with data warehouses, Akkio can automate malware detection and response based on live data. This ensures that your company will have up-to-date protection against emerging threats, enhancing its ability to respond swiftly and effectively.
  • Intuitive impact analysis: Akkio offers easy-to-understand insights that enable security practitioners to identify factors affecting the performance of their malware detection systems. By understanding these insights, you can make necessary adjustments to your company’s systems, improving their effectiveness and efficiency.

If you’re eager to learn how to set up a classification model for cybersecurity analysis, check this article to see exactly how to leverage Akkio’s no-code ML platform for advanced malware detection!

Detect malware more efficiently with Akkio

Machine learning is revolutionizing the detection of malware, offering increased efficiency and accuracy that is becoming essential in today's complex cybersecurity terrain. By leveraging machine learning, businesses can stay ahead of emerging cyber threats, ensuring robust and proactive data security.

Akkio stands out as an ideal platform for incorporating machine learning into malware detection efforts, offering a range of powerful features, including effortless forecasting, rapid insights, live data integration, and intuitive impact analysis. 

Akkio’s user-friendly, no-code approach makes it accessible to a wide range of users, from cybersecurity professionals to business leaders. By using Akkio, you can enhance your organization's ability to predict, detect, and respond to malware threats, ensuring robust protection against the evolving landscape of cyber threats. 

Don’t miss out on the benefits of ML. Start your ML-based malware detection journey with Akkio, today!

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