How to Prepare for Machine Learning Security Risks

Is Machine Learning Secure?

Several applications for machine learning (ML) have the potential to increase security. But machine learning comes with security dangers, just like any other technology. Several important factors about machine learning security are listed below:

Data Security: Machine learning algorithms need a lot of data to be effective. Sensitive information, such as financial or personal information, is frequently included in this data. Machine learning systems must be created with data privacy and security in mind to guarantee that this data is secure. Access restrictions and encryption are used for both data in transit and at rest.

Model security: Attacks that might jeopardize the accuracy and integrity of machine learning models are possible. Using malicious data points or manipulating training data, adversarial assaults might provide unreliable findings. Additionally, model poisoning assaults can potentially inject undetectable, tiny defects into the model. Machine learning models must be created with security in mind, incorporating precautions like model validation and testing and ongoing model performance monitoring.

Deployment Security: After a machine learning model has been developed, it must be used in a real-world setting. To avoid unwanted access and ensure that the model is not compromised in transit or during deployment, this deployment procedure must be developed with security in mind. This entails access restrictions, secure communication techniques, and ongoing surveillance.

Human Factors: Humans ultimately develop and maintain machine learning systems, and they have the potential to create vulnerabilities through mistakes or malevolent behavior. Organizations must ensure that staff members are adequately trained in machine learning security best practices and that access to sensitive data and models is strictly regulated to reduce this risk.

Machine learning systems frequently rely on external data sources or algorithms, which poses a third-party risk. If these third-party components are not adequately verified or their security is breached, they may pose security problems. Organizations must guarantee third-party components' security and implement adequate controls to mitigate third-party risk.

Machine learning may be made secure when security is considered at all design phases, from data collection to model deployment. Data encryption, model validation, secure communication protocols, staff training, and third-party risk management are a few methods that fall under this category. Machine learning may be an effective technique for enhancing security in many applications when these safeguards are in place.

Threats to Machine Learning Systems

Machine learning systems are vulnerable to a variety of dangers. These include model theft, system hijacking, data poisoning, and evasion attacks. 

When a malevolent actor adds inaccurate or misleading data to the machine learning system's training set, this is known as "data poisoning." 

When an attacker tries to trick the system by giving it input data intended to make it anticipate something incorrectly, that assault is known as an evasion attack. When an attacker seizes control of the machine learning system and manipulates it to create false predictions, this is known as system hijacking.

Finally, model stealing occurs when an attacker takes the machine learning system's trained model for their use or resells it on the black market.

Types of Machine Learning Security Risks

Since machine learning uses data, this accounts for a substantial part of the security risks. However, dozens of risks are associated with machine learning that can potentially threaten systems and reduce positive outcomes in machine learning models. 

By educating yourself on the types of risks, you can take the first step in learning how to best protect systems from outside threats. For those looking to pursue a career in machine learning, it’s important to understand machine learning security risks to prepare your own capabilities and further your knowledge. Some of the more common types of risks attributed to machine learning are described below:

Data Privacy

Data privacy attacks are incredibly common, where sensitive and private business, employee, client, or customer data is stolen. Think about eBay in 2014, where 145 million users were compromised, or LinkedIn in 2012 and 2016 when 165 million email and passwords were affected.  

Data Poisoning

Data poisoning attacks change training data that affect the parameters of machine learning models. Bad data is inserted into your model causing it to learn something new that was not originally intended. 

Transfer Learning Attack

In this scenario, trained models are at risk and potential attacks are launched that trick your machine learning models and alter their behavior.  

Online System Manipulation

Systems connected online can be exploited, especially in a world where information is shared between users while machine learning models are trained.

How Can You Prepare for Risks?

Machine learning systems need to be secured before attacks occur rather than combating attacks after the fact. The process of engineering secure systems is integral to machine learning development, and someone with an interest in machine learning as a career must prepare for machine learning security risks with the right knowledge and education. Below are some of the processes for creating secure systems at the design level:

• Architectural risk analysis is a process that helps to create a system that can understand the types of risks involved, so that machine learning engineers are better prepared to combat those risks or avoid them altogether.
• Adversarial training helps to train your systems to recognize the potential threats mentioned above—such as poisoning attacks—so that your system understands what those threats look like to stop attacks before they start.   
• Anomaly detection is used, for example, in data poisoning so that when something malicious is inserted into your training data, you can detect it. An attacker might create poisoning points called “inliers” which are very similar to your data distribution model. To go deeper, you can use micromodels to mark safe or suspicious training instances. 
• Documenting and tracking how people are working is especially useful with online system attacks. Having information about who is working, their intended purpose, and at what times the algorithm is being used are recorded. 
• System verification should be implemented at all times so that everyone working within the system can check the information and make sure that it is verified, or check for weaknesses within the system that can potentially be exploited.  

Machine Learning and Cybersecurity

Cybersecurity and machine learning are two of the most vital areas of technology today. The two, taken together, can offer strong defenses against various cyber threats. Artificial intelligence's area of machine learning can offer a method for automatically detecting and responding to cyber threats. By utilizing massive datasets and algorithms, machine learning can identify minor trends and abnormalities in data that may signify malicious conduct.

Protecting networks, systems, and data against nefarious actors and security threats is the profession of cybersecurity. It is essential to every organization's security posture and can fend against various dangers and catastrophes. Security teams must be able to watch for dangers to their networks and systems and react swiftly and efficiently when they spot criminal behavior.

Cybersecurity and machine learning may work together to detect risks to the network and take action. Machine learning can identify minor trends and abnormalities in data that may suggest malicious conduct by utilizing vast datasets and potent algorithms. The security personnel may then be informed using this information and take the necessary action. Machine learning may also recognize and react to known risks and weaknesses, such as zero-day attacks.

Numerous procedures and jobs that would ordinarily need manual labor, such patching and upgrading systems, conducting vulnerability assessments, and responding to crises, may be automated using machine learning. These procedures can be automated to save time and money and to enable rapid and effective threat response.

Finally, machine learning may enhance an organization's general security posture. Machine learning may use historical data to identify trends and abnormalities that can point to malicious behavior, alerting security personnel to further investigate. Machine learning may also identify and respond to new risks, such as zero-day attacks or new malware.

Overall, cybersecurity and machine learning offers an effective means of identifying and countering online threats. Machine learning can identify minor trends and abnormalities in data that may suggest malicious conduct by utilizing vast datasets and potent algorithms. Machine learning may also automate procedures and jobs and strengthen an organization's security posture.

Further Your Machine Learning Career and Education

Machine learning is as valuable as the accuracy of your algorithms. Understanding and mitigating the risk associated with machine learning development will help you maintain secure systems that increase the reliability of successful outcomes.

Simplilearn is a certified online bootcamp provider that can help you learn more about improving the security of your systems and machine learning outcomes. Check out the Basics of Machine Learning for an introduction. For learners who want to dive deeper into machine learning, check out the Post Graduate Program in AI and Machine Learning, offered in partnership with Purdue University and collaboration with IBM.