Advance vulnerability prediction with machine learning. Explore how AI can enhance proactive cybersecurity measures to mitigate potential risks. Machine learning is a field devoted to understanding and building methods that let machines “learn” – that is, methods that leverage data to improve computer performance on a set of tasks.
It’s the technology that enables Google search engines to sort out spam, ecommerce systems to recommend products, and Gmail to filter phishing messages.
A machine learning model can improve the prediction of whether a vulnerability will be exploited or not. It can also be used to predict which vulnerabilities should receive top priority for patching or mitigation. This type of prediction can be very beneficial for organizations who manage security resources efficiently, since it can help to prioritize fixes.
Supervised classification-based machine learning algorithms do well when the number of instances of each class is fairly evenly distributed. However, when the number of instances of one class far exceeds that of another class, machine learning models may experience a problem known as class imbalance, which significantly lowers predictive accuracy for the minor class and renders the model practically unusable. This is a very common issue encountered in many machine learning applications such as image classification, image recognition, natural language processing, fraud detection and more.
Existing research on vulnerability exploitation prediction demonstrates that the use of data from a variety of sources (NVD, exploit DB and marketplaces of dark web/deep web discussions) significantly improves the prediction capability compared to models using only NVD data. However, these models still suffer from poor overall performance metrics values for accuracy, precision and recall.
Some researchers have tried to address this problem by applying artificial data resampling and model overfitting techniques. They also experimented with different model architectures, application techniques and dataset and feature set selections. But these approaches hardly made a significant difference and were not capable of improving the performance of existing models.
Using machine learning at the endpoint, Trend Micro has been able to reduce false positive rates, which in turn improves the speed of detecting and blocking malicious threats. Its pre-execution machine learning capability analyzes static file features, identifies files that are suspected to be malware and blocks them, which drastically decreases the risk of the file being executed.
The key to successfully using machine learning for cybersecurity purposes is to combine it with a wide range of other traditional and non-machine-learning security techniques. This provides a necessary check and balance to ensure that the machine learning is not generating too many false positives and allows it to perform its work in an effective manner.
When attackers discover vulnerabilities in applications and networks, they use machine learning to identify attack patterns and build exploits that are capable of taking advantage of them. This threat intelligence enables them to overwhelm perimeter-based security defenses at endpoints, systems and assets not protected with the latest patches. That’s why CISOs tell VentureBeat that patch management technologies with built-in AI and ML are critical for addressing emerging threats and meeting stringent SLAs. Look for leading vendors that are several product-generations into their machine learning development to set the pace of innovation in this space.
Vulnerability management is a continuous process, and it’s not always feasible to create timely patches for every discovered vulnerability. To help prioritize them, security teams often use a combination of factors, including likelihood of exploitation and severity of the vulnerability. Unfortunately, existing models to predict whether a vulnerability has been exploited have poor accuracy, precision and recall values.
One of the key challenges to improving exploitation prediction models is extracting features that differentiate between vulnerable and non-vulnerable code. Previous work attempted to address this using text processing algorithms that represent statistical features of words, but these methods are limited in their ability to capture context. Other researchers have tried to develop more granular features by studying the source code of applications (Shar and Tan, 2013; Gupta et al., 2014; Walden et al., 2015).
A number of attempts have been made to improve model performance by retraining the model on a different dataset or using a regularized logistic regression model instead of a linear model. These improvements improve model accuracy and precision but do not address the problem of over-fitting to the major class of exploited vulnerabilities.
The SIG developed a third version of the Exploit Prediction Scoring System (EPSS), which significantly improved its predictive performance in terms of accuracy, precision and recall. This was achieved by expanding the number of sources from which a score could be generated and engineering new features. It also changed the way the EPSS model was trained to align it with real-world priorities. By focusing on predicting the probability of exploitation within the next 30 days, the model was able to produce scores that matched the typical remediation window of the SIG’s practitioners.
Detecting Potential Vulnerabilities
Machine learning algorithms can power a number of cybersecurity processes, from recognizing patterns to detecting unknown threats. However, the types of algorithms that power these tasks vary significantly. Those that power machine learning for cybersecurity need to be capable of handling a large volume of data, parsing it in order to understand its structure and context. They also need to be able to recognize and adapt as the threat landscape changes, making them more effective than conventional systems that rely on simple rules and heuristics.
Vulnerability exploitation prediction models are no exception. They must achieve high accuracy, precision and recall metrics to be useful in practice. This has proved challenging. Despite the recent surge in vulnerability exploitation detection methods, the overall performance of many models remains low for various reasons.
Several machine learning approaches have been proposed to overcome this problem. One common approach uses deep learning. This is a subfield of machine learning that utilizes artificial neural networks that are composed of multiple layers. Data travels sequentially through these layers, transforming it as it goes. This allows the model to build a meaningful representation of the data and make predictions based on it.
Another method focuses on learning the vulnerable programming pattern. It does so by training a classifier with labeled vulnerable code and applying it to identify potentially vulnerable code snippets. This method is relatively easy to implement and requires smaller computational resources than anomaly detection, but it suffers from a dependency on labeled vulnerabilities datasets.
A more efficient approach is to use a hybrid model that leverages RNN and LSTM neural networks. The former can process sequences of data with a very high speed, while the latter can learn local features that are important in the detection task. Vulnerability exploitation detection requires the capability to distinguish between a vulnerable and clean program, which is usually defined by a sequence of control statements or misused variables. The inter-procedural statement-level granularity of these models makes them easier to detect, but they can be difficult to interpret and require a significant amount of data to be analyzed.
Implementing Machine Learning
While machine learning can help improve the prediction of exploited vulnerabilities, it’s not an answer to all security challenges. For example, as we learned in high school, copying another student’s answers to a test question can lead to plagiarism and other violations of academic ethics. The same applies to cyberthreats and other security risks. To make accurate predictions, the machine must have ample data to work with. Without this, the model is prone to noise and other limitations that can lead to poor results.
To overcome these limits, ML models require careful data preparation and optimization to achieve the best performance. To this end, researchers have developed several algorithms to process source code to extract relevant features for vulnerability detection. These feature extraction methods include path generation, filtering and vulnerability context rules. However, these processes are time-consuming and may not be effective against all types of malware.
For instance, an attack may use different libraries to exploit a vulnerability and the code containing these libraries is difficult to analyze manually. Therefore, a more efficient approach is needed to detect these vulnerabilities. The ML method has been shown to be effective in this regard.
Several studies have used ML to classify detected software vulnerabilities. However, these methods are not optimal because they only consider the presence of certain metric values in the code. Moreover, they fail to take into account the interaction between multiple code segments. In addition, the ML algorithms must be trained to recognize patterns in the source code and identify vulnerable features.
As such, this study has proposed a new method that incorporates machine learning to automate the classification of detected vulnerabilities. The method uses a deep learning framework to predict whether the application contains vulnerability components. This is accomplished by identifying the low-dimensional distributed representations (i.e., embeddings) of the Dark Web discussions and incorporating them into the model.
The experimental evaluation shows that the proposed approach performs better than traditional classification methods, including k-nearest neighbors, Naive Bayes and decision tree. This suggests that the proposed algorithm has the potential to be a good tool for automatically detecting vulnerable applications.