Machine Learning as an Answer to Cyber Security Threats
Cybersecurity has always been a major concern for businesses and governments around the globe. Major attacks in forms of Twitter account related bitcoin scam and sony pictures hack practically shook the world. Due to the pressure of COVID19, dependence on the internet for daily bread is at its peak. Therefore, in 2020 the fear of cyber attacks are rising at a pace never seen before. Most of the existing cybersecurity infrastructure is suited for mostly in house security and 2020 has posed a challenge of securing remote work environments as well. Bring Your Own Device (BYOD) policy by many companies after the lockdowns have added to the vulnerability. “Work From Home” is humanity’s weapon of choice to fight the novel coronavirus of 2019. Hence, the need for more efficient defence against cyberattacks is at an all-time high.
How do the cybersecurity systems work?
The first line of defence against malicious software is the firewall. Firewall filters the data according to user criteria and prevents the introduction of anything irrelevant to the system, securing the network infrastructure downstream.
The leftover files such as malware and virus which corrupts the network infrastructure are dealt with by the antivirus.
And for disaster management, periodic backup of all data is taken.
But that is not quite enough
A cybercriminal can, however, evade the firewall, disrupt the anti-virus system and confiscate all the backup and working data rendering a network useless. In 2014, the sony pictures hack incident manifested into something similar. The hacker group demanded the removal of a movie and disabled Sony’s network completely. Additionally, they released employee details including their family, data of planned films for the future and many critically classified pieces of information.
Machine learning to the rescue
Machine learning can, however, be the game-changer. It can tip the balance of power in favour of both parties, depending on how it is being used.
For example, an ordinary firewall can not detect attacks which arrive in forms of patterns and periodical anomalies, apparently harmless irregularities in the data. But, if armed with advanced machine learning tools, the firewall can detect, learn and even remember the user action specific details of the event.
Similar statements can be made in case of antiviruses and malware detection systems as well. Machine learning will only increase the user’s efficiency. This is a niche that candidates can target after completing a machine learning online training.
What about financial institutions? how much they ethically can allow a cybercriminal within attacking range? What about work from home?
The solution to all these problems can be a zero-trust security system for the work network and biometric login procedures. Things, impossible to achieve without proper implementation of machine learning tools.
A case in point
A story regarding a hospital in the USA revealed an incident where people died due to a cyber attack! Ransomware was used to disable the network infrastructure of the hospital by hackers. It caused the deprivation of treatment for a lot of critical cases. As the pandemic rages on, people are reluctant to visit a doctor if not absolutely necessary. A financial blow for the already overstrained healthcare system. Whatever limited amount of infrastructure remains unengaged by the pandemic, is becoming expensive for the masses. In such a condition a threat from the cyber world is the last thing the healthcare system can effort. Introduction of machine learning tools can process the natural language and locate the criminals. Provided, it fails to detect the threat beforehand. Either way, it ends with justice.
The introduction of 5G has ensured an increased amount of threat as well as probabilities of fighting it better. In order to adapt to the new environment, assistance from machine learning tools and sometimes artificial intelligence can ease up the hurdles a lot.
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Sometimes a cyber-attack needs immediate mitigation. A phenomenon like a zero-day threat allows the user zero days to respond to an attack. Sometimes it starts to exploit and destroy as soon as the contact. In this case, prevention is always better than cure. With the help of machine learning-based systems, a threat like this can be detected beforehand and actions can be taken according to previous experiences. Even in case of a fresh encounter, a fully enabled machine learning-based security system can calculate and deduce remediation a lot sooner than its conventional counterparts.Going as far as detecting the criminals by natural language processing.
As the work culture is rapidly evolving and work from home has become the new norm, home workers remain the most vulnerable targets. Most of the home workers are using their own devices, plagued with a lot of other personal entanglements. These remote systems remain at risk of attacks as they are not properly covered by most security systems of the day. After the inefficiencies of VPNs are exposed, there is no other means of securing the systems at this point. Perhaps machine learning can provide a solution. Perhaps it can secure employee data and institution data present in the remote system! Without the presence of adequate examples, the confusion remains!