Technology often means that our lives are more convenient and safer. But at the same time, these advances have revealed more sophisticated ways for cybercriminals to attack us and compromise our security systems.
Artificial intelligence (AI) is available to both cybersecurity professionals and cybercriminals. Similarly, machine learning (ML) systems can be used for both good and bad. This lack of moral compass makes adversarial attacks in ML increasingly difficult. So what exactly are adversarial attacks? What are their goals? And how can we protect against them?
What is an adversarial attack in machine learning?
Adversarial ML or adversarial attacks are cyberattacks aimed at tricking ML models with malicious inputs, leading to poor accuracy and poor performance. So, despite its name, adversarial ML is not a form of machine learning, but a variety of techniques cybercriminals (aka adversaries) use to target ML systems.
The primary goal of such attacks is usually to trick models into distributing sensitive information, failing to detect fraud, producing false predictions, or corrupting analytics-based reports. is. There are several types of adversarial attacks, but they often target deep learning-based spam detection.
You may have heard of Adversary-in-the-middle attacks. This is a new and more effective sophisticated phishing technique that involves stealing identity and session cookies, as well as bypassing multi-factor authentication (MFA) methods. Fortunately, these can be countered using phishing-resistant MFA technology.
types of hostile attacks
The easiest way to classify types of adversarial attacks is to divide them into two main categories.targeted attack and non-targeted attackAs suggested, targeted attacks have a specific target (such as a specific person), whereas untargeted attacks have no specific person in mind. That means you can target just about anyone. Naturally, non-targeted attacks take less time than targeted attacks, but they also have a lower success rate.
These two types can be further classified as follows: white box and Black box Color suggests knowledge or lack of knowledge of the targeted ML model. Before we delve into white-box and black-box attacks, let’s take a quick look at the most common types of adversarial attacks.
- Avoidance: Evasion attacks, primarily used in malware scenarios, attempt to evade detection by hiding the content of malware-infected or spam emails. Using trial-and-error techniques, attackers manipulate data at deployment time to subvert the confidentiality of ML models. Biometric spoofing is one of the most common examples of evasion attacks.
- data poisoning: Also known as a pollution attack, the aim is to manipulate the ML model during the training or deployment period to reduce its accuracy and performance. By introducing malicious input, attackers can disrupt models and make it difficult for security professionals to detect the types of sample data that corrupt ML models.
- Byzantine fault: This type of attack causes a Byzantine failure in a system that requires consensus among all nodes, resulting in loss of system services. If one of the trusted nodes becomes rogue, it can launch a denial of service (DoS) attack that shuts down the system and prevents other nodes from communicating.
- model extraction: In an extraction attack, an attacker examines a black-box ML system to extract training data or, in the worst case, the model itself. An attacker can then have a copy of her ML model in hand to test the malware against antimalware/antivirus and figure out how to circumvent it.
- inference attack: Similar to the extraction attack, the goal here is to leak information about the training data to the ML model. However, attackers can exploit system vulnerabilities and biases by trying to identify the datasets used to train the system.
White box vs black box vs gray box adversarial attacks
What sets these three types of adversarial attacks apart is the amount of knowledge the attackers have about the inner workings of the ML system they plan to attack. The white-box method requires complete information about the ML model of interest (including its architecture and parameters), whereas the black-box method does not require any information, only observing its output.
The greybox model, on the other hand, falls somewhere between these two extremes. It gives an attacker information about the data set and other details about the ML model, but not everything.
How can machine learning be defended against adversarial attacks?
Humans are still a key factor in enhancing cybersecurity, but AI and ML have learned how to detect and prevent malicious attacks. Increase accuracy in detecting malicious threats, monitoring user activity, identifying suspicious content, and more. But can they push back adversarial attacks and protect ML models?
One way to combat cyberattacks is to train ML systems to recognize adversarial attacks in advance by adding examples to the training procedure.
Unlike this brute-force approach, defensive distillation uses a more efficient first-order model to capture key features of a less efficient second-order model and improve the accuracy of the second-order model with the first-order model. I suggest ML models trained with defensive distillation are less susceptible to adversarial samples and thus less likely to be exploited.
You can also always change the algorithm that the ML model uses for data classification. This can reduce the success rate of adversarial attacks.
Another notable technique is feature refinement, which reduces the search space available to attackers by “thinning out” unnecessary input features. The goal here is to minimize false positives and make detection of hostile examples more effective.
Securing machine learning and artificial intelligence
Adversarial attacks show that many ML models can be shattered in surprising ways. After all, adversarial machine learning is still a new research area in cybersecurity, with many complex problems for AI and ML.
There is no magic solution to protect these models from all adversarial attacks, but the future will bring more advanced technology and smarter strategies to deal with this formidable adversary. there is a possibility.
