Posted in IoT on 27 Dec, 2018
Cybersecurity is presently a hot topic of discussion which is drawing the attention of a majority of the companies regarding data protection. Due to the worldwide availability of Internet services, vulnerabilities are exploited in more sophisticated ways by increasing array of malignant actors in a large number of verticals, such as insurance, healthcare, finance, high-frequency trading, Machine Learning and AI in cybersecurity.
This leads to storing an enormous amount of sensitive data which creates new liability and challenges for security analysts. As per market experts, Artificial Intelligence and Machine Learning algorithms have been considered as a prominent tool to protect users from modern cyber attack.
The purpose of this article is to make people aware of the pitfalls and challenges which development teams usually face during implementation of AI and ML in their security solution. The article also discusses various other ways of improving cybersecurity.
We have defined a certain paradigm which helps us to decide where we are now and what we can achieve by getting our answers to these five vital questions:
Will AI and ML Really Improve Cybersecurity?
IoT enables the system access through the Internet which increases data storage and adds more threat to each individual using real-world interaction devices like webcams and autonomous vehicles. This creates a massive amount of data which is tedious to monitor.
However, a traditional security system generates too many undifferentiated alerts which makes it difficult to be interpreted by an individual and becomes almost impossible to monitor. To eradicate the old signal monitoring procedure developers are developing a new approach to cyber attack detection and response. It will help to process high volume data and detect suspicious activity quickly.
How Effective is AI and ML in Improving Cybersecurity?
As we are equipped with sophisticated advanced tools for cybersecurity protection, but still we breach data that may arise a risk of detection, investigation, and remediation of damage by security managers.
AI is a science of making machines replicate human intelligence. It involves various technologies, some exist and some of them are under development phase. Examples are Apple’s Siri is intelligent personal assistants, game-playing programs like AlphaGo, IBM Watson is designed for solving business analytics solutions.
Machine learning is a constituent of AI that uses mathematical algorithms to predict the patterns and tries to learn from those patterns. With the help of Machine learning, we try to interpret the anomalous behavior of users and systems to determine the possible threats which may occur from different sources. Supervised and unsupervised learning helps to improve cybersecurity developed under machine learning algorithms.
What are the Various Methods to meet the Challenges?
Security Information and Event Management (SIEM) applications are getting improved to implement machine learning. SIEM solutions comprise of event and log management, behavioral analysis, real-time monitoring of databases and applications. If SIEM applications find any suspicious activity, it pushes an alert and blocks the further access.
According to Gartner’s Definition, Advanced Analytics (AA) is basically a tool that has been designed in order to solve the wide range of data with the help of AI techniques. AA also tries to find deeper correlations, provide recommendations and make predictions as well.
Deep learning algorithms is another technique which processes large volumes of data using neural networks that stimulates the activity of the human brain. Implementation of AI and ML is only possible by Big Data integration in cybersecurity.
All security tasks have been split into five sub-categories: prediction, prevention, detection, response, and monitoring. The major implication of this technology is to find in the endpoints of anti-malware, application of WAF or database firewalls or user point anti-fraud analysis.
Here are a few processes which will implement the Machine learning in the cybersecurity tasks.
Regression, in other words, prediction, helps to utilize the current knowledge of the data in order to find the opinion of the new data. In terms of cybersecurity, application of ML helps to determine the user’s behavior as well as fraud detection.
Classification is also referred to as supervised learning in which, we are aware of what we are exactly looking for and classify our requirement into groups in the subconscious mind. Its major application is in network layer for intrusion detection system (IDS) and identifies different classes of network attacks such as scanning, spoofing, etc.
Clustering is similar to classification with a slight difference in it. In such analysis, we are not aware of the classes of our data or whether this data can be classified. It is employed basically in cybersecurity tasks, where we can apply classification.
What are the Other Applications that will help in Improving the Vulnerabilities?
Gartner aspects further integration of ML and AL in cybersecurity solutions within the next five years. The cybersecurity industry is booming these days with the evolution of new startups. With the increasing use of IOT, business is investing into modern threat cybersecurity that is an intelligent approach to cater more business in the cybersecurity world like Darktrace, CrowdStrike, Hexadite, Cylance and Amazon Macie, which is former Harvest AI.
Which Factors need to be Considered while Developing AI-based Security System Solution?
For a new startup in AI, there are certain challenges which need to be faced while developing UEBA solution:
To solve cybersecurity problems with AI and ML, we require the best computing resources. However, it’s not easy to employ the full potential of AI and ML in deploying such advanced technology. Only highly skilled and experienced professionals who can handle complicated data processing and software development issues would be competent for the work.
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