by Madhurjya Chowdhury
October 1, 2021
Today, deploying robust cybersecurity solutions is unfeasible without significantly depending on machine learning. Simultaneously, without a thorough, rich, and full approach to the data set, it is difficult to properly use machine learning.
MI can be used by cybersecurity systems to recognise patterns and learn from them in order to detect and prevent repeated attacks and adjust to different behaviour. It can assist cybersecurity teams in being more proactive in preventing dangers and responding to live attacks. It can help businesses use their assets more strategically by reducing the amount of time invested in mundane tasks.
Machine Learning in Cyber Security
ML may be used in different areas within Cyber Security to improve security procedures and make it simpler for security analysts to swiftly discover, prioritise, cope with, and remediate new threats in order to better comprehend previous cyber-attacks and build appropriate defence measures.
Automating Tasks
The potential of machine learning in cyber security to simplify repetitive and time-consuming processes like triaging intelligence, malware detection, network log analysis, and vulnerability analysis is a significant benefit. By adding machine learning into the security workflow, businesses may complete activities quicker and respond to and remediate risks at a rate that would be impossible to do with only manual human capabilities. By automating repetitive operations, customers may simply scale up or down without changing the number of people required, lowering expenses.
AutoML is a term used to describe the process of using machine learning to automate activities. When repetitive processes in development are automated to help analysts, data scientists, and developers be more productive, this is referred to as AutoML.
Threat Detection and Classification
In order to identify and respond to threats, machine learning techniques are employed in applications. This may be accomplished by analysing large data sets of security events and finding harmful behaviour patterns. When comparable occurrences are recognised, ML works to autonomously deal with them using the trained ML model.
For example, utilising Indicators of Compromise, a database to feed a machine learning model may be constructed (IOCs). These can aid in real-time monitoring, identification, and response to threats. Malware activity may be classified using ML classification algorithms and IOC data sets.
A study by Darktrace, a Machine Learning based Enterprise Immune Solution, alleges to have stopped assaults during the WannaCry ransomware outbreak as an example of such an application.
Phishing
Traditional phishing detection algorithms aren’t fast enough or accurate enough to identify and distinguish between innocent and malicious URLs. Predictive URL categorization methods based on the latest machine learning algorithms can detect trends that signal fraudulent emails. To accomplish so, the models are trained on characteristics such as email headers, body data, punctuation patterns, and more in order to categorise and distinguish the harmful from the benign.
WebShell
WebShell is a malicious block of software that is put into a website and allows users to make changes to the server’s web root folder. As a result, attackers have access to the database. As a result, the bad actor is able to acquire personal details. A regular shopping cart behaviour may be recognised using machine learning, and the system can be programmed to distinguish between normal and malicious behaviour.
User Behaviour Analytics (UBA), a supplemental layer to normal security measures that provides comprehensive visibility, detects account breaches, and mitigates and detects malicious or aberrant insider behaviour, is the same way. Patterns of user behaviour are classified using machine learning algorithms in order to determine what constitutes natural behaviour and to detect aberrant activity. If a device on the network performs an unexpected action, such as a worker login late in the evening, unreliable remote access, or an abnormally large number of downloads, the action and user are assigned a risk rating based on their behaviour, patterns, and time.
Network Risk Scoring
Quantitative methods for assigning risk rankings to network segments aid organisations in prioritising resources. ML may be used to examine prior cyber-attack datasets and discover which network regions were more frequently targeted in certain assaults. With regard to a specific network region, this score can assist assess the chance and effect of an attack. As a result, organisations are less likely to be targets of future assaults.
When doing company profiling, you must determine which areas, if compromised, can ruin your company. It might be a CRM system, accounting software, or a sales system. It’s all about determining which areas of your business are the most vulnerable. If, for example, HR suffers a setback, your firm may have a low-risk rating. However, if your oil trading system goes down, your entire industry may go down with it. Every business has its own approach to security. And once you grasp the intricacies of a company, you’ll know what to safeguard. And if a hack occurs, you’ll know what to prioritise.
Human Interaction
Computers, as we all know, are excellent at solving complex problems and automating things that people might accomplish, but which PCs excel at. Although AI is primarily concerned with computers, people are required to make educated judgments and receive orders. As a result, we may conclude that people cannot be replaced by machines. Machine learning algorithms are excellent at interpreting spoken language and recognising faces, but they still require people in the end.
Conclusion
Machine learning is a powerful technology. However, it is not a magic bullet. It’s crucial to remember that, while technology is improving and AI and machine learning are progressing at a rapid pace, technology is only as powerful as the brains of the analysts who manage and use it.
Malicious people will always improve their skills and technologies in order to identify and exploit flaws. To be able to identify and respond to cyber threats correctly and quickly, it is critical to combine the best technology and procedures with industry expertise.
Source: https://www.analyticsinsight.net/how-machine-learning-improves-cybersecurity/
