Whether you admit it or not, the rise of data science, machine learning, and artificial intelligence is acting as a boon in the 21st century. Humans nowadays are defeated by artificial intelligence. If you look around, you will find many related examples to this. In the adoption of machine learning, organizations need to maintain their human understanding and capacity to oversee and manage the trending technologies. Machine learning is not a panacea for cybersecurity but it allows the introduction of intelligence to the first level of defense against cyber threats for an organization. Various subjects of cybersecurity have been made more powerful with machine learning. These may include spam filters, IDS/IPS systems, false alarm rate reduction, fraud detection, cybersecurity rating, incident forecasting, and secure user authentication systems.
The best badass hackers and security professionals utilize machine learning to break and secure systems. Through this blog, let us discuss the fundamental concepts of using machine learning in cybersecurity and real-time examples of its utilization.
Machine learning in the domain of cybersecurity
Machine learning in the cybersecurity domain is recognizing cyber-attacks to support humans in order to manage and protect their systems effectively. There was a time when software has been developed to manage various functions like mathematical calculations that are tough to handle for human beings. And then the demand for humans increased more. After this, the next step was to elongate the capability of software by implementing artificial intelligence and machine learning techniques. With the advancement of technology, the amount of data to be produced was getting bigger and bigger every minute, every hour, and every day. This led to the rise of “big data” and due to this systems became more intelligent for processing and getting a smarter sense of data. Now, as per the development of technology, many algorithms were developed (and still developing). These algorithms are now used for research areas, image processing, speech recognition, biomedical area, and in the domain of cybersecurity as well.
The purpose of machine learning in cybersecurity is to provide a mechanism to software as normal people do. The domain of cybersecurity is an important research stream to work upon. Taking a glance at the stats of previous years, the Centre for Strategic and International Studies in 2014 estimated annual costs to the global economy caused by cybercrimes were between $375 billion and $575 billion. Other resources may differ; the average cost of a data breach incident to large companies is over $3 million. Researchers have developed some intelligent systems for the cybersecurity domain with the purpose of reducing this cost.
Big data and Machine learning for cybersecurity
- Volume
- Variety
- Velocity
Albeit big data doesn’t equate to a particular volume of data, the term is regularly used to portray terabytes, petabytes, and even exabytes of data caught after some time. With the initiation of far-reaching utilization of IoT technology, the data to be processed will become significantly bigger in the future. Big data and machine learning are the two components that complement each other. If we need to break down Big Data, we need to utilize Machine Learning strategies, then we need to make an intelligent framework utilizing AI we need to utilize a huge amount of data. Deep learning is one of the most drifting themes in machine learning. Since this strategy permits to increase of high accuracy for intelligent frameworks with the intensity of big data.
Role of Machine Learning in Cyber Security
Machine learning in cybersecurity will boost spending in big data, artificial intelligence (AI), and analytics to $96 billion by 2021, while some of the world’s technology giants are already taking a stand to better protect their own customers.
- Utilizing machine learning to recognize malignant action and stop assaults :-
Machine learning algorithms will assist organizations with detecting malignant movement quicker and stop assaults before they begin. David Palmer says that Dark trace recently helped one casino in North America when its algorithms detected a data exfiltration attack that used a connected fish tank as the entryway into the network. - Utilizing machine learning to dissect mobile endpoints:-
Machine learning is now going standard on cell phones, yet hitherto the greater part of this movement has been for driving improved voice-put together encounters with respect to any experiences of Google Now, Apple’s Siri, and Amazon’s Alexa. However, there is an application for security as well. Google is also utilizing machine learning to dissect threats against mobile endpoints, while the organization is seeing a chance to ensure the developing number of bring-your-own and pick-your-own cell phones. Each organization uses its own machine learning algorithm to detect potential threats.
Sairam Uppugundla is the CEO and founder of Codegnan IT Solutions. With a strong background in Computer Science and over 10 years of experience, he is committed to bridging the gap between academia and industry.
Sairam Uppugundla’s expertise spans Python, Software Development, Data Analysis, AWS, Big Data, Machine Learning, Natural Language Processing (NLP) and more.
He previously worked as a Board Of Studies Member at PB Siddhartha College of Arts and Science. With expertise in data science, he was involved in designing the Curriculum for the BSc data Science Branch. Also, he worked as a Data Science consultant for Andhra Pradesh State Skill Development Corporation (APSSDC).
