Intro
Adversarial AI is the elephant in the room that no one is talking about, but should be. It is the use of the same technology that promises to revolutionize our lives, against us. It’s AI’s dark side, where machine learning algorithms are manipulated to make wrong decisions or take wrong actions.
It’s the use of AI to bypass security measures, like passwords and captchas, and even mimic human behavior. It’s the use of deepfakes to impersonate individuals and spread false information. It’s the future of cybercrime.
A 2020 global survey on artificial intelligence found that 62 percent of respondents who adopt AI say cybersecurity is their greatest concern. Only 39 percent are prepared to deal with cybersecurity vulnerabilities related to AI.
But what can we do about it? Cybersecurity experts must be proactive in understanding the risks and taking steps to mitigate them. It’s time to embrace AI-based security solutions to detect and respond to Adversarial AI attacks.
What is Adversarial AI?
Adversarial AI is the use of artificial intelligence and machine learning in cyber attacks. Adversarial AI can refer to the use of AI against cybersecurity efforts, or malicious attacks against AI systems.
Artificial Intelligence and the Cybercrime Landscape
Adversarial Machine Learning – where AI’s decisions have real-world consequences.
- Picture an innocent cardboard box mistaken for an explosive device
- Imagine a friendly drone identifying a group of hikers as enemy combatants
- Envision a harmless bird being misinterpreted as a hostile missile.
In each of these seniors, the stakes are high and one wrong move could lead to disastrous results. This is the reality of the rising field of Adversarial Machine Learning.
New deep-learning techniques have made it possible to analyze images and data from various sensors. However, machine learning techniques were not designed to compete with intelligent opponents.
Unfortunately, a small disruption of the input data is enough to compromise the accuracy of machine learning algorithms and render them vulnerable to manipulation by adversaries.
Here are 6 ways that Adversarial AI is being used:
- Passwords and CAPTCHAs
Cybercriminals are leveraging Adversarial AI to bypass traditional authentication methods, like passwords and captchas. These AI-powered attacks can mimic human behavior and have the potential to evade even the strongest security measures.
2. Deepfakes
Deepfakes are here. Yay… As the technology for deepfakes becomes more advanced and accessible, cybercriminals will scale up their use to impersonate individuals to steal personal information or spread false information
3. Malware Hiding
Adversarial AI is also being used to conceal malware from detection. This is making it harder for traditional security methods to identify and remove malware from systems, leaving organizations vulnerable to cyberattacks.
4. Improved social engineering
AI-powered tools are being used by cybercriminals to improve their social engineering tactics and trick even the savviest users into falling for phishing scams.
5. Poisoning Attacks
Poison by any other name would be just as deadly. Poisoning attacks—in which adversaries manipulate input data to cause AI systems to draw incorrect conclusions—can undermine the decisions and actions of an AI system. It only takes a small amount of data to make substantial changes to an AI system.
6. Evasion Attacks
Criminals can use Evasion Attacks like camouflage to slip past detection. These attacks often alter the appearance of spam emails and disguise malware code.
Conclusion
To handle these threats, cybersecurity experts must stay current on the latest adversarial AI techniques and use robust security measures, such as using many layers of defense and monitoring and testing systems for vulnerabilities. Additionally, organizations should also invest in AI-based security solutions to detect and respond to these types of attacks.
