Boosting enterprise security with deep learning
Businesses today continue to be bombarded by an increasing number of cyberthreats, as hackers become adept at identifying and exploiting vulnerabilities in security systems. A survey by the World Economic Forum ranked data theft and large-scale cyberattacks 4th and 5th in a list of the biggest risks facing our world. With cybercrime regularly hitting the headlines, regulators are implementing new security guidelines and costly fines for violations. Adding to the pressure are consumers who are increasingly prepared to abandon business with a company if they’ve been hit by a data breach. Businesses can’t afford to turn a blind eye to cybersecurity, which has now become a top priority for enterprises.
The growth of IoT over the last decade has meant that thousands, if not millions, of devices are now contributing to network traffic, and all are potential entry points for attackers. With Gartner predicting that there will be 20.4 billion connected devices by 2020, the potential for unprecedented exposure is only going to continue. Furthermore, the more devices on a network, the more data security analysts have to wade through, making identifying potential threats harder than ever – especially when reports suggest that UK businesses faced a cyberattack every 50 seconds in the second quarter of 2019. While we’re seeing increased awareness around the threat IoT devices can pose, worryingly, cyberattacks on IoT devices have already increased by 300 per cent in 2019.
Compounding the vulnerabilities IoT devices can bring to networks is the nature of cybercriminals, who are constantly evolving their attacks which are becoming increasingly targeted and sophisticated. Furthermore, they’re also collaborating in marketplace environments, sharing tips and advice on how to launch attacks that will cause the most damage.
Most enterprises still rely on traditional approaches to network security to defend against threats. This approach relies on feeding historical data – i.e anomalous activity that was suspicious or malicious – into a learning algorithm so the system knows what to look out for in the future. This enables the system to flag suspicious activity that corresponds to historical data to security teams, and prevent such attacks slipping through the net.
However, this approach is no longer adequate in today’s evolving threat landscape, because it hinders an organisation’s ability to investigate activity that hasn’t been seen before, causing them to miss new attacks. Furthermore, behaviour that is deemed “normal” or “good” within an organisation is constantly evolving, and businesses have to be able to adapt in real time.
