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For example, in spam detection, we have deployed classifying systems to detect and stop spam, but then attackers learned how to fool the protection system by changing critical words (instead of Viagra, they use to make the antispam system think that a message is legitimate. This entails studying attacks but also defenses against attacks. Its aim is to make machine learning systems robust against malicious attacks. To avoid adversarial attacks, a new field called ‘adversarial machine learning’ is emerging. These security issues question our standard algorithm design methods, given the presence of adaptive adversaries ready to intervene in the problem to modify the data on which we rely. Quite importantly, attackers quickly adapt to the defense machine learning systems in place, and this could have dramatic implications in domains such as automated driving systems, defense systems, law enforcement and health to name a few.
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Fraudsters could also disguise illegitimate insurance claims, fooling the corresponding algorithm to receive compensation. In real life, a worrying equivalent is that an autonomous car can be fooled into reading a stop sign as speed limit, and therefore not stop at the sign. To achieve this, attackers simply needed to interfere during the machine learning process, presenting data that is falsely labelled - here, passing gibbons for pandas during the machine training phase. The attack led the machine to recognize a panda with high confidence when the picture was in reality, replaced by a picture of a gibbon.
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The first instance of this type of attack targeted a machine trained to recognize panda pictures. These attacks use data crafted precisely to fool AI. Indeed, while state-of-the-art machine learning algorithms perform extraordinarily well on standard data, they are vulnerable to so-called ‘adversarial attacks’. To ensure that AI applications are secure, machine learning algorithms need to be robust and reliable. However, while the list of AI applications requiring strict security is endless (automated driving, content filters, policing and so on), AI is not immune to cyber-attacks itself. New approaches, such as adversarial risk analysis, facilitate online decisions and enhance accuracy and speed in cyber risk management. In all these cases, AI supports cyber security decision making in the presence of adversaries. This goes further than scanning, as ascertaining the nature of the tweets for example relies on advanced AI tools, such as language and sentiment analysis. Threat intelligence systems can also analyze web and social network content, looking for negative online mentions of a company, which constitute a reputational threat but could also trigger cyber-attacks. However, the entailed data deluge needs to be coherently aggregated to provide meaningful and useful risk indicators, and a combination of machine learning and economic models aid in performing such an aggregation. In addition to mere scanning systems, some threat intelligence systems perform in-depth analysis of the security environment and posture within an organization. Predictive models can also forecast imminent failures, and AI then offers precious time to react in advance. For example, automated systems can check the status of hundreds of thousands of connected devices and send warning signals to engineers when a device behaves abnormally, signalling a potential intrusion. Are smart connected machines going to make our world more secure or, to the contrary, less so?ĪI is now used by cyber security companies and governments to track down unknown vulnerabilities in their information systems and fix them before attackers exploit them. They gather and transmit data and learn on the go, powered by artificial intelligence in a globally connected world. In a not-so-distant future, they might routinely transport us from home to work in a driverless manner. Cars can now help you plan your itinerary and help you park, sensing the trees, pavements and surrounding vehicles and activating the brakes as needed. Machines are getting increasingly smarter.