For years, attackers have manually collected information about their target to send them scams (e.g., malicious links) by email or social media messages using techniques like spoofing and social engineering. Phishing attacks are a traditional but efficient way to compromise a network.
Hackers can now generate accurate password lists automatically and even customize them according to a specific set of data (e.g., the targeted user’s info), significantly increasing the chances of success.ĭefenders can no longer fight attacks with classic defenses, and it’s especially true with phishing campaigns. ML automates brute force attacksĪnother black hat use of ML is for brute force attacks. Even if developers have been trying to make CAPTCHA ever more challenging to recognize and crack, it’s now a losing game for defenders, even for big platforms such as Amazon. However, models are now remarkably efficient in solving such operations.
CAPTCHA is no longer complex enoughĬAPTCHA, for example, are quick challenges that invite users to solve elementary math operations (or copy/paste a series of random letters and numbers), which is supposed to be trivial for humans but extremely difficult for robots. Most security specialists agree that more and more cybercriminals use ML to generate sophisticated attacks, evade detection, and bypass classic defenses. The threat landscape is constantly evolving. Logs analysis to find trends and patterns.DDoS (distributed denial of service) mitigation.More pragmatically, security teams can use ML to have a proactive defense for various threats such as: Such tools can be particularly helpful for defending against zero-day, or previously unknown, threats. UEBA tools look for new or unexpected network activity for detecting threats.
It’s not uncommon for security information and event management (SIEM) to include ML modules to detect network vulnerabilities and respond automatically.īehavior analytics, often called UEBA for user and entity behavior analytics, is one of the more promising security applications of machine learning. Even some of the top consumer antivirus tools have begun to add machine learning-based detection.
Increasingly, popular tools such as Microsoft’s Windows Defender use this approach to identify and catch threats. Statistical analysis on large datasets allows the ability to predict a computer’s behavior and anticipate actions that haven’t even been programmed. In this approach, defenders can use existing data to detect fraud and malware. The most common ML security approach is the regression technique, also known as the prediction. The data go through several layers where hidden inputs and outputs execute predictive tasks and pass the result to the next layer, making the processing chain a complex structure, hence the word “deep.” Deep learning thus can be seen as a particular set of techniques of machine learning. When you train a model for image or speech recognition, it’s deep learning (DL), a subset of machine learning. Machine learning systems help make decisions based on collected data and self-adjust their model when detecting new patterns.
AI is the most generic term, including all other fields. Deep Learningįirst, some terminology, as there are many variations of computer learning. It’s the latest chapter in the ongoing cybersecurity arms race, with attackers and defenders locked in a never-ending struggle, each adapting to the other’s innovations to try to gain an edge.Īlso read: Best User and Entity Behavior Analytics (UEBA) Tools AI vs. It’s an excellent asset for cyber defense, but adversaries have learned to trick algorithms and even use similar technology to compromise targeted systems. Instead of repeating the same procedure, sometimes manually, the system can detect the attack, report and categorize the incident, and then apply the fix automatically.Įven better, security tools like behavioral analytics can spot attacks simply by noticing anomalous activity, important technology for catching zero-day threats and adversarial attacks. Indeed, it’s not uncommon to have similar incidents that generally require the same response. The technology also predicts potential attacks and automatically responds to threats by identifying specific trends and cycles. Machines can recognize patterns to detect malware and unusual activity better than humans and classic software. Machine learning (ML) and artificial intelligence (AI) have emerged as critical tools for dealing with the ever-growing volume and complexity of cybersecurity threats.