Role Of Machine Learning In Cyber Security

Machine learning is so critical to cybersecurity because, it can make cyber security simpler, more effective, less expensive, and proactive. It is a sub-field that comes under Artificial Intelligence (AI) Machine learning means, computers changing the way they do a task by learning from new data automatically without the help of humans. With the help of machine learning, cybersecurity systems can analyze patterns and helps to prevent cyber-attacks. It is about developing patterns and manipulating those patterns with algorithms. Machine learning is based on some patterns that are capable of making new predictions according to the new data, like a shopping application that provides you with many recommendations based on your previous views.  To develop a pattern, it should have rich data because the data should represent possible outcomes from the possible structure. It is always misunderstood as a robot that can destruct human skills, but that is not the truth. Most people have a lot of misconceptions about Machine Learning. But the thing is, it is not a new concept, we are using it daily without our knowledge. Some examples of machine learning include banking apps, social media applications like Facebook, and Instagram, using portrait mode on smartphones, online games, etc.

Benefits of Machine Learning

Machine learning can be used in various domains within cybersecurity to increase the security process and make it easier for a security analyst to identify, prioritize, and deal with new attacks.

  • Automating task: One of the uses of ML is to automate time-consuming and repetitive tasks like threat analysis, network log analysis, triaging intelligence, and vulnerability assessment. With the help of ML, an organization can easily identify the threat at a rate that is impossible by a human. It helps a company to accomplish a task faster without manpower, thus reducing the costs in the process. This automating process is known as AutoML. It signifies the tasks are automated for analysts, data scientists, and developers.
  • Malware recognition and categorizing: Machine learning is used to identify and detect threats. The threat can be detected only by analyzing big data of security events and the patterns of malicious activities. When a similar event or a pattern is detected, they are automatically dealt with by the trained machine learning models. ML model can be created using Indicators of Compromise (IOCs), which helps in identifying, monitoring, and detecting threats in a really fast time. ML algorithms are used in IOC data to classify malware.
  • Phishing: Phishing Detection Technique lacks speed and accuracy in identifying and classifying harmful threats and dangerous malware. The latest ML algorithm has the power to identify patterns, classify, and reveal a harmful email. To do this they are trained on features like email headers, body data, punctuation patterns, and the ability to classify between harmful and harmless threats.
  • Continuous Development: Machine Learning algorithms keep on improving their efficiency and accuracy as they gain experience. This helps them to make better decisions. So, as the number of data increases, the algorithms will learn to take accurate decisions quickly.

Machine learning is fascinating because it learns from the collected data and will analyze and find a solution to the problem automatically. One of the important requirements for creating an effective machine learning system is providing it with good input data. What plays a big role in the ML process is the quality of training data and output that we use to develop. For the question, how can an organization effectively use machine learning in cybersecurity? asked Giora Engel, he said, “It is about how you collect, organize, and structure the data,” He also said, “Part of work is stitching all of the data together, so you get one representation with the full picture.”

One of the biggest challenges in machine learning is getting data from the endpoint, network, and cloud, and normalizing these data into one state. So that these data can be used effectively. Using modern machine learning technology, it is very difficult to make sense if the data is irrelevant or to analyze if it is from multiple sources. So, the thing is all the data should be in the same language for the algorithm to read, analyze and apply machine learning capabilities effectively. When it comes to cybersecurity, there is so much to talk about machine learning and artificial intelligence, the potential of machine learning has to be dramatic and should have a long-lasting impact.

In 2018, there were about 10.5 million cyber-attacks. A big thanks to machine learning because ML was picking up during that time and it helps to reduce the number of cyber-attacks. Besides, early threat identification, machine learning is used to scan for automated responses and network threats. Nowadays, about one-third of the organization’s security is reliable on Artificial Intelligence and those unethical hackers are always trying new ways to break this huge security. The cybersecurity experts have put together all their efforts to find a new way to defeat the threats and cyber-attacks down. They put all their efforts and finding two important tools Artificial Intelligence and Machine Learning; both of these tools have created a huge revolution in the field of cyber security.

Machine learning is a boon to cybersecurity, it helps to reduce the amount of time spent on daily task and enable the organization to use their data more wisely. With the ability to identify malicious threats, machine learning is used to discover threats and destroy them before they create a problem. It is important to remember that, as technology is developing, advancements are taking place in Artificial Intelligence and Machine Learning are progressing at a greater speed, all these developments can be taken on a positive or negative note, but all the lies in the mind of a developer or an analyst. Nowadays many organizations are after learning algorithms, so that is why machine learning has become so important