With ever-growing technological innovations, the complexity of IT networks has risen sharply. Mobile devices, tablets, and wearable technology are generating huge amounts of data in real time that is getting exposed to cybercriminals. There are over 23 billion IoT devices connected to the internet today, which have created larger cracks for cyber attackers to exploit.
Information and business data are very important for any organization – they are increasingly taking note of the value of this data for their success in the current economy. This overwhelming reliance on information and data to make important decisions also requires protection of company and customer data. Organizations should implement strategies and may need to invest in testing security against penetration so that even the most sophisticated attacks can be foiled.
Today, the increase of cyber-attacks in terms of volume and complexity has made the traditional tools and infrastructure redundant. To combat the growing number of cyber-attacks, big data analytics is showing its immense potential and organizations are increasingly banking on it. According to a recent US Government survey, MeriTalk, 90% of respondents said they observed a decline in security breaches.
Visit Innovation Enterprise's Machine Learning Innovation Summit, part DATAx New York on December 12–13, 2018
Big data analytics refers to the process of evaluating large data sets with an aim to uncover hidden patterns, intricate correlations, market trends, consumer preferences, and other useful information. It uses advanced statistical and data science models for speed detection, while a real-time stream processing enables rapid threat analysis. As a significant technological push, organizations have finally reached a point where intelligent algorithms and large-scale data processing are helping them secure business data. These tools operate in real time and generate a small number of security alerts as per their severity level. These alerts are further augmented with additional forensic details for quick detection and mitigation of cyber breaches.
Here is how organizations can use big data analytics to address cybersecurity threats:
Analysis of historical data
Historical data can be used to predict attacks since the cost impact due to worms, viruses, or other malicious programs is massive. With the help of advanced analytics, businesses can create statistical baselines to figure out what is considered 'normal’, which in turn will help them determine data deviation. This risk evaluation along with a quantitative prediction of vulnerability to attack can help organizations develop counter-attack measures. In view of this, the Common Vulnerability Scoring System (CVSS) serves as a standard framework. Moreover, cybersecurity needs actionable intelligence in order to automate tasks so that the data is efficiently available and the analysis is sent to the right professionals in a timely manner. The historical data can also be helpful in developing predictive models, statistical models, and AI-based algorithms.
Monitoring and automating workflows
Big data security analytics can play a vital role in monitoring and automating workflows. That means it can also serve a valuable tool for employee system monitoring program since there are many cases of security threats due to employee related breaches. Many instances of data hacks involving employees are reported to have surfaced of late, which are termed as “inside jobs”. Automating helps in quickly responding to detected threats, for example, preventing clearly recognized malware attack. Big data analytics also facilitates the submission of a suspicious event to a managed security service for further analysis. In practice, this means having enough processing power to analyze billions of records within seconds.
Deployment of intrusion detection system
- With the help of network flows, logs, and system events, big data analytics can identify anomalies and suspicious activities. Since firewalls, data encryption, and multi-factor authentication are common security techniques, but intrusion detection system (IDS) is much powerful given the growing sophistication of cyber breaches. Typically, IDS can be categorized into three types, host-based (HIDS), network-based (NIDS), and hybrid. A HIDS system detects malicious activities on a single computer while a NIDS identifies intrusions by monitoring multiple hosts and examining network traffic. Sensors are strategically located at critical points of the network to perform monitoring. Hybrid-IDSs detect intrusions by analyzing application logs, system calls, file-system modifications. Furthermore, big data techniques like the Hadoop ecosystem and stream processing can transform security by:
- capturing large-scale data from numerous internal and external sources
- carrying out deep analytics on the derived data
- achieving real-time analysis
- providing an integrated view of security-related information
Machine learning will add new functionality
Machine learning (ML) enables computers to learn new behaviors based on empirical data. The advanced algorithms allow a computer to display behavior learned from past experiences, rather than human interaction. That means, when big data analytics meets with ML, a whole new possibility for cybersecurity opens up. By collecting, analyzing, and processing data based on previous cyber-attacks, machine learning can enable businesses to develop respective defense responses. ML can effectively fight spear phishing. The predictive URL classification models that are based on the latest ML algorithms, can identify patterns and reveal a malicious sender’s emails. Businesses can use these capabilities to detect vulnerabilities, identify a breach as it's happening and correlate information from multiple sources.
Stream data processing for real-time threat analysis
Stream data processing allow companies to analyze data as soon as it becomes available, thereby evaluating possible risks before they occur. Stream data processing connects to external data sources, enabling companies to integrate valuable data into the application flow and may be beneficial in scenarios where new, dynamic data is generated on a continual basis. It applies to most of the industry segments and big data use cases.
In this technology-driven world, cybersecurity risk is growing with enormous proportion due to the increased sophistication of cyber attack techniques. Business enterprises and government organizations need equally powerful cybersecurity measures to save their data.