Can Predictive Analytics be a Cure for Cybersecurity Challenges?
Predictive analytics detects the vulnerable areas of the system and ensures cybersecurity across the system.
An unsolicited incident of data breach exposed the personal data of 2.8 lakh students in India recently after the AWS server of a renowned educational institute was rendered with multiple vulnerabilities leading to security infringement. Such incidents of a data breach and security infringement is not uncommon and has been listed amongst the major challenges that organizations are failing to tackle.
Though organizations employ advanced cybersecurity tools to tackle security concerns, only a handful deliver to their promise. It not only becomes perilous for a client’s privacy but drives organizations towards economical, financial and reputational loss. A report by IBM states that an average cost for data breach compounds at US$3.86 million, with USA being the most expensive country (US$8.64million), and healthcare (US$7.13 million) being the most expensive sector for data breaches. Another report by Verizon points that more than 50% of security infringements such as ransomware, malware, cyberattacks and data breach remains undiscovered for a very long time.
As the cyberattacks have become advanced and smart, organizational also need solutions that exceed traditional security capabilities. Preparation is the key to tackle challenges. The companies and enterprises cannot rely on basic artificial solutions but needs to be proactive for deploying tools that can predict such attacks. Experts view Predictive Analytics to deliver the outcome of what traditional model cannot.
Predictive analytics in Hacker bots
Predictive Analytics is an advanced analytics technique which utilizes data mining, statistics, modeling, machine learning and artificial intelligence to analyze the current data and make predictions for the unknown future events. As automated bots are one of the most common and powerful tools for cybercriminals to hack a system, predictive analytics can be leveraged in hacker bots to mitigate cyberattacks. To further explore this possibility, seven teams collaboratively deployed hacker bots at the Defense Advanced Protection Agency (DARPA) cyber grand challenge to address the cybersecurity threats through the concept of ‘bot-against-bot’. The bots were able to deliver better results than traditional security tools. The teams integrated predictive analytics to identify vulnerable areas of other bots and attacking them. As the results were positive, hacker bots enabled with predictive analytics can be used to mitigate cyberattacks.
Additionally, hacker bots use complex data analytics procedures through self-learning to identify the weak areas. They also use advanced techniques to monitor the activity across the network and alerting the user during an emergency. The available traditional security tools employ the complex method of digital fingerprinting by scanning through the plethora of data, to first identify the source to mitigate the attacks. This technique becomes time-consuming for an industry that demands urgent results. Hence, the hacker bots ensure time-effectiveness identification of the red zone with exception of such complex procedures. This leaves organizations to early to detect the areas of discrepancies across networks, servers and systems, without the need to identify sources to mitigate such attacks. This is especially useful for banking and finance institutes, where cyberattacks can lead possible to global chaos.
Predictive Analytics and NLP
Natural Language Processing automatically processes the human language in the form of texts, speech, videos and images to make it comprehendible and value-based for the system. By harnessing the power of predictive analytics with NLP, organizations can mitigate the cyberattacks by identifying the risk factors in the existing data that are vulnerable to security infringement.
Predictive analytics is a less explored tool for cybersecurity. But if deployed carefully, the method will eliminate a large percentage of security concerns across the industry.