Cybersecurity protects computer systems and networks from data leaking, theft, damage, and disruption of services. Cybersecurity is challenging due to the variety and complexity of information systems. It can specifically struggle with processing data registered in IT systems.
Recently, process mining has been applied to solve this issue. Process mining can specify user-behavior anomalies and fraudulent activities in business operations by extracting and analyzing business process data.
Therefore, we will cover 12 use cases of process mining in cybersecurity.
1. Security breaches
Process mining can be useful for modeling and evaluating security breaches in a fast and data-driven manner. Also, typically security breaches target high-level activities, but process mining enables mapping both high and low-level operations to capture the interaction between these two levels.
2. Industrial control systems
Industrial control systems (ICS) are widely used to produce, monitor and control for manufacturing, transportation, and utility sectors. The ICS security efforts intend to protect these systems from cyber attacks.Cyberattacks include risks such as information confidentiality, integrity of the process and safety to personnel and property.
Process mining can detect cyberattacks in these systems and compare noticed anomalous cases. Process mining can facilitate cybersecurity efforts since it can manage asset inventory, vulnerability, user access and patch, which are crucial activities in cybersecurity.
3. IoT security
According to cybersecurity statistics, In 2020 alone, ~1.5 billion cyberattacks on IoT devices have been caught.1 IoT cyberattacks can affect network functioning and allow criminals to access military operation information or personal information for IoT houses.
The major contribution of integrating process mining with IoT is to reduce the time and effort for detecting and modeling the attacks realized. Process mining can automatically model the attacks and determine whether an attack is new or belongs to the existing list by running a conformance check. Based on the result, the system can direct the latest outbreak to the security operator or provide information on the attack traces to take action immediately.
4. Smart grids
Smart grids are operation and energy measures that target maintaining a reliable, secure, sustainable and efficient energy infrastructure. These grids are interconnected to network between producers and consumers. Cybersecurity aims to protect smart grids from user errors, equipment failures, natural disasters, espionage operations and terror attacks.
Process mining can improve cybersecurity by facilitating employee and user errors and failure detection. It also assesses compliance with grid policies and pinpoints abnormal energy usage.
Smartphones are often under threat of data leaks and financial theft. The security system identifies and prevents unauthorized users’ access to the enterprise network to secure smartphones.
Process mining can seamlessly notice malware and identify specific attacks with conformance checking.
6. Network traffic
Network traffic security monitors network activity to spot security and operational anomalies, such as ransomware. Analysts are expected to gather real-time data and historical network traffic records to achieve it.
Process mining can leverage network traffic data (e.g., DNS logs) to discover and visualize attacks, classify attacks based on the type, and detect unexpected behavior like spam attacks in network operations.
Web applications security concerns focus on breaches which can end up with service disruptions and data leaks.
Process mining generates a model of user behavior to catch malicious activities on social network websites. Process mining can also facilitate enforcing security policies by constantly monitoring the flow following the desired model.
8. Attack inspection
Attack inspection focuses on understanding how a specific attack is performed. Attack inspection aims to help prevent future attacks.
Process mining can discover the attack process, compare successful and unsuccessful attacks, and assess the impact of protection measures for the given application software.
9. Outlier user behavior
User behavior detection refers to the efforts to identify malicious user activities.
Process mining can deliver information about outlier user behaviour that is non-conforming and deviations because the software shows the entire process flow with relevant activities and people involved in the given organization.
Moreover, Process mining can be useful for multinational organizations or large enterprises with complex processes. With process mining, security teams can monitor the interaction across sub-processes in various departments and look for undesired behavior and deviations.
10. Fraudulent activities
Such activities refer to processes that do not confirm a rule or policy and are commonly found in financial services and banking.
Process mining offers user-friendly dashboards and visualizations and provides deep and detailed analysis, which consists of control flow, time and resources. As a result, it helps place and visualize frauds.
For example, in a case study, process mining identified fraud in credit applications by identifying skipped events and violations of rules attributed to conformance checks. 2
11. Quality assurance
Quality assurance (QA) aims to test the developed software and identify bugs and other problems before deployment. QA and cybersecurity are closely related because both deal with software vulnerability and weakness to manage and reduce risks.
Process mining ensures that services and software projects conform to the contract or ideal models by diagnosing bugs and assessing the conformance levels. As a result, it enables quality assurance and improve cybersecurity.
12. Error detection
Error detection is the method to determine improper behavior and its root causes, specifically within the software system. It aims to assist in deployment and maintenance.
Process mining is widely used for error discovery in IT, service-oriented systems, and blockchain. Process mining can illustrate the deployment of the software as well. Consequently, it helps comprehend and predict problems occurring in that phase.
For instance, process mining can audit blockchain smart contracts or service behavior by monitoring configuration management. It can also verify the run time of the implemented IT system through conformance checks. Process mining pinpoints the root cause behind such problems and helps the system become more robust.
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