Deep Learning and Security Applications – What is it All About?

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Machine learning, deep learning, and other aspects of artificial intelligence (AI) have become major basis for advanced technologies in any field. In the defense and security sectors, innovation in AI has gained momentum as a leading paradigm, enabling numerous innovations and developments that otherwise would not have been possible.

AI is used for developing efficient warfare systems, which are less reliant on human input. Systems equipped with AI can autonomously protect networks, computers, programs, and data from any kind of unauthorized access. AI techniques are being developed to enhance the accuracy of target recognition in complex combat environments. Combat simulation and training platforms also use machine and deep learning.

Unmanned ISR systems can either be remotely operated or sent on a pre-defined route. Equipping these systems with AI assists defense personnel in threat monitoring, thereby enhancing their situational awareness, according to marketresearch.com.

AI can assist in culling and aggregating information from different datasets, as well as acquire and sum supersets of information from various sources. This advanced analysis enables military personnel to then recognize patterns and derive correlations.

MIT’s technologyreview.com explains the terms often referred to in the media, including in our website articles regarding many of the HLS and security technologies.

The vast majority of the AI advancements and applications refer to machine-learning.  

Machine learning – Algorithms use statistics to find patterns in massive amounts of data – numbers, words, images, clicks, etc. Anything that can be digitally stored, it can be fed into a machine-learning algorithm.

Machine learning is the process that powers many of the services we use today, including not only recommendation systems like those on Netflix, YouTube, and Spotify, search engines, voice assistants like Siri and Alexa, but also airport security, mapping and documentation by unmanned systems and sensors, battlefield situational awareness, facial recognition, and more.

Each platform collects as much data as possible and uses machine learning to make a highly educated guess about future activities. The process is basically simple – find the pattern, apply the pattern. It is based on an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

Deep learning – Deep learning uses a technique that gives machines an enhanced ability to find — and amplify — even the smallest patterns. This technique is called a deep neural network — deep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.

Neural networks – Inspired by the inner workings of the human brain, neural networks nodes represent neurons, and the network represents the brain itself. Hinton published his breakthrough paper at a time when neural nets had fallen out of fashion. No one really knew how to train them, so they weren’t producing good results. It took nearly 30 years for the technique to make a comeback.

Supervised learning – Machine (and deep) learning comes in three flavors: supervised, unsupervised, and reinforcement. In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down targets once it knows the scent it’s after. That’s what you’re doing when you press play on a Netflix show — you’re telling the algorithm to find similar shows.

Unsupervised learning – In unsupervised learning, the data has no labels. The machine just looks for whatever patterns it can find. This is like letting a dog smell tons of different objects and sorting them into groups with similar smells. Unsupervised techniques aren’t as popular because they have less obvious applications. Interestingly, they have gained traction in cybersecurity.

Reinforcement learning – A reinforcement algorithm learns by trial and error to achieve a clear objective. It tries out lots of different things and is rewarded or penalized depending on whether its behaviors help or hinder it from reaching its objective. Reinforcement learning is the basis of Google’s AlphaGo, the program that famously beat the best human players in the complex game of Go.