Towards Greater Cyber Security with Big Data Analytics
Companies that are using big data and analytics are found to experience a considerable mitigation of cyber attacks. This is established by MeriTalk’s new US government’s survey wherein 84 percent participants confirmed use of big data to prevent such attacks and 90 percent confirmed reduction in security breaches.
It can be easily understood that large scale use of analytics can instill greater confidence to use analytics for detection of cyber threats. The issues in effective use of big data for detection of cyber threats revolve around how to connect the dots in real time and manage the huge volumes of security data. Faster detection of threats can help better prediction and subsequent action.
Managing the volumes
Given the fact that threats associated with cyber security are on the rise, there is a growing need to address the challenges effectively. Around 53 percent of respondents confirmed use of analytics for building their strategy and around 28 percent are accessing big data in restricted capacity.
It should be noted that according to 59 percent respondents the agency faced data breach on at least one occasion in a month since the analysis and management of huge volumes of data proves to be an overwhelming task.
The following major challenges to the use of big data analytics for detection of cyber threats can be shortlisted:
- Unmanageable volumes of data
- Lack of proper systems to collect the data
- Inability of procuring real time and fresh data
Effective use of big data analytics
It is now an established fact that big data can help security teams to gain greater yields in terms of effective recognition of security threats. It is possible to tap into external internal sources of information by leveraging available network telemetry, data streams from application layers and so forth for obtaining data and knowledge that is contextually relevant.
It is expected that security teams become agile and flexible as far as their interpretation abilities are concerned. Enterprises need to practice principles of liquid workforce and build staffing models that constitute internal as well as external staff including managed service providers.
Big data can only be used effectively if it is appropriately mined for enhancing cyber security. It is not possible to leverage meta data for maximum benefits in spite of its availability. Organizations need to find right people for effective mining of data for trends.
Relevance of big data is severely compromised due to delays in making data available to the right people. Risk management and actionable intelligence needs to focused for empowerment of cyber security.
In addition to sophisticated tools for data analysis, we need to develop high performance and automated tools to accelerate availability and analysis of big data so that right people are able to access the same at right time. This will significantly enhance classification and analysis of big data by analysts in order to improve relevance of data to the impending attack.
Importance of historical data
Ability to utilize big data in its raw format facilitates utilization of disparate data not only from the present perspective but also in terms of historical data. The historical data can be used to established normal paradigms to understand possible deviations from such norms. It is observed that the common indicators tend to be missed if these are presented in real time. However, we can derive new meaning by viewing such indicators over a period of time.
There are many possible applications of historical data including machine learning, predictive models, and statistical models to enable prediction of future events. Future course of any cyber attack depends only on your ability to out the data to its right use. If you fail to initiate a fitting response to cyber attack, the availability of big data will be meaningless. The only way to leverage big data to design an effective cyber security solution is to improve one’s ability to respond automatically to threats that are brought to the notice by data and to improving trustworthiness of data in terms of its accuracy and relevance.
Automation and artificial intelligence
It is expected that artificial intelligence will prove to be a great resource for improved understanding of the wider and run time realty picture of malicious activities for anticipation and subsequent actions.
By employing a pipeline of machine learning, operational analytics, and artificial intelligence a security team would be in a far better position to understand abnormal behavior related with application processes, network telemetry and file system. This way analytics can be exploited to for exposing even the new indicators of data breach.
Automation can be used in close association with artificial intelligence. It presents new models for engagement in order to empower security teams with exceptionally sophisticated tactics to respond to cyber attacks.
These will be in form of ability to manipulate data in near real time environment to effect a subtle change in the perception of attackers while targeting the enterprise. Similarly, automation can also be leveraged to mitigate the loss of time that is spent by security personnel while dealing with insignificant threats and noise.
Automation coupled with artificial intelligence can provide validation and boost response to commonly encountered issues including phishing attacks. The entire process of automaton can eventually enable security analysts to not only monitor but even predict the cyber attacks as they progress.
It can be safely predicted that by 2020 every enterprise will incorporate visualization of data as a vital aspect cyber security strategy. Visualization is associated with inherent ability of human beings to quickly establish patterns with available information to pinpoint abnormal behavior.
Security teams can leverage the ability of visualization to quickly understand contextual validity of the threat and the possible areas of business that can be impacted. Visual comprehension will increasingly replace the interpretations based on logs and texts and improve organization’s ability to predict security events.
It is also possible to blend visualization with artificial intelligence to treat abnormal patterns as actual threats with enhanced ability to interpret abnormalities of patterns as risks.
One needs to understand that bi data is not a panacea for cyber threats. In fact the role of big data is only limited to help organizations recognize abnormalities and identify advanced attack vectors.