Is Blending Machine Learning with Human Intelligence the Ultimate Solution for Cyber Security?
At present, Netflix has 75 million subscribers in over 190 countries. With such a large user base, it’s practically infeasible to manually handle all this data. And, that’s where machine learning and artificial intelligence comes into the picture. Netflix tracks our past searches, the movies we have watched, language, country, most watched genre, etc., and based on this data, it lays out customized recommendations for each user.
Netflix is not the only one. Many big companies are investing in machine learning as it has a potential for positive ROI. With the current scenario of big data growing exponentially, handling all this information has become almost impossible for humans on their own. To streamline their business processes or for understanding their customer behavior, companies are employing machine learning.
However, when it comes to cybersecurity professionals still rely on the efficiency of the human brain. Many researchers believe that there is a huge risk of triggering false positives if we completely depend on the machine learning solutions. But, the volumes of data generated each day in the corporate is beyond the capacity of human expertise. So, maybe, the solution lies in the amalgamation of machine learning solutions and complexity of human brain.
What is a Machine-learning?
Machine learning is a type of Artificial Intelligence that allows the computer to learn and grow when presented with new data without being explicitly programmed. A few months back, Facebook asked his users to prioritize what they want to see on their timeline first. Through this priority list, recently liked posts and various other parameters, Facebook now present a tailor-made news feed for its members. And this is facilitated by machine-learning. When this collected data were fed to the respective algorithm, it analyzed the data, combined it with the user’s behavioral pattern and sorted the news feed to comply with the user preferences.
Can machine-learning help augment the level of cyber security?
If we take a look at the recent cyber breaches, we will find that no organization, whether big or small, is safe from these attacks. Cyber criminals are continually coming up with new ways to extort money from corporate organizations, proving the current security system to be inefficient. In the present scenario, companies take months to detect a cyber crime and by the time they find an intrusion, a significant amount of loss has already occurred.
To protect them from such attacks by cyber criminals, companies are investing in various tools and software that further results in the generation of more data. This vast amount of data, increasing number of cyber attacks and shortage of cyber analysts is bringing out the need to reevaluate IT management strategies.
An unsupervised machine learning solution can lead to false positives and alerts leading to decreased sensibility and trust for such methods. And, with the ever increasing amount of data, it is not realistic to manually code and configure all this data to prevent cyber attacks. But, if we combine machine learning and human expertise, we might be able to counter such attacks. The hybrid complements the weakness of each system and gives a more effective and efficient solution against cyber crimes.
How will this hybrid change the scenario?
As per a recent survey, the security workforce is in high demand and nearly all IT professional believed that there are skill gaps in their organizations. The low scalability of the human brain can be supplemented by the rapid problem-solving of the machine learning solutions. And the incapability of the machines to deal with the complexities can be complemented by the intelligence of the human brain. Together, this hybrid will eliminate the skills gap and reduce the need for hiring additional analysts. By automating the process of recognizing valuable patterns in data security, organizations will be able to make better business and hiring decisions.
With the enormous data being generated and transferred every day, it has become difficult for cyber security experts monitor everything and therefore any potential threat can go unnoticed. But with machine-learning, it is possible to analyze data from tens of millions of log lines every day and segregate anything suspicious. From these suspicious units, expert analysts can analyze, eliminate, identify and fix genuine potential threats. While it takes months to even identify a breach today, with the help of machine-learning it will be possible to detect them early and avoid any kind of losses.
In most incidents of security breaches, a malicious malware infects the system by encrypting certain file types and forces the users to pay a ransom in exchange for the access to these files. Manual processes are unable to detect security anomalies when it comes to large corporate data. To overcome this, machine learning-based behavioral techniques can be applied by the organizations to track any strange behavior on the company network. With the help of the machine learning algorithms, it is possible to spot any kind of crypto-ransomware before the infiltration of the IT infrastructure that allows the cyber analysts to react quicker.
Machine-learning shows the potential of making the security system of an organization proactive instead of retroactive.
Breeze Telecom creates boutique data virtualization solutions for enterprises of all scales, across different industry verticals. Using its industry collaboration with more than 200 IT service providers across North America, Breeze Telecom takes care of the most basic requirements like high-speed network connectivity or the later, emerging challenges like IoT-related disruptions.