Into The Future: Deep Learning Apps That Can Target Malware Become A Possibility
As per the tech available today, unless and until a programmer tells a program that something’s malicious, malware killing programs are unable to do their job well. However, what if the programs we use had the ability to identify malware on their own? Fortunately, this is no longer a case of what if.
Some companies are in fact, trying to develop a technology that gives programs the ability to identify malicious software. They call this technology ‘Deep Learning’ which takes inspiration from the human mind.
Deep learning is based on neural networks with perceptron being the fundamental unit of these networks. Similar to neurons, perceptrons do not do much on their own but become powerful when linked together as each perceptron performs some algebraic function by taking in numerical inputs. If the result is above a particular threshold, it returns 1 and if not, it gives a 0. However, ever if a programmer designs the program to evolve before each new contagion or alters a few lines of code, computer malware often manages to find its way through antivirus software.
A better way of catching such harmful code is offered by feigned neural grids. These grids or networks use malicious and non-malicious file examples to recognize the characteristics of malicious code. They also make use of the deep learning approach which necessitates multiple-layered simulated neurons network training. According to its developers, the deep learning software does a significantly better job of catching malware than existing antivirus software. Although these claims have not been verified as of yet, some people have explored the usage of deep learning and most of them find the results satisfactory when it comes to winning the battle against malicious software.
The network that deep learning contains can appropriately identify new examples when it is fed with a sizeable amount of malware and non-malware examples. Deep learning systems can then tell if a file bears similarity to a malware that already exists. This is where it surges ahead of the existing antivirus software. An alteration in the coding it uses can easily fool existing antivirus software whereas it is difficult to do the same with deep learning systems.
Taking all the above-mentioned things into consideration, it is easy to see why deep learning is seen as the next step in improved software security. Today, a growing number of startups and large tech companies are aggressively pursuing deep learning. The performances of voice recognition software and handwriting recognition are two things the deep learning approach has already improved. Additionally, it’s being used for many complex tasks.
If organizations use tools that learn without training, they will be better placed to protect themselves. This simply means eliminating the system’s need for human instructions to identify normal behavior or traffic in a server or application. When this dependency on human instructions is eliminated, systems will be able to automatically identify unusual or abnormal usage patterns.
The good news is that we are close to achieving that and it won’t be long before deep learning apps that can target malware become a possibility.