Machine learning is being used by hackers to find weaknesses in chips and systems, but it also is starting to be used to prevent breaches by pinpointing hardware and software design flaws.

To make this work, machine learning (ML) must be trained to identify vulnerabilities, both in hardware and software. With proper training, ML can detect cyber threats and prevent them from accessing critical data. As ML encounters additional cyberattack scenarios, it can learn and adapt, helping to build a more sophisticated defense system that includes hardware, software, and how they interface with larger systems. It also can automate many cyber defense tasks with minimum human intervention, which saves time, effort, and money.

ML is capable of sifting through large volumes of data much faster than humans. Potentially, it can reduce or remove human errors, lower costs, and boost cyber defense capability and overall efficiency. It also can perform such tasks as connection authentication, system design, vulnerability detection, and most important, threat detection through pattern and behavioral analysis.

“AI/ML is finding many roles protecting and enhancing security for digital devices and services,” said David Maidment, senior director of market development at Arm. “However, it is also being used as a tool for increasingly sophisticated attacks by threat actors. AI/ML is essentially a tool tuned for very advanced pattern recognition across vast data sets. Examples of how AI/ML can enhance security include network-based monitoring to spot rogue behaviors at scale, code analysis to look for vulnerabilities on new and legacy software, and automating the deployment of software to keep devices up-to-date and secure.”

This means that while AI/ML can be used as a force for good, inevitably bad actors will use it to increase the sophistication and scale of attacks. “Building devices and services based on security best practices, having a hardware-protected root of trust (RoT), and an industry-wide methodology to standardize and measure security are all essential,” Maidment said. “The focus on security, including the rapid growth of AI/ML, is certainly driving industry and government discussions as we work on solutions to maximize AI/ML’s benefits and minimize any potential harmful impact.”

Zero trust is a fundamental requirement when it comes to cybersecurity. Before a user or device is allowed to connect to the network or server, requests have to be authenticated to make sure they are legitimate and authorized. ML will enhance the authentication process, including password management, phishing prevention, and malware detection.

Areas that bad actors look to exploit are software design vulnerabilities and weak points in systems and networks. Once hackers uncover these vulnerabilities, they can be used as a point of entrance to the network or systems. ML can detect these vulnerabilities and alert administrators.

Taking a proactive approach by doing threat detection is essential in cyber defense. ML pattern and behavioral analysis strengths support this strategy. When ML detects unusual behavior in data traffic flow or patterns, it sends an alert about abnormal behavior to the administrator. This is similar to the banking industry’s practice of watching for credit card use that does not follow an established pattern. A large purchase overseas on a credit card with a pattern of U.S. use only for moderate amounts would trigger an alert, for example.

As hackers become more sophisticated with new attack vectors, whether it is new ransomware or distributed denial of service (DDoS) attacks, ML will do a much better job than humans in detecting these unknown threats.

Limitations of ML in cybersecurity
While ML provides many benefits, its value depends on the data used to train it. The more that can be used to train the ML model, the better it is at detecting fraud and cyber threats. But acquiring this data raises overall cybersecurity system design expenses. The model also needs constant maintenance and tuning to sustain peak performance and meet the specific needs of users. And while ML can do many of the tasks, it still requires some human involvement, so it’s essential to understand both cybersecurity and how well ML functions.

While ML is effective in fending off many of the cyberattacks, it is not a panacea. “The specific type of artificial intelligence typically referenced in this context is machine learning (ML), which is the development of algorithms that can ingest large volumes of training data, then generalize and make meaningful observations and decisions based on novel data,” said Scott Register, vice president of security solutions at Keysight Technologies. “With the right algorithms and training, AI/ML can be used to pinpoint cyberattacks which might otherwise be difficult to detect.”

However, no one — at least in the commercial space — has delivered a product that can detect very subtle cyberattacks with complete accuracy. “The algorithms are getting better all the time, so it’s highly probable that we’ll soon have commercial products that can detect and respond to attacks,” Register said. “We must keep in mind, however, that attackers don’t sit still, and they’re well-funded and patient. They employ ‘offensive AI,’ which means they use the same types of techniques and algorithms to generate attacks which are unlikely to be detected.”

ML implementation considerations
For any ML implementation, a strong cyber defense system is essential, but there’s no such thing as a completely secure design. Instead, security is a dynamic and ongoing process that requires constant fine-tuning and improvement against ever-changing cyberattacks. Implementing ML requires a clear security roadmap, which should define requirements. It also requires implementing a good cybersecurity process, which secures individual hardware and software components, as well as some type of system testing.

“One of the things we advise is to start with threat modeling to identify a set of critical design assets to protect from an adversary under confidentiality or integrity,” said Jason Oberg, CTO at Cycuity. “From there, you can define a set of very succinct, secure requirements for the assets. All of this work is typically done at the architecture level. We do provide education, training and guidance to our customers, because at that level, if you don’t have succinct security requirements defined, then it’s really hard to verify or check something in the design. What often happens is customers will say, ‘I want to have a secure chip.’ But it’s not as easy as just pressing a button and getting a green check mark that confirms the chip is now secure.”

To be successful, engineering teams must start at the architectural stages and define the security requirements. “Once that is done, they can start actually writing the RTL,” Oberg said. “There are tools available to provide assurances these security requirements are being met, and run within the existing simulation and emulation environments to help validate the security requirements, and help identify any unknown design weaknesses. Generally, this helps hardware and verification engineers increase their productivity and build confidence that the system is indeed meeting the security requirements.”

Fig. 1: A cybersecurity model includes multiple stages, progressing from the very basic to in-depth. It is important for organizations to know what stages their cyber defense system are. Source: Cycuity

Steve Garrison, senior vice president, marketing of Stellar Cyber, noted that if cyber threats were uncovered during the detection process, so many data files may be generated that they will be difficult for humans to sort through. Graphical displays can speed up the process and reduce the overall mean time to detection (MTTD) and mean time to response (MTTR).

Fig. 2: Using graphical displays  would reduce the overall meantime to detection (MTTD) and meantime to response (MTTR). Source: Stellar Cyber

Testing is essential
Another important stage in the design process is testing, whereby each system design requires a vigorous attack simulation tool to weed out the basic oversights to ensure it meets the predefined standard.

“First, if you want to understand how defensive systems will function in the real world, it’s important to test them under conditions, which are as realistic as possible,” Keysight’s Register said. “The network environment should have the same amount of traffic, mix of applications, speeds, behavioral characteristics, and timing as the real world. For example, the timing of a sudden uptick in email and social media traffic corresponds to the time when people open up their laptops at work. The attack traffic needs to be as realistic as possible as well – hackers try hard not to be noticed, often preferring ‘low and slow’ attacks, which may take hours or days to complete, making detection much more difficult. The same obfuscation techniques, encryption, and decoy traffic employed by threat actors needs to be simulated as accurately as possible.”

Further, due to mistaken assumptions during testing, defensive systems often perform great in the lab, yet fail spectacularly in production networks.  “Afterwards we hear, for example, ‘I didn’t think hackers would encrypt their malware,’ or ‘Internal e-mails weren’t checked for malicious attachments, only those from external senders,’” Register explained. “Also, in security testing, currency is key. Attacks and obfuscation techniques are constantly evolving. If a security system is tested against stale attacks, then the value of that testing is limited. The offensive tools should be kept as up to date as possible to ensure the most effective performance against the tools a system is likely to encounter in the wild.”

Semiconductor security
Almost all system designs depend on semiconductors, so it is important to ensure that any and all chips, firmware, FPGAs, and SoCs are secure – including those that perform ML functionality.

“Semiconductor security is a constantly evolving problem and requires an adaptable solution, said Jayson Bethurem, vice president marketing and business development at Flex Logix. “Fixed solutions with current cryptography that are implemented today will inevitably be challenged in the future. Hackers today have more time, resources, training, and motivation to disrupt technology. With technology increasing in every facet of our lives, defending against this presents a real challenge. We also have to consider upcoming threats, namely quantum computing.”

Many predict that quantum computing will be able to crack current cryptography solutions in the next few years. “Fortunately, semiconductor manufacturers have solutions that can enable cryptography agility, which can dynamically adapt to evolving threats,” Bethurem said. “This includes both updating hardware accelerated cryptography algorithms and obfuscating them, an approach that increases root of trust and protects valuable IP secrets. Advanced solutions like these also involve devices randomly creating their own encryption keys, making it harder for algorithms to crack encryption codes.”

Advances in AI/ML algorithms can adapt to new threats and reduce latency of algorithm updates from manufacturers. This is particularly useful with reconfigurable eFPGA IP, which can be implemented into any semiconductor device to thwart all current and future threats and optimized to run AI/ML-based cryptography solutions. The result is a combination of high-performance processing, scalability, and low-latency attack response.

Chips that support AI/ML algorithms need not only computing power, but also accelerators for those algorithms. In addition, all of this needs to happen without exceeding a tight power budget.

“More AI/ML systems run at tiny edges rather than at the core,” said Detlef Houdeau, senior director of design system architecture at Infineon Technologies. “AI/ML systems don’t need any bigger computer and/or cloud. For instance, a Raspberry Pi for a robot in production can have more than 3 AI/ML algorithms working in parallel. A smartphone has more than 10 AI/ML functions in the phone, and downloading new apps brings new AI/ML algorithms into the device. A pacemaker can have 2 AI/ML algorithms. Security chips, meanwhile, need a security architecture as well as accelerators for encryption. Combining an AI/ML accelerator with an encryption accelerator in the same chip could increase the performance in microcontroller units, and at the same time foster more security at the edge. The next generation of microelectronics could show this combination.”

After developers have gone through design reviews and the systems have run vigorous tests, it helps to have third-party certification and/or credentials to ensure the systems are indeed secure from a third-party independent viewpoint.

“As AI, and recently generative AI, continue to transform all markets, there will be new attack vectors to mitigate against,” said Arm’s Maidment. “We expect to see networks become smarter in the way they monitor traffic and behaviors. The use of AI/ML allows network-based monitoring at scale to allow potential unexpected or rogue behavior to be identified and isolated. Automating network monitoring based on AI/ML will allow an extra layer of defense as networks scale out and establish effectively a ‘zero trust’ approach. With this approach, analysis at scale can be tuned to look at particular threat vectors depending on the use case.”

With an increase in AI/ML adoption at the edge, a lot of this is taking place on the CPU. “Whether it is handling workloads in their entirety, or in combination with a co-processor like a GPU or NPU, how applications are deployed across the compute resources needs to be secure and managed centrally within the edge AI/ML device,” Maidment said. “Building edge AI/ML devices based on a hardware root of trust is essential. It is critical to have privileged access control of what code is allowed to run where using a trusted memory management architecture. Arm continually invests in security, and the Armv9 architecture offers a number of new security features. Alongside architecture improvements, we continue to work in partnership with the industry on our ecosystem security framework and certification scheme, PSA Certified, which is based on a certified hardware RoT. This hardware base helps to improve the security of systems and fulfill the consumer expectation that as devices scale, they remain secure.”

Outlook
It is important to understand that threat actors will continue to evolve attacks using AI/ML. Experts suggest that to counter such attacks, organizations, institutions, and government agencies will have to continually improve defense strategies and capabilities, including AI/ML deployment.

AI/ML can be used as weapon from an attacker for industrial espionage and/or industrial sabotage, and stopping incursions will require a broad range of cyberattack prevention and detection tools, including AI/ML functionality for anomaly detection. But in general, hackers are almost always one step ahead.

According to Register, “the recurring cycle is: 1) hackers come out with a new tool or technology that lets them attack systems or evade detection more effectively; 2) those attacks cause enough economic damage that the industry responds and develops effective countermeasures; 3) the no-longer-new hacker tools are still employed effectively, but against targets that haven’t bothered to update their defenses; 4) hackers develop new offensive tools that are effective against the defensive techniques of high-value targets, and the cycle starts anew.”

Related Reading
Securing Chip Manufacturing Against Growing Cyber Threats
Suppliers are the number one risk, but reducing attacks requires industry-wide collaboration.
Data Center Security Issues Widen
The number and breadth of hardware targets is increasing, but older attack vectors are not going away. Hackers are becoming more sophisticated, and they have a big advantage.

The post Using AI/ML To Combat Cyberattacks appeared first on Semiconductor Engineering.

Machine learning is being used by hackers to find weaknesses in chips and systems, but it also is starting to be used to prevent breaches by pinpointing hardware and software design flaws.

To make this work, machine learning (ML) must be trained to identify vulnerabilities, both in hardware and software. With proper training, ML can detect cyber threats and prevent them from accessing critical data. As ML encounters additional cyberattack scenarios, it can learn and adapt, helping to build a more sophisticated defense system that includes hardware, software, and how they interface with larger systems. It also can automate many cyber defense tasks with minimum human intervention, which saves time, effort, and money.

ML is capable of sifting through large volumes of data much faster than humans. Potentially, it can reduce or remove human errors, lower costs, and boost cyber defense capability and overall efficiency. It also can perform such tasks as connection authentication, system design, vulnerability detection, and most important, threat detection through pattern and behavioral analysis.

“AI/ML is finding many roles protecting and enhancing security for digital devices and services,” said David Maidment, senior director of market development at Arm. “However, it is also being used as a tool for increasingly sophisticated attacks by threat actors. AI/ML is essentially a tool tuned for very advanced pattern recognition across vast data sets. Examples of how AI/ML can enhance security include network-based monitoring to spot rogue behaviors at scale, code analysis to look for vulnerabilities on new and legacy software, and automating the deployment of software to keep devices up-to-date and secure.”

This means that while AI/ML can be used as a force for good, inevitably bad actors will use it to increase the sophistication and scale of attacks. “Building devices and services based on security best practices, having a hardware-protected root of trust (RoT), and an industry-wide methodology to standardize and measure security are all essential,” Maidment said. “The focus on security, including the rapid growth of AI/ML, is certainly driving industry and government discussions as we work on solutions to maximize AI/ML’s benefits and minimize any potential harmful impact.”

Zero trust is a fundamental requirement when it comes to cybersecurity. Before a user or device is allowed to connect to the network or server, requests have to be authenticated to make sure they are legitimate and authorized. ML will enhance the authentication process, including password management, phishing prevention, and malware detection.

Areas that bad actors look to exploit are software design vulnerabilities and weak points in systems and networks. Once hackers uncover these vulnerabilities, they can be used as a point of entrance to the network or systems. ML can detect these vulnerabilities and alert administrators.

Taking a proactive approach by doing threat detection is essential in cyber defense. ML pattern and behavioral analysis strengths support this strategy. When ML detects unusual behavior in data traffic flow or patterns, it sends an alert about abnormal behavior to the administrator. This is similar to the banking industry’s practice of watching for credit card use that does not follow an established pattern. A large purchase overseas on a credit card with a pattern of U.S. use only for moderate amounts would trigger an alert, for example.

As hackers become more sophisticated with new attack vectors, whether it is new ransomware or distributed denial of service (DDoS) attacks, ML will do a much better job than humans in detecting these unknown threats.

Limitations of ML in cybersecurity
While ML provides many benefits, its value depends on the data used to train it. The more that can be used to train the ML model, the better it is at detecting fraud and cyber threats. But acquiring this data raises overall cybersecurity system design expenses. The model also needs constant maintenance and tuning to sustain peak performance and meet the specific needs of users. And while ML can do many of the tasks, it still requires some human involvement, so it’s essential to understand both cybersecurity and how well ML functions.

While ML is effective in fending off many of the cyberattacks, it is not a panacea. “The specific type of artificial intelligence typically referenced in this context is machine learning (ML), which is the development of algorithms that can ingest large volumes of training data, then generalize and make meaningful observations and decisions based on novel data,” said Scott Register, vice president of security solutions at Keysight Technologies. “With the right algorithms and training, AI/ML can be used to pinpoint cyberattacks which might otherwise be difficult to detect.”

However, no one — at least in the commercial space — has delivered a product that can detect very subtle cyberattacks with complete accuracy. “The algorithms are getting better all the time, so it’s highly probable that we’ll soon have commercial products that can detect and respond to attacks,” Register said. “We must keep in mind, however, that attackers don’t sit still, and they’re well-funded and patient. They employ ‘offensive AI,’ which means they use the same types of techniques and algorithms to generate attacks which are unlikely to be detected.”

ML implementation considerations
For any ML implementation, a strong cyber defense system is essential, but there’s no such thing as a completely secure design. Instead, security is a dynamic and ongoing process that requires constant fine-tuning and improvement against ever-changing cyberattacks. Implementing ML requires a clear security roadmap, which should define requirements. It also requires implementing a good cybersecurity process, which secures individual hardware and software components, as well as some type of system testing.

“One of the things we advise is to start with threat modeling to identify a set of critical design assets to protect from an adversary under confidentiality or integrity,” said Jason Oberg, CTO at Cycuity. “From there, you can define a set of very succinct, secure requirements for the assets. All of this work is typically done at the architecture level. We do provide education, training and guidance to our customers, because at that level, if you don’t have succinct security requirements defined, then it’s really hard to verify or check something in the design. What often happens is customers will say, ‘I want to have a secure chip.’ But it’s not as easy as just pressing a button and getting a green check mark that confirms the chip is now secure.”

To be successful, engineering teams must start at the architectural stages and define the security requirements. “Once that is done, they can start actually writing the RTL,” Oberg said. “There are tools available to provide assurances these security requirements are being met, and run within the existing simulation and emulation environments to help validate the security requirements, and help identify any unknown design weaknesses. Generally, this helps hardware and verification engineers increase their productivity and build confidence that the system is indeed meeting the security requirements.”

Fig. 1: A cybersecurity model includes multiple stages, progressing from the very basic to in-depth. It is important for organizations to know what stages their cyber defense system are. Source: Cycuity

Steve Garrison, senior vice president, marketing of Stellar Cyber, noted that if cyber threats were uncovered during the detection process, so many data files may be generated that they will be difficult for humans to sort through. Graphical displays can speed up the process and reduce the overall mean time to detection (MTTD) and mean time to response (MTTR).

Fig. 2: Using graphical displays  would reduce the overall meantime to detection (MTTD) and meantime to response (MTTR). Source: Stellar Cyber

Testing is essential
Another important stage in the design process is testing, whereby each system design requires a vigorous attack simulation tool to weed out the basic oversights to ensure it meets the predefined standard.

“First, if you want to understand how defensive systems will function in the real world, it’s important to test them under conditions, which are as realistic as possible,” Keysight’s Register said. “The network environment should have the same amount of traffic, mix of applications, speeds, behavioral characteristics, and timing as the real world. For example, the timing of a sudden uptick in email and social media traffic corresponds to the time when people open up their laptops at work. The attack traffic needs to be as realistic as possible as well – hackers try hard not to be noticed, often preferring ‘low and slow’ attacks, which may take hours or days to complete, making detection much more difficult. The same obfuscation techniques, encryption, and decoy traffic employed by threat actors needs to be simulated as accurately as possible.”

Further, due to mistaken assumptions during testing, defensive systems often perform great in the lab, yet fail spectacularly in production networks.  “Afterwards we hear, for example, ‘I didn’t think hackers would encrypt their malware,’ or ‘Internal e-mails weren’t checked for malicious attachments, only those from external senders,’” Register explained. “Also, in security testing, currency is key. Attacks and obfuscation techniques are constantly evolving. If a security system is tested against stale attacks, then the value of that testing is limited. The offensive tools should be kept as up to date as possible to ensure the most effective performance against the tools a system is likely to encounter in the wild.”

Semiconductor security
Almost all system designs depend on semiconductors, so it is important to ensure that any and all chips, firmware, FPGAs, and SoCs are secure – including those that perform ML functionality.

“Semiconductor security is a constantly evolving problem and requires an adaptable solution, said Jayson Bethurem, vice president marketing and business development at Flex Logix. “Fixed solutions with current cryptography that are implemented today will inevitably be challenged in the future. Hackers today have more time, resources, training, and motivation to disrupt technology. With technology increasing in every facet of our lives, defending against this presents a real challenge. We also have to consider upcoming threats, namely quantum computing.”

Many predict that quantum computing will be able to crack current cryptography solutions in the next few years. “Fortunately, semiconductor manufacturers have solutions that can enable cryptography agility, which can dynamically adapt to evolving threats,” Bethurem said. “This includes both updating hardware accelerated cryptography algorithms and obfuscating them, an approach that increases root of trust and protects valuable IP secrets. Advanced solutions like these also involve devices randomly creating their own encryption keys, making it harder for algorithms to crack encryption codes.”

Advances in AI/ML algorithms can adapt to new threats and reduce latency of algorithm updates from manufacturers. This is particularly useful with reconfigurable eFPGA IP, which can be implemented into any semiconductor device to thwart all current and future threats and optimized to run AI/ML-based cryptography solutions. The result is a combination of high-performance processing, scalability, and low-latency attack response.

Chips that support AI/ML algorithms need not only computing power, but also accelerators for those algorithms. In addition, all of this needs to happen without exceeding a tight power budget.

“More AI/ML systems run at tiny edges rather than at the core,” said Detlef Houdeau, senior director of design system architecture at Infineon Technologies. “AI/ML systems don’t need any bigger computer and/or cloud. For instance, a Raspberry Pi for a robot in production can have more than 3 AI/ML algorithms working in parallel. A smartphone has more than 10 AI/ML functions in the phone, and downloading new apps brings new AI/ML algorithms into the device. A pacemaker can have 2 AI/ML algorithms. Security chips, meanwhile, need a security architecture as well as accelerators for encryption. Combining an AI/ML accelerator with an encryption accelerator in the same chip could increase the performance in microcontroller units, and at the same time foster more security at the edge. The next generation of microelectronics could show this combination.”

After developers have gone through design reviews and the systems have run vigorous tests, it helps to have third-party certification and/or credentials to ensure the systems are indeed secure from a third-party independent viewpoint.

“As AI, and recently generative AI, continue to transform all markets, there will be new attack vectors to mitigate against,” said Arm’s Maidment. “We expect to see networks become smarter in the way they monitor traffic and behaviors. The use of AI/ML allows network-based monitoring at scale to allow potential unexpected or rogue behavior to be identified and isolated. Automating network monitoring based on AI/ML will allow an extra layer of defense as networks scale out and establish effectively a ‘zero trust’ approach. With this approach, analysis at scale can be tuned to look at particular threat vectors depending on the use case.”

With an increase in AI/ML adoption at the edge, a lot of this is taking place on the CPU. “Whether it is handling workloads in their entirety, or in combination with a co-processor like a GPU or NPU, how applications are deployed across the compute resources needs to be secure and managed centrally within the edge AI/ML device,” Maidment said. “Building edge AI/ML devices based on a hardware root of trust is essential. It is critical to have privileged access control of what code is allowed to run where using a trusted memory management architecture. Arm continually invests in security, and the Armv9 architecture offers a number of new security features. Alongside architecture improvements, we continue to work in partnership with the industry on our ecosystem security framework and certification scheme, PSA Certified, which is based on a certified hardware RoT. This hardware base helps to improve the security of systems and fulfill the consumer expectation that as devices scale, they remain secure.”

Outlook
It is important to understand that threat actors will continue to evolve attacks using AI/ML. Experts suggest that to counter such attacks, organizations, institutions, and government agencies will have to continually improve defense strategies and capabilities, including AI/ML deployment.

AI/ML can be used as weapon from an attacker for industrial espionage and/or industrial sabotage, and stopping incursions will require a broad range of cyberattack prevention and detection tools, including AI/ML functionality for anomaly detection. But in general, hackers are almost always one step ahead.

According to Register, “the recurring cycle is: 1) hackers come out with a new tool or technology that lets them attack systems or evade detection more effectively; 2) those attacks cause enough economic damage that the industry responds and develops effective countermeasures; 3) the no-longer-new hacker tools are still employed effectively, but against targets that haven’t bothered to update their defenses; 4) hackers develop new offensive tools that are effective against the defensive techniques of high-value targets, and the cycle starts anew.”

Related Reading
Securing Chip Manufacturing Against Growing Cyber Threats
Suppliers are the number one risk, but reducing attacks requires industry-wide collaboration.
Data Center Security Issues Widen
The number and breadth of hardware targets is increasing, but older attack vectors are not going away. Hackers are becoming more sophisticated, and they have a big advantage.

The post Using AI/ML To Combat Cyberattacks appeared first on Semiconductor Engineering.