In layman terms, Artificial Intelligence is a combination of two words: Artificial and Intelligence. Intelligence can be defined as the ability to learn, understand, or deal with new or vexing situations. Hence, we can define AI as the artificial ability to learn, understand, and deal with different scenarios. In the computing world, AI is the machines’ ability to learn and apply intelligence similar to human intelligence at an exponential rate in terms of speed and scope. This is done through different algorithms. A good example is the application of machine learning in AI. Machine learning makes use of different algorithms to learn from the available (structured) data and apply the knowledge to improve the performance of the AI tasks. One such technique used by Machine learning is deep learning. Deep learning is the ability of the machines to learn from unsupervised or unstructured data. Deep learning is inspired by biological neural networks.
Types of Artificial Intelligence
Artificial Intelligence (AI) is generally divided into two broader categories.
1) Narrow Artificial Intelligence
Narrow Artificial Intelligence (Narrow AI) is the most common and widely used artificial intelligence concept in computing technology. Narrow AI is often termed as a simulation of human intelligence. However, the operations carried out in Narrow AI are strictly monitored i-e there is no self-awareness involved in Narrow AI. Narrow AI operates within a restricted (defined) boundary. Google’s Assistant, Apple’s Siri, and IBM’s Watson are a few examples of Narrow AI where the functions of these technologies are predefined by the developers.
2) Artificial General Intelligence
Artificial General Intelligence (AGI) is the future landmark of AI. AGI research goal is to find algorithms that can enable a full set of cognitive abilities in machines i-e machines should be able to think and act in any environment like a human brain.
Artificial Intelligence and Cybersecurity
In Cybersecurity, some tasks are carried out by humans while others are performed automatically by the software. In some cases, an automated approach is considered highly effective compared to human efforts. For instance, a human has to manually add an attack signature to the system’s database to stop similar attacks in the future. Although there is a concept of heuristic approach applied by the humans in Cybersecurity, the success ratio of recognizing zero-day attacks by humans is comparatively low. On the other hand, an automated approach is considered more lethal against incidents like zero-day attacks where the software can analyze the historical data to devise a future defense against zero-day attacks. The later approach opens the door for the researchers to integrate AI in Cybersecurity. Following is a brief overview of some scenarios where we can combine the AI technology and Cybersecurity to combat cyber-attacks.
Firewalls are probably the first line of defense for any network. Firewalls are programmed in a way that they filter the incoming and outgoing traffic on the bases of a defined set of rules. It means that Firewalls only act on a pre-defined set of protocols. As technology evolves, intruders find new ways to bypass Firewall filters. In these scenarios, AI-enabled Firewalls can quickly learn and re-define the rules to anticipate the intruders.
AI-Based Malware Detectors
Malware is malicious software that can harm computers. Ransomware, Trojans, worms, and viruses are a few examples of a devastating malware family. The detection of such malware is not an easy job. WannaCry (2017) is an example of a ransomware attack that remained active for four days, infecting more than 300,000 computers in many parts of the world. Many of these computers had the Malware detection security in place but they were not able to promptly detect and respond to the attack. Such incidents may happen again with great success because the current defense strategy is a code-based or signature-based approach. To successfully anticipate these types of attacks, we need a behavior-based analytical approach. A behavior-based analytical approach is possible through machine learning. Therefore, incorporation of AI in Malware detection systems can greatly improve the performance of malware detectors and improve the success ratio of defense.
AI-Based Insider Attacks Detectors
One of the major threats in cyberspace is the insider attacks. According to the 2019 Verizon Data Breach Investigations Report, 34% of total breaches occurred due to insider threat actors. This is probably due to the fact that insider actors have more information than the outside attackers. Database flow analysis, network activity analysis, and logs analysis are few ways to detect insider attacks. However, the success ratio of detecting insider threats is low mainly because insiders are well aware of the defense system being used by the organizations. Who knows! The person monitoring the security logs is part of the insider cyber-attack. Artificial Intelligence (AI) can play a great part in these circumstances. AI-enabled Cyber-defense systems can be trained to analyze the behavior of all insiders including those who are responsible for evaluating and handling cyber incidents. In this way, any suspicious activity, either by the employees or Cybersecurity staff can be efficiently detected and reported to the concerned authorities.
Honeypots are designed as a decoy to secure the actual data or assets of organizations. Many cyber-attackers gaining access to honeypots think that they have gained access to the target network. Honeypots are also used to understand the possible impact of the attacks if successfully carried out by cyber-criminals. Artificial Intelligence can improve the efficiency of these honeypots by analyzing the current cyber-attacks and predicting future attacks as well through techniques like deep learning.
AI-Based Data Encryption
Data encryption is one of the tedious jobs in Cybersecurity. An organization with a big data set requires a lot of human effort to classify data that should be encrypted and filter the data that should be shared or stored in plain text format. Machine learning techniques can be used to automatically detect and encrypt data that is meant to be stored or shared in an encrypted form.
AI-Based Data Analysis
Malicious payloads and encrypted has a lot of similarities. Hackers often use this similarity as an opportunity by embedding the payloads with the legitimate encrypted data. The decryption of such data often results in the execution of malicious code and security breach. AI algorithms can be used to differentiate between meaningful encrypted data and malicious codes to solve this problem.
It is true that AI failed on different occasions, depicting the fact that AI is still in the evolution phase. Despite the failures and drawbacks of AI technology, Cybersecurity is likely to adopt Artificial Intelligence As a Service (AIAS) to improve the overall performance of tools and automating certain Cybersecurity tasks.