Machine learning is a type of artificial intelligence that allows computers to learn to look for patterns in data without being explicitly programmed. But, at present, machine learning in cybersecurity is one of the most promising applications in cybersecurity.
How does machine learning work in the field of cybersecurity?
Machine learning in the network realm allows network systems and cybersecurity to do amazing things. Thus, it is possible to pinpoint and detect anomalies in traffic patterns, connections, user activity, and many other aspects of the network.
In this way, powerful machine learning algorithms can filter traffic patterns, learn the digital footprint of network activity, and then make decisions based on machine learning algorithms. Accordingly, it is necessary to focus on intrusion detection and prevention systems.
Why use machine learning in cybersecurity?
Two terms are frequently used when talking about cybersecurity: intrusion detection systems (IDS) and intrusion prevention systems (IPS).
1. Intrusion Detection
IDS is the detection of an attack that has occurred. IPS is the prevention of any attack. It is easier to detect an attack than to prevent it altogether.
In this way, machine learning can increase the reliability of cybersecurity methods. IDSs can be classified into two main categories based on operational logic:
HEURISTICS-BASED IDS
Anomaly-based IDS checks traffic behavior, and whenever there is an anomaly in the usual behavior, an alarm is generated. It has excellent flexibility and uses high-level machine learning frameworks.
RULES-BASED IDS
Rule-based IDS works with specific definitions of known vulnerabilities that are considered attacks. Its operation logic is based on the fundamental classification problem.
Rulesets determine whether the software has established good, benign behavior. The main drawback of this method is the definition of its rule sets. But anomaly-based detection systems will work consistently as long as the rule sets are well defined beforehand.
Much work is being done to improve intrusion detection strategies. In contrast, research on the data used to train and test the detection model is equally important because better data quality can improve offline intrusion detection.
Both techniques have advantages and disadvantages; some hybrid approaches are developed by combining the benefits efficiently and eliminating the penalties. One part of the detection mechanism works with the supervised algorithm, and another works with the unsupervised algorithm. In recent years, most research has focused on hybrid detection approaches.
2. To analyze the threats against mobile POS
Machine learning has improved detection algorithms to a great extent. However, intelligent hackers are developing attacks that could outsmart them by exploiting loopholes.
Intensive research is being done to eliminate these loopholes and create better algorithms to prevent this. In this sense, Google is beginning to use this methodology to avoid attacks against POS (Point of Sale Terminal).
3. To end zero-day threats
Zero-day attacks demand more and more attention among the various cybersecurity concerns that modern businesses have to deal with.
An attack that exploits a vulnerability in a program or application is called a zero-day attack. It is so named because the developers and responsible cybersecurity team don’t have time to defend their systems and must work in firefighting to quickly reclaim control.
This is where behaviour-based detection systems come into the picture. Instead of focusing solely on a threat database, these systems evaluate programs and anticipate whether their actions are genuinely intentional or linked to a deliberate change in function.
