Machine learning extracts data using a predictive model. You can train devices to do specific tasks without supervision. This includes addressing attacks it detects to mitigate the risks. You can also use machine learning to standardise behaviour and analyse this to identify threats. It will automatically apply updates or patches to prevent attacks.
Because machine learning is complex and confidential, it is difficult for hackers to outthink this type of system. It can be simpler to target the process instead. These systems often use data from potentially unsafe sources. This includes social media, web traffic, consumer feedback and crowd sourcing data from websites. This makes the system vulnerable to start with. For hackers to compromise a machine learning system they need to introduce their own instructions to train or configure the system not to update devices or apply patches. This leaves the system vulnerable to attack. Adversaries can also teach machine learning systems to ignore particular types of behaviours and applications, and instruct them to ignore certain types of traffic to avoid detection. This means a hacker can get into the system through the back door.
There are three main types of attacks on machine learning hackers use:
- Data corruption. Hackers use data corruption attacks by introducing adverse data into the system. The most common type of attack is distorting datasets to change the system to classify good data as bad, and vice versa. You can also corrupt data by weaponizing feedback which manipulates the system into incorrectly classifying good content as offensive.
- Model stealing techniques. Cybercriminals use model stealing techniques to duplicate machine learning models or to access valuable data used to train the system. These attacks are serious as they target intellectual property such as medical data. Hackers use two main methods:
- Leaking sensitive information. Hackers build a similar model to find out what you used to train your system. When they cannot simulate the model, they can leak sensitive information.
- Recreate the system model. A hacker will recreate your machine learning model by probing the public API to refine their model to use against AI algorithms.
- Introducing adversarial inputs. Introducing adversarial inputs into a machine learning system allows hackers to instruct the system to misclassify these to avoid detection. These can take the form of emails that evade spam filters and malicious documents to avoid antivirus detection.
Addressing the challenges
Organisations need new strategies to address the challenges of adversarial attacks on their machine learning systems. It is important to protect and monitor your machine learning processes and resources. Organisations will have to abandon traditional forms of security to use new tactics. This will force hackers to redesign their methods which could cost too much money and time. Good systems security makes hackers abandon the target for one that is easier to attack.
Rightsize Technology are specialists in cybersecurity. Talk to us about protecting your business from hacker attacks.
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