Cybersecurity systems are evolving every day to prevent cyber threats that are becoming more and more vicious. Internet and automation are undoubtedly one of the biggest advancements in the last couple of decades that totally revolutionized the modern technology and business world. Every day, we face a huge amount of data transfer and we are quite nonchalant about it. However, all this data is unprotected without a proper security system. In the case of huge corporate companies that have enormous sensitive data storage and data transfer, they are extremely vulnerable to security attacks and threats. To protect it, they need an efficient and reliable security method.
Antivirus is already a thing of the past now that cannot handle the complicated security threats of today. Currently, deep machine learning is a popular security measure used by companies. However, a better AI-based security system is already developed, which is called deep learning. It is already about to replace machine learning as it has an upper hand in all the advantages it provides. Therefore, this article will try to explore the current hot topic: deep learning vs machine learning, which is better?
Brief Introduction: Machine Learning
Before moving on to the comparative discussion, here is a brief idea of machine learning to gain a better understanding. Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Brief Introduction: Deep learning
Deep learning is actually a subfield of machine learning that is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It learns to do classification directly from the source.
The Debate: Deep learning Vs Machine Learning
As deep learning is a subcategory of ML that uses artificial intelligence to learn, it sounds similar to machine learning. However, the two are quite different and deep learning is much superior to machine learning in the application. The two are not unrelated, but deep learning can be seen as an upgrade of ML itself.
- Domain Expert – In regard to a domain, machine learning needs human intervention for feature-engineering and extraction. The human domain experts define and develop features for the machine to conduct the classification. On the other hand, deep learning does not require human intervention. It is fully autonomous, which means it can collect and analyze raw data on its own. Therefore, it reduces the need for a human supervisor, which also reduces cost.
- Content Usage – Machine learning technology only uses 2.5-5% of the total available data. It converts the data into a small vector of features like statistic correlations. So in effect, it is throwing away a large chunk of data without using it, making them a total waste. However, the deep learning technology is able to process 100% of the available raw data and that too with a massive number of characteristics like pixels, waveforms or bytes in order to make a decision.
- Correlations – The dependency of humans limit the algorithm of machine learning. Thus, it neutralizes other correlation patterns, which could not be rationalized by the features that were pre-defined. Deep learning uses raw data directly, therefore, is able to determine non-linear connections between data that are too intricate for a human to define.
- Domain Expertise and Time Consumption – Machine learning currently supports only PE file types. Feature extraction is a completely different process, therefore the effort and knowledge commence from scratch. But deep learning supports all file types without requiring any modification or adaptation.
- Prediction Quality – Machine learning cannot provide protection from unknown threats. In contrast, deep learning can understand and defines by itself what is relevant or when to predict attacks.
- Human Error – Because of the involvement of human domain experts, there is a chance of human error in defining the features. The fully autonomous deep learning does not have any human error.
Machine learning provides limited visibility of the content while deep learning looks at the whole content of the files. Considering all these points, deep learning obviously comes off as a better choice for the future of cybersecurity. So, if you are looking to upgrade enterprise security, deep learning software would be the best choice.
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