Que :-What is Machine learning ?

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data.

A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.

Que:- What is AI and ML?

Artificial intelligence (AI) and machine learning (ML) are being heralded as a way to solve a wide range of problems in different industries and applications, such as reducing street traffic, improving online shopping, making life easier with voice-activated digital assistants, and more.

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The cybersecurity industry is no different. However, we need to be careful of the “hype” around AI and ML. And there is a lot of hype out there! A simple Google search of the term “artificial intelligence” yields about 630 million results, and AI continues to dominate the headlines and has even made its way into mainstream TV advertising. However, the cybersecurity industry needs to set the record straight — contrary to popular belief, AI and ML will not solve all of our problems.

Artificial Intelligence in Cyber Security: How is That Helpful?

Adopting artificial intelligence in cyber security offers better solutions when it comes to analysing massive quantities of data, speeding up response times, and increasing efficiency of under-resourced security operations. Cyber-attacks can be predicted by tracking cyber threats through cyber security analytics which uses Big Data to create predictive analyses of how and when cyber-attacks will occur. The network activity can be analysed while also comparing data samples using predictive analytics algorithms. Essentially, artificial intelligence can help to enhance the capabilities of cyber security through several aspects, including machine learning, risk identification, access securing and fast responses.

Here are some various ways:-

1. Updating Databases and Identifying Large Scale Movements

AI can be used to update security databases. By analyzing logs from various sources, artificial intelligence can detect when new threats are imminent.

In other words, AI can collect comprehensive data from different logs and records and “connect the dots” to identify new threats that are being spread by hackers.

AI can also identify malware and spyware trends by analyzing data across multiple channels.

By using AI, new malware systems can be detected much quicker and before they can do damage on a large scale. There will be more time to come up with prevention methods to fix any bugs or security flaws that may be exploited by the malware or virus.

2. Identify Unusual Activity

Besides detecting large scale malware movements, AI can also be used on an individual level to scan a system for abnormal activity. By constantly scanning, enough data can be collected to determine when a particular activity is abnormal.

Users can be constantly monitored to detect when unauthorized access occurs. If abnormal activity is detected, AI can use certain parameters to help determine whether or not it may indicate a threat or whether it is a false alarm.

Machine learning can be used to help AI determine what “normal” activity is and what should be considered “abnormal.” As machine learning becomes more advanced, AI will become better at detecting slight abnormalities which may indicate something wrong going on.

As above, “connecting the dots” is the key here. Certain slight abnormalities may not seem significant on their own, but together they can paint a bigger picture of what may be causing them.

AI can constantly scan the system, analyze different activities, compare them with each other, and create warning alerts.

3. Detection

This is slightly different from how AI detects abnormal activity. Here, the focus is AI pinpointing potential weaknesses, bugs, and security flaws. For example, machine learning can be used to detect when untrusted data has been sent from an application.

Injection SQL vulnerabilities are one of the most commonly exploited weaknesses by malware and viruses to steal data and enter systems. Another weakness AI can help detect is a buffer overflow, or when an application puts more data than usual in a buffer. Yet another area where AI can help is human error. Employee mistakes are some of the major causes of data breaches, and AI can detect them in time to prevent damage.

On a larger scale, AI can stay up-to-date on current malware threats (as mentioned) and scan the current system to see how it would be vulnerable to any potential threats.

4. Prevention

As AI gets more advanced, it cannot only detect when a certain system or update has a flaw, but automatically prevent those flaws from being exploited.

Whether it’s adding additional firewalls or fixing coding errors causing vulnerabilities, this can be a great way to prevent problems from occurring.

5. Response

This is similar to prevention, but happens at a later stage — when malware has already entered the system. As mentioned, AI can be used to detect abnormal behaviors and connect the dots to create a profile of malware or viruses in the system.

The next step is an appropriate response to the malware or virus. This includes damage control, removing the virus from the system, patching any security flaws, and making sure additional protections are put in place to prevent the virus from infecting the system again.

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In all, AI/ML, when used properly can play an important role in assisting security teams by informing them, and thereby helping them to make more timely and accurate decisions about security threats and incidents. But AI/ML cannot do the job for you, there is no magic solution inside, and this is an important difference that the broader industry needs to understand. Causation — understanding why something happened — is something that AI/ML cannot do and is a key difference between humans and AI/ML systems.

By understanding the real value that AI and ML play in the cybersecurity process, versus the value that humans bring, our industry can reduce a key misconception, and, in the long run, make our overall cybersecurity strategies more effective.