保护AI系统免受恶意数据中毒攻击 

As the use of artificial intelligence (AI) and machine learning (ML) continues to grow, businesses that utilize these technologies must also be aware of the attack methods cybercriminals use to target them. One such attack that hackers employ is data poisoning—when a malicious code is introduced into a dataset to compromise the performance of AI and ML systems.

安装完成后, the unwelcome software manipulates training data to induce errors or biases, 哪些会显著降低这些系统的可靠性. Data corruption created by data poisoning can lead to critical errors that affect the accuracy and efficacy of AI system outputs, so businesses must ensure they have mechanisms to address this vulnerability.

This article provides more information on data poisoning attacks and tips to defend against them.

数据中毒概述

通过在人工智能的训练阶段改变数据集, 黑客可以破坏系统输出的完整性, 导致错误, 意外结果或偏差. The attacks can also increase a system’s vulnerability to additional cybersecurity issues by creating an access point for future intrusions.

进行数据中毒有几种方法，例如:

• Intentionally inputting incorrect or misleading information in the training dataset
• 修改现有数据集
• 删除部分现有数据集

数据中毒攻击通常根据其结果进行分类. 这里有两种常见的分类:

• 1. 有针对性的攻击 are when a malicious actor aims to influence the model’s behavior in a specific situation. 有针对性的攻击 generally do not impact the AI model’s overall performance.
• 2. 不属预定目标的攻击 occur when a cyber adversary attempts to manipulate the dataset to degrade the overall performance of the AI, 从而对其预测或决策能力产生负面影响.

威胁行为者和动机

为了解决暴露问题, businesses must be aware of the different threats and the motivations behind these malicious actors. Examples of individuals or groups that may initiate data poisoning attacks include:

·   恶意的内部人员, including employees with access to data who may have grievances with an organization and seek revenge

·   外部黑客 whose purpose is to exploit vulnerabilities to disrupt operations for financial gain

·   民族国家 that seek to engage in cyberwarfare to undermine the technological advantages of their adversaries

Other parties involved in data poisoning may do so due to ideological beliefs. 例如, activists who look to increase privacy from AI may turn to data poisoning tactics to demonstrate flaws and vulnerabilities in AI to accomplish their objectives. Others may engage in these attacks to gain notoriety or to prove their capabilities. 无论他们的动机是什么，企业都需要意识到 这些潜在的渗透，并采取措施减轻其风险.

数据投毒攻击的例子

Malicious actors are discovering new ways to leverage data poisoning attacks. 策略包括:

• 垃圾邮件过滤器故障-黑客可以毒害人工智能的数据集, allowing spam emails to bypass filters and impact large numbers of employees and create vulnerabilities to other cyberattacks (e.g.、网络钓鱼诈骗).
• 网络流量分类错误—A threat actor can poison a learning model’s dataset to incorrectly label network traffic (e.g.(网页浏览和视频流)，导致网络性能不佳.
• 网络安全退化入侵检测系统的数据集可能会中毒, 导致威胁未被发现或误报.
• 聊天机器人操作—AI tools such as chatbots can be fed with poisoned datasets to produce inaccurate, 敌对的或攻击性的反应.
• 健康和安全剥削—Data poisoning attacks aimed at creating errors in autonomous driving systems or AI medical diagnostic tools can lead to significant injuries or fatalities.

企业预防方法

鉴于数据中毒攻击的深远影响, businesses should consider these strategies to mitigate their exposure to them:

• 数据验证和清理—Businesses should filter out potential attacks by removing data anomalies and investigating suspicious patterns. They should also verify the validity of the data sources used for training.
• 安全数据处理—Utilizing encryption, access controls and secure protocols can add protective layers for datasets.
• 监察及审核—Businesses should implement systems to detect vulnerabilities and data irregularities. This enables organizations to identify potential troublesome areas and address them before they create larger issues.
• 数据来源多样化—Training models on varied data sources can reduce attack risks by preventing targeted data manipulation.
• 强大的训练技术-通过将对抗性训练纳入模型学习, 人工智能可以用来处理被篡改的数据.
• 数据来源—Maintaining clear records of data sources is beneficial when tracing to find potential points of compromise.
• 输出验证-快速检测问题, businesses should frequently compare model outputs against expected behavior.
• 全面的用户培训—Raising awareness through ongoing training and education can help users recognize suspicious activity or outputs related to data poisoning.
• 插入式测试—It’s important to test systems regularly to see where they are vulnerable. 这可以帮助企业主动解决薄弱环节.

结论

数据中毒攻击具有严重的风险. Businesses can reduce their exposure to these cybersecurity incidents by taking the time and initiative to implement prevention methods.

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网络风险 & Liabilities document is not intended to be exhaustive nor should any discussion or opinions be construed as legal advice. Readers should contact legal counsel or an insurance professional for appropriate advice. ©2024 Zywave, Inc. 版权所有.