Artificial Intelligence: Deep Learning and its step up in Cyber-Security

Source: Deep Learning on Medium

Artificial Intelligence: Deep Learning step up in Cybersecurity

Artificial Intelligence (AI) is contributing to several sectors of our life, including cyber-security. AI brings a huge improvement over traditional algorithm by its unique algorithmic analysis, which is applied to a big amount of data — concluding a result based on the context and info gathered from the previous similar situation. Both the attackers and protectors can apply Artificial Intelligence for learning to get a better result. Thus, AI uses its algorithm to learning something from the previous result and it helps to build more secure, user-friendly security solution systems with minimal mistakes.

In a cyber-security context, Artificial Intelligence (AI) is a piece of software, developed with its unique type of algorithm which analyses a huge amount of data and observes certain situation optimally to detect possible actions and take action again any defined condition.

Many companies already using Artificial Intelligence in their security systems to provide better security to users, as well as themselves. As hackers are relatively intelligent, they may utilize a more sophisticated system and most of the companies are not actually prepared to face possible attacks.

In this article, I will describe “Deep Learning”, some interesting applications of deep learning in cyber-security and how deep learning can be used to have better security measures for everyone.

What’s Deep Learning in AI/ML?

“Deep Learning” — a branch of Machine Learning (ML), related to a wider category of AI (Artificial Intelligence). Deep learning uses “RNA” — formerly “Artificial Neural Networks”, which is designed based on the idea of the human brain. Artificial Neural Networks mimic the functionality, activity and the connectivity of the neurons of a human brain.

DL is called “Deep Learning” because it uses deeper artificial neural networks that other AI branches such as machine learning (ML). In this type of networking, the neurons are divided into layers and the quantity of the layers indicates the depth of the network. As an example, the most common and familiar type of neural network is the “Convolutional Neural Network” — CNN. CNN is usually used for many computer vision works. However, RNA’s performance boost as the network progresses in its learning. The main goal of “Deep Learning” is to make it capable of training itself and learning independently without any human supervision.

How Deep Learning Works?

In a Deep Learning network architecture (basically RNA), there can several layers of a network, with several connection types. The first layer is fed with input, which deals with the different layers of the neural network. Each layer has different functionality that analyzes the input and makes needed changes. After going through several layers, the algorithm modifies the input in a particular order, ending up with a predicted output.

Some deep learning frameworks like “TensorFlow” or “PyTorch” allow you to make your own neural network and learning models conduct deep learning experiments. For a beginner, both “TensorFlow” and “PyTorch” would be good to start, as they’ve well documentation and resources from beginner to advanced level.

As you’ve already known, deep learning is different from other AI fields like traditional machine learning. What makes it unique is that during complex analyzes, all the needed characteristics of the processing won’t be supervised by a human, but directly by its unique deep learning algorithm.

Cybersecurity: Interesting Applications of Deep Learning

Now that I’ve covered what is “Deep Learning”, how it works and some basics of Artificial Intelligence. Now it’s time to have a look at how deep learning helps in cyber-security to prevent security risks & threads:

1. Intrusion Detection System & Intrusion Prevention System (IDS / IPS)

IDS/IPS systems automatically detect malicious activities on the network and prevent intruders from getting access to the system — alerting the user. Usually, they can be recognized by known signatures and generic forms of attack. This is helpful to prevent security threats like data breaches.

Usually, Machine Learning algorithms were used to do these kinds of tasks. But, these ML algorithms caused the system to create many false positives. Removing these false positives is a tedious job for security experts and it causes unnecessary fatigue.

Deep learning can help to build a smarter detection system with the help of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These systems can analyze traffic more precisely, reduces false alerts, and helps security teams to identify suspicious network activities.

2. Detecting Malware

In the field of cybersecurity, AI systems help to detect and defuse security threads more efficiently and effectively by analyzing a huge amount of data without any human supervision. For examples, an Artificial Intelligence (AI) detection system, which includes:

· Detection of new malware and viruses via abnormal pattern recognition technology of apps

· Anticipation and neutralization of a thread in its first stages

· Isolation of systems before being infected

· Automatic detecting malware and blocking fraudulent activities of phishing, spamming.

Common firewalls use a traditional malware detection system — which detects malware using a signature-based detection system. The security providers manage a database of known threads and update it as soon as they know about a new thread. Though it works well in most of the cases, it’s risky against the latest threads — possibly unknown by the security service provider.

Deep learning algorithms shines here. These algorithms can anticipate, detect and defuse more advanced threads comparing to traditional firewall systems. It’s also database independents — which ensures that it can detect the most recent malware more accurately and efficiently. A deep learning algorithm learns the system and starts to recognize suspicious activities on a system which might be done by malware or poorly optimized applications.

3. Detection of Spam and Blocking

Deep learning can help you to easily anticipate and treat spam and other types of suspicions activities by “Natural Language Processing” (NLP). NLP is a deep learning technique, designed to learn the forms of communication and language patterns, as well as to detect and block spam by numerous statistical models and data processing.

4. Analysis of Network Traffic

Deep learning RNAs show promising results in the analysis of HTTPS network traffic to look for malicious activities. This is very useful for dealing with many cyber threats such as SQL injections and DOS attacks.

Deep learning neural networks show the surprising result in the analysis of the HTTPS network. It can look for malicious activities on the protocol and blocks attackers from accessing the website. This is a very convenient way of dealing with many cyberattacks such as SQL, DOS or DDOS attacks.

5. Monitoring User Behavior

Monitoring user activities and behaviors are important for any organization. Recognizing traditional user activities against networks is much more complicated and challenging. Sometimes it avoids security measures — not resulting in flags or alerts.

As an example, while having internal threats and users use their legitimate access with suspicious behaviors, they aren’t penetrating the system from the outside. This causes many security tools and systems useless against such type of attacks.

“User and Entity Behavior Analytics” — a tool based on deep learning known as “UEBA”, is great for preventing such attacks. After a period of learning the system, it can detect normal patterns of employee activities and suspicious behaviors as well. For example, UEBA can detect system access at strange times, which may direct an internal attack — depending on the learning.

The Future of Artificial Intelligence

The question that can be asked is what would happen if these systems were aware of their mistakes? And even more, if they knew they made a mistake and were able to correct it?

Are you thinking that “What would happen if AI systems can learn from its mistakes? Are they able to correct themselves from learning the previous conditions and situations?” The answer is here, YES — AI can learn from its previous lessons and already it’s a branch of machine learning (ML).

A branch of ML (Machine Learning) is intended for this kind of works, in which machines can deal with ambiguity. For example, Google and Uber are already working on general deep learning frameworks — which is known as “Deep Learning” to make AI programs that calculate the situation and can make decisions. Those Artificial Intelligent programs know when they should doubt themselves and be less likely to fail.

Though Artificial Intelligence (AI) is still in an early phase of its development, it advances our life through various fields of communication and information technologies. While it makes our information easy to manage, it also helps us to protect them against cyberattacks.

Deep learning, a sub-branch of Machine learning helps to advance security measures. Deep learning systems automatically analyze and learns a system and can detect malicious and suspicious activities on the system. Also, it can be used to track the network traffic and requests on HTTPS protocol to server a secure internet protocol for users. UEBA is a great step up in cybersecurity. While traditional security systems fail to monitor users’ behaviors and activities, UEBA can detect and take a decision based on its learning — which is much more efficient and accurate.