Deep Learning & Cybersecurity
Uploaded on 2019-04-25 in TECHNOLOGY--Developments, FREE TO VIEW
The cyber-electronic-scape is a changing environment as current network security measures like signature-based detection techniques, firewalls and sandboxing are failing to keep up. With corporate networks becoming a prime target for threat actors, software vendors are beginning to use deep learning and other types of AI in cybersecurity.
Much of the progress we’ve seen in artificial intelligence in the past five years is due to deep learning.
Advances in software algorithm models, processing power and dramatically lower costs have put deep learning within reach of more companies, opening the door for broader innovation in products and services, and also supporting the execution of complex business processes.
The startup company, Blue Hexagon, has developed a deep-learning-powered network security platform, which was able to detect an Emotet infection as soon as it hit one of Heffernan Insurance Brokers’ servers. Deep learning and neural network technology are some of the most advanced techniques that can be used to help defend an enterprise from threats. Although deep learning was having a significant impact on image and speech recognition, these techniques were not being used in computer security.
The company’s deep learning platform focuses on threats that pass through the network. It looks at a packet as they flow through the network and applies deep learning.
The Blue Hexagon deep learning models inspect the complete network flow, payloads, headers, malicious URLs and C2 communications, and are able to deliver threat inference in less than a second, according to the company. Threat prevention can then be enabled on firewalls, endpoint devices and network proxies. However, Gartner analyst Augusto Barros told techtarget.com that “many machine learning implementations, including those using deep learning, can find threats, such as new malware, for example, that has common characteristics with what we already know as malware”.
“They can be very effective in identifying parameters that can be used to identify malware, but first we need to feed them with what we know as malware and also with what we know as not malware so they can learn. New threat types … won’t be magically identified by machine learning.”
Nevertheless, there are several advantages of Deep Learning versus the more traditional Machine Learning:
- Deep learning algorithms thrive on big data. The generalisation error bound shrinks as the training data set size increases. This means that while deep learning continues to excel in performance and efficacy, traditional machine learning systems will plateau at some point no matter how much more data you train it with.
- Deep learning models can represent complex non-linear separating functions. Certain tasks require the ability to learn complex concepts, deep learning is the ideal technique to solve this. No feature engineering is required and therefore minimises the likelihood of introducing human bias to the model.
- Deep learning can harness parallel computational power to learn better models, with the fast advances of GPUs, deep learning models can be trained and optimised in a more efficient manner than before.
Some other services that have shifted from traditional machine learning to deep learning include face detection, speech recognition and language translation.
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