Systems and Security : Attacks, Threats, and Vulnerabilities

1.2 Malware attacks

Adversarial artificial intelligence (AI)

1. Tainted training data for machine learning (ML)
2. Security of machine learning algorithms

1. Tainted Training Data for Machine Learning (ML): Adversarial AI refers to the use of AI to attack or manipulate machine learning models. Tainted training data is a type of attack in which the attacker intentionally provides incorrect or biased data to the machine learning model during the training phase. This can lead to the machine learning model making incorrect predictions or decisions, leading to security or privacy vulnerabilities.

2. Security of Machine Learning Algorithms: Another aspect of adversarial AI is the security of machine learning algorithms themselves. Attackers can try to manipulate the inputs to the algorithm to cause it to make incorrect predictions or decisions, or they can try to compromise the algorithm's training process to cause it to behave in unexpected ways. Additionally, machine learning algorithms can also be vulnerable to various types of attacks, such as evasion attacks, in which the attacker provides data specifically designed to evade detection, or poisoning attacks, in which the attacker intentionally introduces inaccuracies into the training data to manipulate the behavior of the algorithm.

To defend against adversarial AI, it is important to consider security and robustness during the development of machine learning algorithms, as well as during the selection and processing of training data. Techniques such as adversarial training and validation can also be used to improve the robustness of machine learning models to attack.

Supply-chain attacks

A supply-chain attack is a type of security breach that occurs when an attacker targets a weak link in a company's supply chain in order to compromise the security of the target organization. This can be achieved by compromising a third-party vendor, hardware component manufacturer, or software supplier, among others.

For example, an attacker could compromise the software of a component manufacturer and include malware in the software that is delivered to the target organization. The malware would then be installed along with the component, giving the attacker a way into the target's network.

Supply-chain attacks are particularly dangerous because they can bypass traditional security measures and go undetected for long periods of time. They can also be used to target high-value targets, such as government agencies and large corporations, which rely heavily on their suppliers.

To mitigate the risks associated with supply-chain attacks, organizations should implement thorough supply-chain risk management processes and perform due diligence on their suppliers and vendors to ensure that they have appropriate security measures in place. Additionally, organizations should regularly monitor and update their software and hardware components to detect and respond to any security breaches as soon as possible.