The Internet of Things (IoT) encompasses many avenues and aspects of modern technology and life. Our devices have enabled us to become a more connected society because they have been designed specifically to connect to one another. But with this connection comes many challenges. As data is shared between the ever-expanding network of "smart" devices, we have to look at how we can protect that data in ways we haven't previously had to.
What is IoT?
The Internet of Things is basically a network of connected devices. Or as one Forbes writer excellently described it: "...the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes everything from cellphones, coffee makers, washing machines, headphones, lamps, wearable devices and almost anything else you can think of. This also applies to components of machines, for example a jet engine of an airplane or the drill of an oil rig.". When we talk about IoT, we're talking about the revolution of technology-driven smart devices that have changed the way we live, work, and play. As more and more devices are developed and connected, there are just as many questions and issues that come into focus.
Among these issues for everyone, individuals and organizations alike, is the issue of data security. Each device stores data and transmits it through the Internet. This data could be personal (such as health information from wearable devices, protected customer data, or even just internal data that an organization's many systems send back and forth). Whatever type of data it is, the vast majority of it is private data that no one else should see. The systems that store this data can be targets for phishing, malware, and virus attacks because hackers know this is where they can gain entry to the data and potentially wreak havoc on a system.
Predictive Analytics helps alleviate some of the changes in attack strategy by introducing a new way to look at security. It approaches security from a proactive rather than reactive stance. The concept is fairly simple - predictive analytics takes past and current data and finds patterns in attack behavior to predict future breaches. It does this in a number of ways. A log monitoring system is a solid way to track the signature of every machine that comes into contact with a network. These logs are combined with current trends in cyber attacks across the world and attack event histories to put together an accurate picture of attacks that may happen in the future.
Traditionally cyber attacks have been detected by taking the past and present signatures (i.e. the unique digital "fingerprint" of an attack) of known attacks and looking for those in the current system files. While this is a valid and very important part of preventing attacks, there is also an increasing trend of attacks where the digital signature changes every time, leaving systems vulnerable because they can't detect a signature that changes every time it attacks. This is very similar to how some viruses in the medical world work as well, and I can only imagine that these attacks may have taken their inspiration from those viruses.
What predictive analysis does is take this analysis work one step further to detect attacks that change their digital signatures. The systems are designed to be self-learning, meaning they adapt and reason their detection techniques to what they learn about the virus or attack. The self-learning analytics study anomalies, or differences, in data behavior and patterns, not just attack signatures, and monitor activities across multiple networks and real-time data streams. This helps them identify threats as they occur without having to know exactly what an attack signature may look like. By studying anomalies, predictive analytics can immediately detect differences in network traffic and data flows and compare them to a predicted normal behavior pattern of a system. The analytics system also constantly changes and updates it's definition of "normal" behavior based on what it finds, which minimizes the risk of false-positive alerts on potential attacks.
What does the rise of IoT mean for your business?
The development of predictive analytics is a big win for the future of data security and attack prevention at every level. With IoT connecting more and more systems and data every day, organizations should consider their security strategy against the rise of attacks that traditional systems can not detect, and how predictive analytics can help mitigate those threats.
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