Machine learning in its simplest form can be defined as making your computer learn without being programmed explicitly. In other words, machine learning teaches computers to perform based on experience. It is a type of artificial intelligence (AI) that enables computers to learn without being programmed. It focuses on changing computer programs randomly when exposed to new data. However, data conversion is not a new technique, it is as old the origin of computer technology. So, what makes machine learning significant in recent years? The answer could be ’Big Data.’ It is now that we have so much data, and therefore, we need a more logical machine to interpret and read essential data for us.
Big data is being generated in large volume by everything around us. It is characterized by three V’s – Volume, Variety, and Velocity. The volume of big data is measured in terms of ‘bytes’ such as ‘petabytes’ or ‘exabytes.’ Every digital process, social media exchange, and the advent of IoT (Internet of Things) technology makes data growth more significant in the future.
How does machine learning work?
To understand how machine learning works in real life, let us consider a few examples:
Recommendation systems: They are well known in the literature and business sector to provide suggestions for items that can be of user’s likeness. Recommended systems are developed on software tools and techniques that track user’s browsing and activity in real-time and based on that, give suggestions. The technique is widely used in commercial businesses as it not only helps an individual make a choice but also markets the products and services on behalf of an enterprise.
Activity recognition: It is another area that is commonly used by YouTube and Facebook to detect activity performed by the user at a particular time. Businesses which are purely digital research on this topic heavily.
Machine learning in security
Machine learning learns by encrypting data and so it can analyze and prompt changing patterns in the applications and systems due to security intrusion. It helps businesses analyze threats and respond to security incidents. It can also automate security tasks generally carried out by semi-skilled or under-skilled professionals. Machine learning in security is a fast-growing trend. ABI Research has estimated the spending of cybersecurity in machine learning, artificial intelligence, and big data, to be around $96 billion by 2021. [1]
Even Google is using machine learning to analyze threats to Android mobile phones and to identify and remove malware from endpoints. Amazon has its own startup on Artificial Intelligence (AI) and has launched Macie, which is a service that is based on machine learning to identify and sort data on S3 cloud storage. However, enterprise security vendors are struggling to incorporate machine learning in the existing and upcoming products to provide extra security shield and mostly to improve malware detection.
The size of the cyber threat landscape for larger organizations appears vast, and continually tracks and correlates several internal and external data points. It is a challenge to monitor and manage the volume of information when it comes to cybersecurity. Machine learning can serve this purpose of streamlining the security standards by recognizing patterns and identifying threats in massive data sets, all at machine speed. By automating the security process partially, the cybersecurity team can detect and isolate systems for more in-depth human analysis.
| Do watch our prominent Cyber Talk series on Machine Learning in Security: https://www.eccu.edu/how-can-we-work-towards-security-in-machine-learning/ |
How machine learning helps security?
Detects malicious activity:
Machine learning helps identify threats by monitoring the behavior of the network or applications for abnormalities. It can also process a large amount of data to discover critical incidents and allows the detection of malware, insider threats, and suspicious activities.
Promotes healthy browsing:
Machine learning identifies “bad neighborhood” online and prevents from diverting to malicious websites. It analyses internet infrastructure to identify threat vectors that are staged for current and emerging attacks.
Endpoint malware protection:
It identifies malicious files and activity on endpoint devices and new malicious files or activity similar to that of the attributes of known malware.
Cloud data protection:
It can analyze a suspicious cloud app login activity, detect location-based anomalies, and identify threats in cloud apps by conducting IP reputation analysis.
Detecting malware in encrypted traffic:
Machine learning analyzes encrypted data in a shared network for malware. It can read through encrypted data for patterns that identify threats.
Machine learning is based on the algorithms designed to serve the purpose. Cybersecurity is a crucial task for any enterprise, and therefore, designing algorithms should be considered high priority. An expertise contribution would ensure that well-structured machine learning algorithms can enhance the security standards of the enterprise. A degree in cybersecurity can help you gain an overview of the entire cybersecurity landscape and develop the necessary talent to be a leader in the industry.
EC-Council University offers Bachelor and Master programs in cybersecurity. The Bachelor of Science in Cybersecurity (BSCS) gives required exposure, builds cybersecurity skills, and develops leadership abilities that help any candidate to grow as a cybersecurity professional. Master of Science in Cybersecurity (MSCS) provides you expertise in desired skills and helps you in gaining domain knowledge to stand ahead in the competition.
Source:
[1] https://www.mdsny.com/cyber-security-2/
