Does Machine Learning Help Hunt Down Cybercriminals?

Cybersecurity is a critical area where machine learning is becoming increasingly significant. It has grown to become a vital technology for cybersecurity. It stamps out cyber threats and bolsters security infrastructure through penetration testing, pattern detection, and real-time cybercrime mapping. The need for machine learning in cybersecurity is felt more today as the whole world is relying on data. Machine learning seems to be the only solution in an era of extremely large amounts of data and cybersecurity talent shortage. Now, answering the most important question: ‘Does machine learning help hunt down cyber criminals? Yes, machine learning plays a major role in cybersecurity and also helps hunt down cyber criminals.

Let us now understand what machine learning means and how it helps in cybersecurity.

What is machine learning?

Machine learning refers to machines being able to learn by themselves without being explicitly programmed. Machine learning experts apply AI to enable systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.

Industries That Use Machine Learning

Let us now throw light on some of the notable industries that use machine learning to track cybercriminals.

1. In the retail industry, machine learning and deep learning is used by merchants for protecting the security and privacy of the personal information and credit cards of a shopper. Merchants utilize machine learning to gain insights into the buying habits and behaviors of customers. This will aid the merchants in offering their customers the right products at the right time.
2. In the finance sector, businesses use machine learning techniques for detecting signs of fraud and cybercrime. Machine learning aids financial services companies to flag suspicious behavior as and when it occurs and helps build threat-detection systems that learn from their experiences and improve over time. It is also used to gather valuable insights into customers’ needs and interests.
3. In the insurance industry, machine learning experts use AI and ML solutions to monitor claims for fraud. Machine learning is used to analyze datasets for finding hidden patterns, trends, and similarities.

Machine learning to Keep Cybercriminals at Bay

Let us now look at some of the techniques that will keep cybercriminals at bay.

1.Threat Detection and Classification

Machine learning algorithms are implemented in applications and networks before they take effect. This is done by using a model that is developed by identifying the pattern of malicious activities and analyzing big data sets of security events. The training dataset of the model is made up of previously identified and recorded Indicators of Compromise (IOC) that are used to build models and systems to identify, monitor, and respond to threats in real-time.

2. Network Risk Scoring

This refers to using quantitative measures to assign risk scores to several sections of the network, thus aiding organizations to prioritize their cybersecurity resources with regard to various risk scores. Machine learning is used to automate this process by determining the areas of networks that were mostly involved in these types of attacks and analyzing historic cyber-attack datasets. By using machine learning, the resulting scores will be data-driven and not only based on the domain knowledge of the networks. This score helps quantify the likelihood and impact of an attack, thus helping organizations reduce the risk of being victimized to attacks.

3. Optimizing Human Analysis and Automating Routine Security Tasks

Machine learning is also used to automate repetitive tasks that are carried out by security analysts or machine learning experts while conducting security activities. This is done by analyzing the reports or records of past actions taken by security analysts to identify and respond to certain attacks, and this knowledge can be used to build similar models that can identify similar attacks and respond appropriately without human intervention. Though it is difficult to replace a human security analyst and automate the entire security process, machine learning can help automate some aspects of the analysis, such as network risk analysis, network log analysis, malware detection, and vulnerability assessments.

Conclusion

Today, companies and cities all over the world are using artificial intelligence and machine learning to prevent and reduce crime and to respond more quickly to crimes in progress. Using AI and machine learning to detect crimes is currently being used widely, and the applications are expected to expand in the future. Improvements in crime prevention technology in the future will spur the increased total spending on these technologies. Technologies such as AI and ML that are used to predict crimes and ensure network security will help companies to prevent loss and to help decide where to establish new locations.

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