This is a sad story you may have experienced.
You are hot and thirsty, and you see a bottle of water on the table. It's too late to think. Oh! leak! It is oil!
When the time is back to 10 seconds, we come back again.
This time, the extra plot is that you have an uncomfortable enemant who lives with you (this situation is easy to find in the co-tenant army). He put a bottle of unknown liquid like water on the table.
You came in again, tired and hot and thirsty, and this time you picked up the bottle of liquid again. This time, you carefully analyzed the substance, shape, and volume. You used your previous experience of struggle to judge again, and then confidently made the right choice and perfectly avoided this prank—a bottle of 100% pure urine. .
The gospel came
If I place this bottle of water-like thing under a conventional computer vision module, it can easily identify its components. If I owe my hand, I have to grab the bottle and try again. Due to the glorious appearance of the fingers, the traditional computer vision module suddenly cannot be identified. However, if I smartly upgrade the system and add an artificial intelligence module, the so-called deep learning technology, this bottle of liquid can be recognized even if my fingers come out. This is the benefit of deep learning with small changes—even if only part of the data can be read, most of the images are masked and correctly identified.
Deep learning, like what people know as neural networks, is stimulated by the brain and continuously enhances the ability to learn to recognize objects. Using visual recognition as an example, our brain can obtain raw data through sensory input while further learning higher-level features. Similarly, in deep learning, raw data is read from a deep neural network, learning how to identify objects. Machine learning, from another perspective, requires manual selection of features for processing through the machine learning module. As a result, this process is time consuming and the accuracy is limited by human errors. Deep-learning is more complex, sophisticated, and capable of autonomous learning. It can guarantee high accuracy and ultra-fast processing speed.
Network security is similar to image recognition. More than 99% of new threats and malware actually originate from minor "mutations" of previously existing threats and malware. It is said that even the 1% of completely new new threats and malware are just a large number of "mutations" of the existing crisis. However, despite this, even the most cutting-edge network security technologies that use dynamic analysis and traditional machine learning are encountering numerous difficulties in detecting large amounts of new malware. As a result, businesses and organizations are vulnerable to data leakage. , data theft, malware seizure and data corruption.
The Gospel is coming. We can solve these problems through deep learning applications and defend our cyber security.
Two types of old methods "alien and eggs"
Let's briefly review the history of detecting malware programs.
Signature-based solutions are the oldest forms of malware detection, and they are also known as traditional solutions. To detect malware, the anti-virus engine compares the content of an unknown piece of code with a known malware signature in its database. If it does not match the known malware signature, then a manually-adjusted heuristic algorithm is used to generate a new manual signature and then update the publication.
This process is very time-consuming. Sometimes the signature is released after a few months of initial testing. Therefore, this detection method cannot keep up with the times and cannot keep up with the pace of producing millions of new malware variants every day. This has also caused companies and organizations to be vulnerable to new threats that have been detected without signatures. Attack.
Using heuristic techniques based on the characteristics of code behavior to identify malicious software creates behavior-based solutions. This malware detection technique analyzes the behavior of the malware while it is running, rather than hard-coding the malware code itself. The main limitation of this malware detection method is that it can only discover malware when malicious actions have begun. As a result, prevention has been postponed and sometimes it has even been dealt with too late.
Sandbox solutions are based on the development of behavioral detection methods. These solutions execute malware in a virtual environment to determine if the file is malicious, rather than detecting runtime fingerprints. Although this technique has proven to be quite effective in detection accuracy, real-time protection costs are high due to the long process time. In addition, new types of malicious code can perform escaping sandbox detection by delaying, thus posing a new challenge.
Deep learning test results are significant
The use of artificial intelligence to detect malicious software came into being.
Combining artificial intelligence to create more sophisticated detection capabilities is the latest step in the evolution of network security solutions. A machine learning-based malware detection method applies a more detailed algorithm to determine whether a file's behavior is malicious or legitimate based on the characteristics of manual engineering. However, this process takes a long time and requires a large amount of manpower to determine the technical parameters, variables or characteristics during the file classification process, and the key points in the file classification process. In addition, malware detection rates are still far from 100%.
Deep learning of artificial intelligence is a high-level branch of machine learning, also called "neural network", because it works in the same way as the human brain. Advanced cognitive tasks take place in the outer cortex of the human brain, and we have billions of neurons that can learn through various types of data. Because deep neural networks are the first unit of arithmetic in machine learning and do not require manual engineering features, this is a great revolution in deep learning. Not only does it not require manual engineering, they can also process high-level features of raw data processing and autonomously learn to recognize objects. This approach is very similar to the way that the human brain learns raw original data through sensory input.
Come and see my gestures, perfect!
When applied to network security, without any human intervention, such as knowing in advance whether the file is malicious or legitimate, the core engine of deep learning has been constantly learning and upgrading in this situation. When detecting first-time malware, Compared with traditional machine learning, the solution based on deep learning presents very breakthrough results.
In true environmental testing of endpoints based on publicly known databases, the detection rates of mobile and APT malware are also significant. For example, deep learning-based solutions have detected and recognized more than 99% of large and slightly modified malicious code. These results are consistent with the performance of deep learning in other areas such as computer vision, speech recognition, and text understanding.
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