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Arya College Cousellor Arun Arya

Prof. (Dr.) Arun Arya

Contact for Admission

Arun College Cousellor Arya

Prof. (Dr.) Arun Arya

How Machine Learning is used in Cyber Security?

Uses of Machine Learning in Cyber Security

Machine learning has a wide range of applications across many industries and cybersecurity is most popular. In fact, the market for artificial intelligence (AI) in cybersecurity is expected to grow in the coming years. Today, it is impossible to deploy effective cybersecurity technology without relying heavily on machine learning. Similarly, you cannot deploy machine learning without a comprehensive, rich and complete approach to the underlying data.

Why has machine learning become so complex to cybersecurity?

There are several reasons to this. With machine learning, cybersecurity systems can analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity professionals of Top Computer Science Engineering  Colleges to be more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically.

Machine learning can make cybersecurity simpler, more proactive, less expensive and far more effective. In the corporate security or cyber investigations space is well aware that adversaries are becoming rapidly more advanced, and cyber-attacks becoming more widespread across every sector. Cybersecurity teams of Best Engineering Colleges are using machine learning to detect and mitigate digital risks more effectively in this quickly evolving landscape.

What Is Machine Learning?

The term machine learning is considered a type of AI, which in and of itself is quite difficult to define. It is easier to define machine learning techniques. One type of machine learning, deep learning, is effective at solving a variety of problems. The term “deep learning” is often synonymous with “neural networks” because it learns using a similar mechanism to biological neural networks.

In the context of classifying text, the goal of machine learning is to train a machine to recognize that a particular combination of words relates to a particular text class or category whether the content of a dark web post indicates a data breach. Some machine learning algorithms go a step further. These deep learning models map sentences to a vector space, training the computer to discern word order, identify concepts, and further contextualize classified data in multiple languages.

For end-users, this means the ability to discover content by concept or meaning with a great deal of accuracy, rather than searching by keyword and manually reviewing results for relevancy.

Why Cybersecurity Teams Need Machine Learning

It is now clear to the students of engineering colleges Jaipur how the power of machine learning can be harnessed in cybersecurity particularly in the threat intelligence process. Cybersecurity professionals rely on data from the surface, deep, and dark web to detect threats and gather relevant investigative information.

An impossibly large amount of data to manually search and assess thoroughly and efficiently. Also, almost 51% of organizations struggle to hire cybersecurity analysts and investigators from best private engineering colleges in Jaipur due to an industry skills shortage, further fueling the need for automation.

Machine learning helps automate the process of finding, contextualizing, and triaging relevant data at any stage in the threat intelligence lifecycle. In other words, it includes anything from finding dark web forum posts indicating a data breach, to detecting suspicious network activity in real time.

With machine learning capabilities, organizations can evaluate a larger scale of data to ensure they aren’t missing out on critical information while reducing false positives. Staff can also delegate more energy to tasks that actually require human intelligence rather than using resources to manually curate data. After all, the goal is to improve a cybersecurity team’s capabilities not to replace them with AI.

How Is Machine Learning Used in Cybersecurity?

There are many stages in the threat intelligence process that can be improved by machine learning. In the real world, there is no “one-size-fits-all” digital risk protection tool, and most companies utilize 2-3 solutions to fulfill their threat intelligence requirements.

Machine Learning in the Echosec Systems Platform

The Echosec Systems Platform aggregates and filters threat data across a variety of social media networks and deep and dark websites. Echosec Systems’ machine learning models are trained to detect breaches and data disclosure (among other classes) based on data within these networks. This means that security personnel of top engineering college in Jaipur can search for the concept of a breach relevant to their organization rather than running breach-related keyword searches.

The Platform automatically retrieves relevant data so that analysts do not have to wade through results to find actionable breach indicators. There are many possibilities for gathering threat intelligence using machine learning, even beyond cybersecurity use cases. For instance, machine learning models can also be trained to gather business risk intelligence, including the following:

  • Other cyber security threats, such as brute force attempts, phishing emails, and malware detection
  • Hate speech, identity hate, and toxicity
  • Radicalization, active shooter manifestos, and other physical threat indicators
  • Public sentiment towards a brand or entity
  • Specific chatter or content related to a crisis, such as COVID-19

On average, it takes organizations almost 30 weeks to detect a data breach after the initial compromise. This significant delay is due in part to holes and inefficiencies in gathering actionable threat intelligence from available online sources. It is a danger that can be addressed by using tools that leverage machine learning.

Conclusion

Between advancements in AI, the growing volume of available threat data online, and increased cybersecurity risks, there has never been a better time for corporate security teams to integrate machine learning-based approaches into their risk management strategy.

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