Application security threats have been exponentially increasing over recently. A large number of vulnerabilities and breaches are being observed in the application layer. Security attacks are not limited to distributed denial of service (DDoS) attacks, ransomware attacks, cross-site scripting, and SQL injection, but can be in the form of viruses, Trojans, and worms as well. Protecting web applications is a challenge in itself. Identifying, monitoring, and rectifying the threats have become notoriously difficult for humans. Various artificial intelligence areas such as machine learning can be used to improve application security to derive, predict, or apply shields to identify and detect malicious user behavioral patterns.
With enhancements in pattern recognition, machine learning has enabled greater automation in web application security. As an attack is encountered over a web application, it would also be able to identify and isolate vulnerable parts of the system and notify the admin of the action it has taken.
Below are a few ways machine learning has transformed the web application security:
- Anomaly detection and predictive analysis
- Misuse detection or application security breach detection
- Data exploration
- Risk scoring
Let’s look at each of these in-depth.
1. Anomaly detection and predictive analysis
Anomaly detection works on the principle of detecting abnormal behavior of users, devices, items, or events within the network by first learning what the normal behavior looks like and then alarming the abnormal behavior as it is encountered enabling the security team to take relevant action. Anomalies, also termed as outliers, are used to detect an issue that is not normal as compared to its learned model. Several industries are adopting anomaly detection techniques in identifying medical problems, financial frauds, and fault detection in systems or machines.
To implement anomaly detection technique in application security, the primary entities are the incoming user requests, data input, or the incoming traffic that are used in a large number to teach the algorithm what “normal” looks like. Then the algorithm is applied in the web application to identify the malicious or abnormal behavior of the user input data, spotting the difference from normal data and isolating it with the rest of the system additionally alarming the organization to take the appropriate action, if required.
Furthermore, there is also a provision to create a pool of abnormal data that is collected by the anomaly detection technique and applying it in the algorithm to detect further behavior of users and devices.
Anomaly detection workflow comprises of multiple stages:
- Setting up logs: This is the base phase on which the overall technique depends. Setting up logs is very useful in deriving a meaningful context of the data to be applied in the algorithm. The logs usually contain the parameters that are required for analytical purpose.
- Preprocessing dataset: Preprocessing usually means transforming the given dataset into a format that a machine learning algorithm can easily deduce and learn from. The main aim of this phase is making the data compatible with the machine learning algorithms.
- Training data: The preprocessed dataset contains the right amount of input and output data, which is now ready to be fed into the machine learning algorithm. The main aim of this phase is to make the algorithm aware of the data attributes and the values that may be useful to identify the anomalies when the user requests are received.
- Applying machine learning algorithms: This phase is used to determine the algorithm that suits the dataset and the outcome.
- Testing (predictive model): The training data is put together to create a learning set that will serve as a base model for identifying potential threats via anomaly detection technique.
- Observing predictive model output: The final step is to test the model and analyze how accurately it works.
2. Misuse detection or application security breach detection
Misuse detection pertains to identifying the malicious behavior based on the training with labeled data. In this approach, abnormal behavior of users, devices, items, or events is defined first and then defining all other behaviors as “normal.” Misuse detection, sometimes referred to as signature-based detection, can raise alarms when specific matches for attack signatures are found. It attempts to encode the knowledge about attacks in the form of well-defined patterns and monitors for the occurrence of these patterns. This technique specifically represents knowledge about any unacceptable or unauthorized behavior and attempts to identify or detect its occurrence. While this is precisely the opposite approach to anomaly detection, it is challenging to label the data automatically based on the incoming user requests or incoming traffic. This requires a considerable amount of human labor. Misuse detection can be implemented by one of the following techniques:
- Expert systems: These gather and code the knowledge about attacks and based on that, define implication rules for future attack detection.
- Model-based reasoning systems: These combine the models of misuse with evidential reasoning to support conclusions about the occurrence of a misuse.
- Keystroke monitoring: This uses a user’s keystrokes to determine the occurrence of an attack.
3. Data exploration
Data exploration pertains to identifying the characteristics of the data that can be a serving foundation for anomaly detection technique and misuse detection technique. This is mainly carried out by using visual exploration and helps the security analysts by increasing the “readability” of the incoming user requests.
Data exploration can be conducted via a combination of automated methods and manual methods. The most commonly used automated tools are data visualization software such as MapR, Microsoft Power BI, Qlik, and Tableau for data exploration because these tools allow an organization to quickly and simply view most of the relevant attributes of a data set which helps the organization identify variables that may have interesting observations and correlations.
4. Risk scoring
Risk scoring refers to assessing the probability of the behavior of a specific user or incoming user requests in the context of maliciousness. The basic idea behind this model is that the analysis of the behavior of the user in the past is utilized to predict his or her probability of being a bad actor.
Application security and machine learning: A safer future
In today’s cyber-oriented era, machine learning has evolved vastly. Machine learning algorithms are near perfection in providing accurate results when a massive amount of data is fed into the system and is trained well to identify the malicious patterns. The only condition that is to be considered is that the data should follow consistency in terms of the format for a machine learning algorithm to work accurately.
A machine learning algorithm, when combined with other security devices or shields such as firewalls, can empower the combination to trample down the web application attack easily. In any security space, whether application security or cybersecurity, wherein a large amount of data is collected and store, machine learning plays a vital role in analyzing different use case patterns. Companies like Twistlock and Aqua Security have already adopted machine learning techniques with “threat detection during runtime” feature that has helped them identify and stop several breaches in the past few months.
The future of application security lies in machine learning as it has proved to be a robust shield to identify and protect the applications and systems from malicious attacks by analyzing use case patterns.
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