With the advent of information technology, organizations are beginning to rely more on interconnected networks for business productivity. Therefore, the reliability and security of these network assets have become more important than ever. Cyber security is a set of tools and guidelines. It is used to protect computer networks, application programs and sensitive data from unauthorized access and possible misuse. Analysis of the security events and device logs can help prevent attacks in the first place. This is done by identifying patterns in previous threats over databases, hardware components, firewalls and application services. To facilitate this process, experts in cyber security are utilizing the potential of Big Data Technology in data security.
Limitations Of Traditional Security Systems
The widespread successful execution of security attacks including WannaCry and NotPetya ransomware attacks has proved that the traditional security systems and procedures are redundant. The reasons why traditional security solutions are inadequate include:
- Data Retention: The number of devices in a network is increasing exponentially each year. The traditional security infrastructure fails to retain the large amount of data flowing in a network. It requires additional storage and advance compression mechanism.
- Analyzing a Large Amount Of Data: In a big enterprise, enormous data is produced each day. It becomes challenging for traditional systems to analyze and identify patterns, errors, and security holes. It requires more sophisticated and cognitive algorithms that can be used to find any anomaly in data security. Doing so can prevent threats in advance.
- Real-Time Monitoring: You might be protecting your organization from known threats. But attackers are constantly evolving their tactics to take advantage of any possible vulnerability. A real-time monitoring system with advanced machine learning algorithms is required for security information and event management (SIEM).
- Advanced Malware: Also known as Advanced Persistent Threats (APT), advanced malware can inject, communicate and control data flowing in the computer network. It can camouflage itself and it remains undetected by the traditional antivirus systems. It has learning capabilities that can be used to create botnets to cause havoc on an IT infrastructure.
How does Big Data help In CyberSecurity?
The large volume of data, both structured and unstructured is termed as Big Data. The term Big Data Analysis refers to large-scale information management. Advanced analytics technologies exceed the ability of traditional data processing systems. To address the limitations and provide remediation of traditional cyber security solutions, organizations are utilizing big data technology in their security systems. This has empowered them to store and analyze large heterogeneous data at an unbelievable speed which mitigates the security flaws in the system. The following points highlight the use of big data technology for security in IT facility:
Big data is used to gather information from multiple devices clustered across the network. It includes gathering accessibility, transactions and application logs for identifying the IP addresses used to access the network. A network administrator can easily utilize this information to know what is happening inside the network. Billions of DNS requests producing enormous data can respond correctly at an ISP that can be analyzed later. If any anomaly is found in the system, it can document the report and notify the concerned authority for its possible remedy. When Big Data log analytics is combined with JIT (Just in Time) analysis, it collects information on the machines that have an open connection with locations outside the local network.
SIEM system in an organization assists in regulatory compliance and forensic analysis of security breaches. It should have the ability to integrate with internal/external devices to identify real-time threats and correlate them with terabytes of previous data quickly to find patterns in security attacks. With advanced data mining algorithms, Big Data minimizes the false threat notifications the and management efforts needed in distinguishing anomalies.
Advanced Persistent Threat Detection:
ATPs are the most challenging security threats faced by organizations. APTs are performed by highly-skilled attackers that target sensitive information in data centers. The massive volume of data makes threat detection finding a needle in a haystack. Big Data technology deploys anomaly sensors on end systems of an organization. A single sensor can, for example, track the external links clicked by a user in order to identify any dangerous access pattern. This information can be used to deploy a behavior-based authentication suitable for providing access to a subnetwork.
Some prominent big data security tools are:
- Hadoop https://hadoop.apache.org/releases.html
- Qubole https://www.qubole.com/
- Apache Spark https://spark.apache.org/
- Cassandra https://hadoop.apache.org/releases.html
- Hive https://hive.apache.org/downloads.html
The needs of new security challenges because of the growth of security intelligence requires big data analytics. The goal of big data technology in cyber security is to provide real-time analysis of threats so that a quick remedy can be deployed. Although the utilization of big data tools and technologies in cybersecurity offers significant advantages, it presents challenges that are unique to cybersecurity. This is understandable because the same tools that are deployed for network security can be exploited to launch attacks on any machine on the Internet.