Malware is a threat for businesses everywhere. Short for “malicious software,” malware is any intrusive program that exploits system vulnerabilities to wreak havoc on a computing system. You need robust malware detection tools to prevent this from happening to your business.
Even the best malware detection methods can fall short, however, because malware is constantly evolving. New malware variants are developed all the time, and cyber criminals frequently change their tactics and techniques.
How can you stay ahead of these threats? There is one state-of-the-art malware detection system you can use: deep learning.
This article explores how deep learning and artificial intelligence can help you uncover malware threats and thwart them before they can damage your organization.
Why Is it Critical to Detect and Mitigate Malware?
A malware infection can be disastrous for your organization. It can cripple your network and systems, as well as destroy, delete, corrupt, or exfiltrate your sensitive data.
Malware is also exceedingly dangerous because it comes in many forms. Worms, Trojans, viruses, bots, and adware are some types of malware that attackers can use to damage your organization.
Another malware variant popular with cyber criminals is ransomware. Ransomware attacks increased by 93 percent from 2020 to 2021, affecting companies, governments, and supply chains worldwide.
Your organization needs to update its detection and mitigation strategies to keep up with these sophisticated and ever-more frequent malware threats. One such strategy is deep learning.
What Is Deep Learning?
Deep learning is a machine learning (ML) technique. ML is the idea that machines can be taught to “learn” and think like humans. In ML, a computer model or algorithm learns how to perform specific tasks to achieve a particular goal.
The computer may learn how to drive a car, distinguish and classify objects, or translate text from one language to another. Machine learning algorithms are trained with a large set of labeled training data to produce more and more accurate results over time.
Unlike many machine learning models, deep learning models eliminate data pre-processing tasks. This means they ingest and process unstructured data such as text and images, to extract features from this data without the need for human inputs.
Deep neural networks, also known as artificial neural networks (ANN), are an essential component of deep learning applications. An ANN is a type of advanced ML algorithm that tries to mimic the human brain to recognize, classify, and describe objects within a dataset.
The deep in deep learning refers to the number of layers in the ANN. Where traditional neural networks contain two to three hidden layers, deep networks can have as many as 150. ANNs with a single layer can still make predictions, albeit approximate ones. Adding more hidden layers increases the accuracy of the model’s predictions.
Deep learning is already used in many artificial intelligence (AI) applications to power industrial automation and automate analytical and physical tasks. Deep learning models aid with language translations, medical diagnoses and research, computer vision and image recognition, and even law enforcement.
These learning methods also power many existing and emerging products, such as:
- Digital assistants;
- Voice-enabled TV remotes;
- Self-driving cars.
In recent years, many enterprise security solutions have adopted the deep learning approach to detect and address malware.
Deep Learning for Object Recognition and Image Classification
Convolutional neural networks (CNNs), a type of ANN, are often used for image processing and identification applications. There’s no need to manually identify or extract image features with convolutional networks. A human operator is not required to define the patterns the model should look for in the image.
Instead, the model will learn patterns in the images, extract features from them, and classify data into various classes. This automated feature extraction capability also makes CNNs and deep learning valuable for malware detection, classification, analysis, and evasion.
How Deep Learning Is Used to Detect Malware
The data-driven deep learning process involves CNNs looking at and learning from the raw bytes of Windows Portable Executable (PE) files. PE files are used for executables (.EXE, .SCR) and dynamic link libraries (.DLL) in Windows-based systems.
By looking at a training set of PE files, CNNs can accurately detect and classify malware. In fact, CNNs can be applied to many use cases in malware analysis using bytes sequence, grayscale images, API call sequence, structural entropy, and HTTP traffic.
A CNN looks at raw bytes to identify new patterns without completing domain-specific feature extraction or pre-processing. It then performs “representation learning” to identify patterns across malware families, learn their features automatically, and detect and classify malware.
The malware’s low-level features are detected by the early layers of the CNN. Subsequent convolutions layers combine these features into a more complete representation of the malware.
Supervised Deep Learning to Detect Malware
In supervised deep learning, both the data and the right answers for each object in the dataset are available. The objects are represented with a feature that relates to file content or behavior, such as file statistics or a list of API functions used.
Each object is also mapped to the correct answer that’s labeled. This label could be a benign file, or it could be malware. It can also have a more precise classification such as “trojan,” “adware,” “ransomware,” “virus,” and so forth.
Supervised deep learning consists of two stages:
- Train a model and fit it to available training data;
- Apply the trained model to new malware samples and make predictions and decisions.
During training, both benign and malicious executables are used to train the model and make it more predictive. Importantly, at this phase the model only produces predictions. It does not make decisions about the potential malware nature of an unknown executable.
After the model is trained, it makes autonomous decisions for malware detection based on its predictions about whether the executable is benign or malware.
With a large enough dataset, the model can be trained to read any new malware sample or object. For this, effective processes are vital to collect and label new samples, enrich training datasets, and perform model retraining.
Important Considerations for Supervised Deep Learning and Malware Detection
The ability of a deep learning classification model to detect malware accurately depends on a few considerations.
It’s crucial to select suitable training models such as neural networks or decision trees to give the most accurate answers over the set of reference objects. It’s also important to focus on low false-positive rate (FPR) models, where the FPR can be fixed easily without having to retrain the model.
Mistakes in this area could result in profound unwanted consequences. For example, the malware may not be adequately detected and may infect its target system. Or an OS driver might get removed by mistake, affecting the system’s operations.
It’s also essential to train the models on data that correctly represents or mimics the real-world conditions where the model will actually work. This representative dataset helps the model determine which features are relevant to accurately identify the malware.
Benefits of Deep Learning Malware Detection
Traditional ML-based malware classification and detection models rely on handcrafted features selected based on human inputs. Although essential, feature engineering can be time-consuming and costly. Plus, handcrafted features sometimes don’t generalize well to novel malware.
CNNs are helpful for automated malware detection and analysis. The raw bytes model eliminates the need for feature selection or engineering since it automatically performs end-to-end malware classification with real-world data. Moreover, it can identify malware families as well as novel malware threats and advanced malware attacks.
Once the model is trained, it captures the various characteristics of different types of malware, which informs your organization’s cybersecurity and malware detection program.
With an appropriate dataset and the right amount of training, CNN models can be applied for early malware identification, categorization, and triage. This is essential for optimal threat prioritization and to guide mitigation efforts.
With visualizations, heatmaps, and gradient activation, human analysts can get insights into CNN decisions. These insights help identify patterns across malware families and improve their malware analysis and classification capabilities.
Reduce the Risk of Malware Attacks With Reciprocity Reciprocity ROAR
With deep learning-powered malware detection, your organization can keep the threat of malware at bay. Alongside deep learning, your computer security arsenal should also include a comprehensive risk management platform such as Reciprocity Roar.
The ROAR platform provides a single source of truth to help you track malware threats, build a strong cybersecurity foundation, and reduce cyber exposure. With ROAR’s insights and metrics, you can better understand the malware threat and adversarial landscape to protect your systems and secure your data.