Nowadays, every single human activity is connected to a digital system. But these digital systems always rely on the programming language. Based upon the previous activities, the system can analyze and learn the data to be processed without any human interaction, this concept leads to a technology called machine learning.
In the case of Cyber Security, Machine Learning technology helps to analyze previous Cyber Attacks and develops the protective response accordingly. The Cyber Security is one of the latest sectors with the huge investment in machine learning based on the response to increasing cyber threat.
In the past, Cyber Security was not very familiar with Machine Learning. Network administrators were struggling with finding and tracking attacks. It looks a lot of time for them to even detect an attack.
Now, there are many security software that has been developed to support the human in order to maintain the System effectively.
As the technology grew, more and more data got produced and processed every day. Finally, “BIG DATA” was born and people began to use more intelligent systems for identifying those data. For that purpose, lots of algorithms are being developed until today.
Machine Learning algorithms may help us to fight with Cyber Attacks. By using machine learning algorithms, Google avoids unwanted communication with 99% accuracy. Apple is also taking responsibility to protect its users’ personal data and privacy. It has become an important component in online retail, Fraud Detection, and others. Many research areas like image processing, speech recognition, biomedical and cyber security domain are utilizing those such Machine Learning algorithms.
Do you think machine learning can benefit cybersecurity?
Yes, Machine learning has an attractive feature called “Predictive Analysis” which can predict the future output of any system. Based on data collection, the machine can predict how, where, and when an attacker can place a malware and alert by using a warning system to be cautious enough for any unwanted exploits to happen. Even if an attacker hacks the system, machine-learning will collect data from the scenario and train systems to defend itself from similar malicious activity in future.
At present, a more dominant part of machine learning approaches in digital security which is utilized as a “warning system”, where humans are required for decision making. Because of this human interpretation, the machine language accuracy is reduced. A research has been initiated for increasing the accuracy of machine language without human intervention.
I would like to give an example of how machine learning can be applied to cybersecurity.
Let’s consider the case of an analyst responsible for an incident response monitoring. For example, if an attack occurs on various machines in a network, the analyst is charged with multiple tasks to detect the malware and prevent similar attacks in future. The analyst will have a difficult time to solve all issues in a short period of time without the help of machine learning.
Some of the successful Machine-learning applications in Cyber Security:
• Spam Mitigation
• Malware detection
• Mitigating the Denial of Service Attacks
• Reputation in Cyber Space
• User Identification
• Detecting Identity Theft
• Information Leakage Detection and Prevention
• Social Network Security
• Detecting Advanced Persisted Threats
• Detecting Hidden Channels
In the next few years, these will be effective in the cybersecurity landscape. Various research activities are in-process to reduce the damage on cyber attacks while implementing machine-learning.
Few research areas are as follows:
• insider threat
• malware analysis
• network analytics
• secure coding
• situational awareness
Machine learning technology aim is not to replace the analyst, it is to provide a platform to secure all digital items essentially.
"The way human beings respond to different types of attacks and how they investigate them is something machines can study.“
Authored By - Hema P
TCS Cyber Security Practice