In today’s digital age, every industry is heavily dependent on technology. We are putting software everywhere and while this is changing our lives in a positive direction, the future maybe slightly worrisome. Thanks to the advent of Internet of Things (IoT), it is predicted that by 2020, the internet will connect a whopping 200 billion things worldwide. One can only imagine the massive amounts of data generated, stored and processed which is sacred to every company or organization.
Most of the technologies today from automated alarm systems in your house to industrial systems can be hacked. Hence stating that all the information is vulnerable to outside attack and no matter how skilled the network security professionals are or however thick your firewall is, the network infrastructure is not secure. In 2016, about 300,000 SimpliSafe alarm systems were hacked and the only solution to it was to replace the pieces. This is a proof that cyber attacks are costing millions of dollars to businesses, and are the biggest threat to companies globally.
Why the traditional methods do not work?
The connected devices create a humongous amount of streaming data, and on the other hands, attacks are getting multifaceted too. Difficulties arise when companies are working on older and newer systems together where one insecure component damages the entire system. As a result, it becomes troublesome for companies to diagnose the breach in security. The existing security tools are not sufficient to process the current volume of data generated and thus assessing even one threat in an enterprise is getting time-consuming.
Systems such as logging, network device events, security information, compliance reporting and file integrity monitoring are important to a company’s network defense arsenal. These are highly vulnerable to cyber-attacks and have limitations in their ability to monitor and indicate anomalies.
A single successful attempt into the systems and the whole network can be exposed to the hackers, in such case, one cannot merely prevent the attacks from happening. So, it necessitates companies to detect them in real-time and respond fast. The National Institute of Standards and Technology (NIST), recently issued updated guidelines in the risk assessment framework that recommends companies to shift toward continuous monitoring and real-time assessments. This can be achieved through the use of big data analytics.
Big data provides robust security solutions
A proactive mode implies looking into all the information and applying predictive analytics techniques to determine the probability of a threat, detect the abnormal behavior and respond to alleviate it. The existing tools and software in Security Information and Event Management (SIEM) systems should be optimised to bring automation into the picture.
Companies today need complete visibility of their data- a comprehensive, centralized view of all relevant data, both current and historic. Historical data is useful in recognizing the deviations from the normal behavior and has opened up new paradigms of statistical and predictive models. If monitored over a period of time, it can predict the likelihood of certain attacks happening in some selected places such as an important database, for an instance.
Secondly, the companies need high ingestion speed of big volume of data. Security telemetry or reports are constantly generated which needs to be stored at high speeds too. Hadoop is one of the most popular tools for big data analytics. Hadoop processes large, heterogeneous data using a core programming model named MapReduce. It analyzes massive amounts of data and mitigates potential threats including the botnets that are a major threat to the Internet.
Big data analytics as a holistic approach
The real-time processing of data streams provides information like threat locations, what data was leaked and where was it sent; all of this with the incorporation of automation. The reports can be sent to the right people at the right time who knows what to do with the data and patterns deciphered.
Today a holistic approach to cybersecurity includes an amalgam of big data technologies, machine learning and data science. Agencies worldwide are in favor of these technologies working far better than the traditional approach, especially for bigger organizations and government agencies. According to a survey, 94% of federal agencies plan to invest in big data technologies, and 84% of big data users have reported success in thwarting threats.
The global cybersecurity market was valued at US$122 billion in 2016 and is expected to reach US$231billion by 2022. Major areas of growth within the cybersecurity market include security analytics, threat intelligence, mobile security, and cloud security. However, the market is faced with challenges and constraints such as inadequate infrastructure, high cost, and a lack of skilled workforce. Developing economies should invest significantly in big data analytics tools with the required infrastructure, and education in order to secure its booming IT industry, and retain innovation and growth.