The benefits and increasing use cases of artificial intelligence (AI) are increasing capabilities in each field of modern technology. Almost every sphere of novel computing now relies on artificial intelligence. Specifically, AI aims to design solutions to perform tasks that currently require intelligence and brainpower when performed by humans.
Clients and servers continuously interact with each other through any digital network. It is essential to ensure security in the network. Because if any unauthorized access occurs in the network, bad actors exploiting the vulnerability could spell disaster. For this purpose, several security algorithms are available which perform data encryption and decryption and protect data from unauthorized access. These algorithms result in higher complexity for an extensive network and are hard to maintain.
However, AI can help tremendously in this manner. Any cyber situational-awareness model requires task automation and proper intelligence. While challenging to manage and implement manually, cyber threat detection, vulnerability checking, and knowledge representation can now be easily automated through artificial intelligence. In a recent press release, Capgemini found that 69% of organizations believe they will not be able to respond to cybersecurity threats without AI.
ITAdOn believes this is absolutely true. When a new threat emerges, it can be challenging to detect and perform effective actions against it. But unsupervised or reinforcement-type AI algorithms can detect and fix them remarkably quickly.
Some classical types of threats that may be found in a cyber system are:
- Eavesdropping: Obtaining copies of messages without authority.
- Masquerading: Sending/receiving messages using the identity of another principal without their authority.
- Message tampering: Intercepting and altering messages.
- Replaying: intercepting, storing, and replaying messages.
- Denial of Service (DoS): Flooding a channel with requests to deny access to others.
Specialized AI models detect and take action against threats like these. A supervised AI model needs to be taught about the threats and prevention techniques before taking effective action, but unsupervised and reinforcement models do not require such preemptive teaching.
In an unsupervised model, the model is fed data, extracts important features and patterns from the data, and returns outputs. No external supervision is required there. Especially most of the classification and neural network models fall into this category.
In the case of a reinforcement model, an agent program tries different paths from a predefined initial state to a final state and assigns rewards for agents to each of the paths accordingly.
Figure 1 shows a classical AI-based networking system where the model is composed of three main parts, namely the client, the medium with AI-based security (labeled below as “Network”), and the server.
Figure 1. A classical AI-based Intrusion Detection & Prevention (IDP) tool
The iterative approaches are very complex for a large cyber system to detect and fix the threats. It is also time-consuming to encrypt and decrypt the data according to an iterative process. Iterative processes have the following drawbacks:
- More complex
- Should be up-to-date with the time
- Skilled simulations required to handle them
AI has the solution for all of the above. AI can automatically classify the threat type and immediately procedurally develop the resolution technique for the specific threat. The AI-based cybersecurity model has two principal parts: threat detection and defensive actions.
The artificial intelligence algorithm first attempts to detect the threat type by using classical machine learning models. It is a classification-type task, and after proper classification, the model attempts to threat prevention. The most used classification models for threat detection are SVM (Support Vector Machine), Decision Tree, Random Forest, and Naïve Bayes. Sometimes, hybrid models may be used to detect the threat. For example, the SVM with KNN (K Nearest Neighbors) is a hybrid model to detect intrusion in a cyber system network.
Some models are supervised and need external supervision to train them, but the unsupervised and reinforcement algorithms need no external supervision.
The second part and core part of the AI threat fixing model. In this phase, the detected threats or intrusions get fixed. This step involves various deep-learning procedures. These deep learning models can verify the threats and intrusions and also take necessary actions against them. This is a complex procedure, and data are analyzed exhaustively in this phase because even a tiny intrusion can lead to harmful consequences. The most widely used model for this phase is the Deep Belief Network (DBN), a probability generation model consisting of multiple restricted Boltzmann layers. They are chiefly used for malware detection in Android applications. A typical DBN model is shown in Figure 2, with the DBN model divided into several layers, namely the classification layer, restricted Boltzmann layers (labeled below as hidden layers “n” and “1”), and an unlabeled data layer.
Figure 2. Deep Belief Network
In addition to the DBN model, nowadays, the RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) are used to take defensive actions against a threat or attack. As with the DBN model, the deep learning model can combine with various supervised learning models in this phase.
Advantages of AI in Cybersecurity:
The advantages of AI in cybersecurity are much more than a typical manual process. We will see some of them based on the following criteria:
- Secured Novel Technologies: Various novel computing and networking technologies, such as cloud computing, big data analysis, Software-Defined Networking (SDN), etc., are required for advanced security systems. They work on vast amounts of data, and the security model should be as efficient as possible. When iterative approaches fail, AI succeeds.
- Reliability: AI-based cybersecurity models are significantly more reliable than classical role-based models.
- Consistency: Process large amounts of data with pinpoint accuracy through AI, and commit changes with absolute ease.
- Imperative Cybersecurity: Most data analysts can become overwhelmed by simply interpreting large amounts of data, and intrusion detection/prevention can be even more demanding. AI achieves where human intelligence just is not enough – even as new data floods in.
Drawbacks of AI in Cybersecurity:
AI models have at least one minor drawback in a cybersecurity system. Initial implementation can be difficult. Expert knowledge of artificial intelligence programming is crucial. Reinforcement learning models, too, require high levels of proficiency and knowledge about agents and their designations.
Please do not hesitate to contact ITAdOn for cybersecurity consultant for any cyber security related inquiries.