AI in Cybersecurity
Not only has the number of cyberattacks increased significantly over the last few decades, but they have also become more sophisticated. As a result, developing a cyber-resilient strategy is critical. Traditional security methods are insufficient to prevent data breaches in the event of a cyberattack. Cybercriminals have learned how to hack, attack, and breach data using new techniques and powerful tools. Fortunately, AI technologies have been introduced into cyberspace to build smart models for defending systems from attacks.
AI technologies can be used as fundamental tools in the field of cybersecurity because they can rapidly evolve to address complex situations. AI-based techniques have the potential to provide efficient and powerful cyber defense tools for detecting malware attacks, network intrusions, phishing and spam emails, and data breaches.
What is AI and How does it work
Artificial intelligence is a technology that enables machines and computer applications to mimic human intellect by learning from experience through iterative processing and algorithmic training.
AI systems learn from patterns and features in the data that they study by combining massive amounts of data with sophisticated, iterative processing algorithms. Each time an AI system runs a round of data processing, it tests and measures its own performance and develops additional expertise.
Because AI never requires a break, it can run through hundreds, thousands, or even millions of jobs extraordinarily fast, learning a significant lot in a short period of time and becoming extremely proficient at whatever it’s being trained to do.
Taditional Security Approaches and their disadvantages
Traditional cybersecurity methods rely on the static control of security devices and work in response to an attack. For instance, the majority of intrusion detection systems currently in use completely rely on a predefined set of Rules or patterns to monitor, detect, and provide alerts for intrusions. However, there is a significant disadvantage to that strategy. For instance, one disadvantage is that attackers can get around static rules or patterns by using different attack obfuscations techniques.
Many of these IDS and IPS, Firewalls, and WAFS use regex-based rules or patterns to detect, prevent, block, and alert security incidents. In fact, Using Regex based Security measures plays a critical role in the development and maintenance of security posture across multiple layers of a corporation’s infrastructure. For example, Network Security Engineers use regex to fine-tune how firewalls behave, Developers use regex to do input validation, System administrators can use regex rules to detect potentially dangerous content in files and to quarantine these files accordingly.
Simply because we are relying entirely on a simple static regex to detect and alert the intrusions/attacks rather than using any dynamic technology to actively analyse the attack’s behaviour and then take active decisions to block and prevent it. It is again subjected to numerous bypasses, and the attacker can simply get around static regular Expressions or patterns by employing various attack obfuscation techniques.
AI in Cybersecurity
AI, as an example, adopts intelligence and can perform real-time analysis and decision making while processing enormous amounts of data to solve problems. AI can analyze large amount of data efficiently, accurately, and in short time. Using threats history, an AI-based system can know about the past threats and use this knowledge to predict similar attacks in the future, even if their patterns change.
Furthermore, because AI does not rely on static resources to detect, alert, and block the intrusion, but rather it has a dynamic analysing and decision making capabilities, it can discover new and significant changes in the attack, thus protecting the organization even if the attacker has sophisticated and obfesticated the attack.
Where does AI fits in Cyber Security.
AI and machine learning (ML) have become critical technologies in information security because they can rapidly analyze millions of events and identify a wide range of threats, from malware analysis, detecting zero-day vulnerabilities to identifying risky behavior that could lead to a phishing attack or malicious code download. Artificial intelligence can be tweaked in any aspect of cyber security. Here are a few examples:
Phishing and spam detection
Spam detection is difficult. The distinction between spam and non-spam messages is hazy, and the criteria change over time. Machine learning has proven to be the most effective and preferred approach by email providers among various efforts to automate spam detection. While as In the case of spam detection using AI, a trained machine learning model will be able to determine whether the sequence of words found in an email are closer to those found in spam emails or safe ones. and Hence can take active decision on spam filtration of emails.
AI methods can detect threats and prevent attacks before they occur. This is typically accomplished by developing a model for analyzing large datasets of cybersecurity events and identifying patterns of malicious behavior. The model is typically composed of previously collected data and recorded Indicators of Compromise (IOC), which are used to monitor, identify, and respond to threats in real time. As a result, if similar activities are detected, the models automatically recognize them.
DOS and DDOS Attacks
DDoS attacks have been a major threat to the Internet and can cause significant financial loss to businesses and governments. With the advancement of emerging technologies such as cloud computing, the Internet of Things, and artificial intelligence techniques, attackers can launch a large volume of DDoS attacks at a lower cost, making DDoS attacks much more difficult to detect and prevent. DDoS traffic is similar to regular traffic and hence makes it really diffcult for traditional security approaches to detect and block it. But with the help of AI intelligence techniques and Algorithms, such as Naive Bayes and Random forest tree, We can classify DDoS attack traffic from the normal traffic hence block the attack.
Attack Surface Detection and Network risk scoring
obtaining a complete, accurate inventory of all devices, users, and applications exposed to the internet, and then performing risk calculations based on historical cybersecurity datasets to determine which areas of networks are more vulnerable or involved in specific types of attacks
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