The last few years has seen an increase in cyber attacks – whether it is hacking into personal data or bringing down electric grids or tampering with federal data. According to the State of Cyber 2019 report, there is an exponentially increasing breach rate of 232 records/sec. This is only going to see upward trends as the number of connected devices increases, exposing the risk of cyber attacks.
Source: Wipro State of Cyber Report 2019
It is humanly impossible to handle the terabytes of data that is vulnerable to such attacks. Automation is the only answer to this challenge of defending our data. However, unlike traditional software, Artificial intelligence tools like machine learning can plough through the vast quantities of data to find vulnerabilities, hacking patterns and response mechanisms. Machine learning is a discipline of AI where an algorithm can be help in learning from vast quantities of data and make predictions without being explicitly programmed for an output.
Here we take a look at five ways to use AI and machine learning to fight cyber attacks.
1. Intrusion detection:
Typical intrusion detection and defense software use monitors based on previously classified intruders and malicious attributes. Using deep learning, a technique of machine learning, intrusion detection can identify previously unrecognized patterns. Deep learning has the ability to learn from highly unstructured data coming from heterogeneous environments. They are better than other forms of machine learning due to their ability to learn incrementally and extrapolate new features from a limited data set.
2. Multi-entity response:
With the advent of machine learning, a new form of Intelligent Threat response is being used to rapidly and accurately respond to threats. Based on the results obtained by threat detection, threat responses can be driven by machine learning algorithms. These responses are typically undertaken based on recommendations by the users. Based on the type of threat, AI programs can block the source automatically or outmaneuver by sending false signals to gather additional information. As threat volume increases, it is increasingly useful to deploy automated responses to cyber attacks in order to reduce the security incident response times.
3. Tracing the dark web:
Dark web is content on the Internet that requires specific software, configurations or authorization to access. It is usually a nesting ground for illegal activities and can be a source for emerging cyber threats. Machine learning can be used in two ways to monitor activities in the dark web 1. To identify potential threats and keep you abreast of the upcoming trends of attacks or patterns of detection and 2. To identify any information pertaining to your organization, your employees or your products. They can also be used to identify if your company assets like software source code are being openly developed or traded. The exploits identified in the dark web will help accelerate your responses to any attacks. As most hackers constantly change their IPs and domain infrastructure, it is almost impossible to track their activities using traditional mechanisms. Machine learning is helpful to gather insights into these chaotic patterns. Another feature of the dark web is the use of local languages and machine learning and natural language processing can be used to successfully transcend these linguistic and geospatial barriers.
Source: Kali Tutorials DarkWeb Statistics
4. Endpoint and network monitoring:
Cyber security teams are often challenged with reduced budgets and increase in security activities such as detection and response. Automating the monitoring of networks and device endpoints is crucial to ensure compliance with your security governance rules. Machine learning/AI provides you the tools to automate the monitoring process. Machine learning can also help you break down data silos and authenticate all users accessing the various sources of data – whether it is transactional or reporting systems. With the help of machine learning, you can monitor new variants of malware by understanding and learning from various aspects and attributes of malware or viruses. You can also use machine learning to simplify your multitude endpoint and networking monitoring tools and consolidate them into a single dashboard.
5. Third party detection:
While in-house systems, applications and devices are vast in a huge organization, it is almost impossible to keep track of third-party systems like vendors and suppliers that often integrate with your systems. Your ecosystem multiplies your risk and exposes your systems if they do not take security as seriously as you do. Recent research shows that organizations are way behind on instituting the governance and technology around third-party risks, across software supply chain, access governance, or data handling.
Machine learning can be used to detect, monitor and alert data coming in and out of third party systems by learning the patterns of data or breaches that occur. In order to effectively manage security of third party data, you would need additional monitoring, controls and governance in place. Machine learning can help you automate the monitoring process across a wide variety of unstructured data. It can also be used to enforce system controls and security policies.
In conclusion, we are in an age of data proliferation, increased cyber-attacks and cyber security incidents. The only way to manage data protection, reduce risk and increase security is to automate the process. Artificial Intelligence mechanisms like machine learning can help with sifting through the vast quantities of data and use intelligent algorithms to learn and detect patterns of vulnerability so that cyber threats are thwarted and your organization is protected.