Stealthy hackers and targeted attacks are making it difficult to detect threats to users’ identities, especially in growing enterprise networks. More users and devices contribute to an increase in data, which must be monitored and analyzed for risks and potential breach activities.
The 79% increase in account takeovers from 2017 to 2018 points to overburdened IT departments lacking the resources to handle the monumental task of combing through data for malicious actions and responding to attacks upon discovery. Machine learning (ML) provides powerful tools to help with threat monitoring and detection and increase protection for all network users.
Learning and Determining Risk Levels
To “learn” what breach activity looks like, ML systems must be taught using either supervised or unsupervised learning methods. In supervised learning, ML tools are presented with known data sets, such as user behaviors, tagged as normal or aberrant. This establishes a statistical model the system later uses to differentiate between standard user activities and signs of network infiltration. The IT teams can adjust ML algorithms to correct false positives and improve future performance.
Unsupervised learning occurs when an ML system draws on known information about a person or group of people performing actions or making access requests on the network. Systems can then determine whether to approve or deny requests based on users’ privilege levels and access requirements. After initial “training,” ML is able to continue to learn new patterns and behaviors. Known as self-learning, this process enables classification of actions according to risk level to detect hacker infiltration without human intervention.
Protecting Users and Devices
The users accessing enterprise networks may be employees, vendors, suppliers or customers and may interact with data using a variety of devices. Businesses must address vulnerabilities and security loopholes to safeguard sensitive data and prevent network infiltration.
By training ML systems to understand and differentiate between varied types of user behaviors, enterprises can implement safeguards to be deployed automatically when malicious activities are detected. This minimizes the risk of fraudulent transactions and saves businesses the hassle and expense of cancellations and refunds.
Machine learning algorithms can also detect threats on devices while devices are in use, which prevents users from unknowingly infecting networks with malware from compromised devices and locks out hackers attempting to use stolen devices to gain access to network data.
Authentication and Fraud Detection in Real Time
The best IT department could spend every waking moment analyzing network activity and still fail to catch subtle attempts at identity theft or hackers operating with stolen credentials. Incorporating ML enables security systems to consistently monitor data sets and behaviors while learning and updating in response to new information.
Because ML operates in real time, problems are flagged at the moment of discovery. Alerts can then be passed on to the appropriate people in the IT department, or a predetermined solution can be deployed to prevent network compromise. Continual assessment of behaviors and risk levels supports smarter approval and denial of access requests, thus minimizing false positives and allowing IT departments to address real threats before user data is compromised.
Building to Scale
Humans can only handle so much data before requiring help, and with the massive scale of information collection and analysis at the enterprise level, it’s not practical to continually expand the IT department in an attempt to keep up with the influx. Even small businesses deal with a significant amount of data and benefit from the assistance of automated systems.
When using ML, more data is a help rather than a hindrance. No matter how many users and devices are introduced into the network, a security system with ML can continue to learn new sets of patterns and behaviors. Increased detail refines the system over time and reduces unnecessary security alerts. Businesses are free to diversify network access without risking compromise or outpacing the system’s ability to monitor network use.
Cybersecurity experts use ML to delve deep into the dark web and gather information to inform businesses of potential breach activities in advance. Just as ML can monitor enterprise network activity, it can also collect data from across the numerous channels hackers use to communicate and do business. Activities can be analyzed for potential threats, such as sales of detailed identity information or transfers of malicious files. Cybersecurity experts either use this information to enable the companies for which they work to protect their networks in advance or provide the results of data analysis to allow enterprises to improve onsite threat detection and response.
Making ML a primary tool in identity theft prevention helps safeguard businesses against inevitable attacks and preserve the identities of all users with network access. In combination with a qualified team of IT professionals trained in identity protection, ML supports a safe network environment and protects sensitive business data from clandestine threats.