Adversarial AI and Cybersecurity Threats Explained

Adversarial AI is a type of artificial intelligence designed to deceive or manipulate other AI systems or humans. This can be a significant threat to cybersecurity, as it can be used to create convincing fake news or malware that evades detection.

In 2019, a study found that 90% of AI systems can be fooled by adversarial attacks, highlighting the vulnerability of modern AI technology. This is because AI systems often rely on patterns and algorithms that can be manipulated.

Malicious actors can use adversarial AI to create sophisticated phishing attacks that are nearly indistinguishable from legitimate emails. This can lead to significant financial losses and reputational damage for individuals and organizations.

Adversarial AI can also be used to create deepfakes, which are highly realistic videos or audio recordings that can be used to spread misinformation or propaganda.

Adversarial AI can be a powerful tool for malicious purposes, as seen in the potential for AI systems to be exploited in sensitive applications like autonomous vehicles or cybersecurity. This can have serious consequences, such as causing an autonomous vehicle to misinterpret a road sign and make a dangerous maneuver.

The dynamic nature of cyber threats necessitates continuous learning and adaptation. Adversarial AI techniques empower intrusion detection systems (IDS) to enhance their learning capabilities in real-time, actively responding to changing attack patterns and adjusting their detection strategies accordingly.

Data poisoning, a form of contamination that increases errors in the output, can reprogram algorithms with potentially malicious intent. Concerns have been raised for user-generated training data, such as content recommendation or natural language models, and the ubiquity of fake accounts offers many opportunities for poisoning.

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In Experiment 1, Vorobeychik's team tested the robustness of ML-based detection models using a simplified evasion model as a proxy for realizable evasion attacks.

The team studied several PDF malware detection tools, including the content-based PDFRate algorithm and the structure-based Hidost classifier, under three different training regimes.

The first regime had no training against evasion attacks. The second regime trained against EvadeML, establishing a baseline for maximum robustness. The third regime used a feature-space attack model, known as RobustML.

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The team measured the resulting models' efficacy against evasion attacks and simpler attacks that don't incorporate adversarial learning.

Training against the incorrect feature-space model made the content-based classifier, PDFRate, nearly 100% robust against EvadeML attacks, but at the cost of reduced performance on non-adversarial data.

RobustML achieved only about 70% robustness against EvadeML attacks when applied to the structure-based classifier Hidost.

The team then explored the idea of using only the features of PDFs that change if malicious functionality is removed, called the "conserved features."

In Experiment 2, Vorobeychik's team explored whether the feature-space approach could be improved by using conserved features. They found that the number of conserved features was extremely small, ranging from 4 to 8 out of thousands of features.

The team identified conserved features using statistical techniques, but they were only a small fraction of the overall feature space. They wondered if they could make sure these features were conserved in a meaningful sense.

By using only the conserved features of the adversarial model against which the malware detector is trained, the team was able to achieve a robustness rate of 100 percent for Hidost and 90 percent for SL2013. For PDFRate, the new approach maintained a high robustness while also improving AUC.

Here are the results of Vorobeychik's team experiments on the number of conserved features:

These results show that using conserved features can significantly improve the robustness of the feature-space approach.

Data poisoning is a type of adversarial attack where an attacker contaminates the training dataset with misleading or deceptive information. This can be done to skew the AI's decision-making process, leading to potentially disastrous outcomes.

Facebook reportedly removes around 7 billion fake accounts per year, which highlights the scale of the problem. Data poisoning can be used to bias recommendation and moderation algorithms on social media platforms.

An attacker may inject malicious samples into a training dataset to disrupt the AI's performance. This can be particularly damaging for intrusion detection systems, which are often trained using collected data.

Data poisoning techniques can also be applied to text-to-image models to alter their output, which can be used by artists to defend their copyrighted works or artistic style against imitation.

Here's a breakdown of the types of data poisoning attacks:

The consequences of data poisoning can be severe, particularly in sensitive applications such as autonomous vehicles or cybersecurity. It's essential to implement robust data validation and cleansing procedures to prevent such attacks.

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A White Box approach to AI is all about transparency and explainability, where the model's decision-making process is open to scrutiny. This is in contrast to a Black Box approach, where the inner workings of the model are unknown.

The goal of a White Box approach is to create models that are easy to understand and interpret, making it possible to identify and correct biases.

This can be achieved through techniques such as feature importance, partial dependence plots, and SHAP values.

By using these methods, developers can gain a deeper understanding of how the model is making decisions, and what factors are driving those decisions.

In a real-world example, a team used a White Box approach to analyze a model that was being used to predict customer churn.

Robustness and security are crucial aspects of Adversarial AI. A robust ML-based detection system can be made more resilient against evasion attacks through techniques like iterative retraining, as demonstrated by Yevgeniy Vorobeychik's team.

Training against the incorrect feature-space model can render a content-based classifier nearly 100 percent robust against EvadeML attacks, although the price of robustness may be reduced performance on non-adversarial data. The team's experimental work showed that training against a feature-space attack model (the RobustML approach) can improve robustness, but it may not always be effective.

The success of a robust ML-based detection system depends on its ability to learn from labeled examples and identify patterns in data. However, attackers can manipulate malware files to evade detection, highlighting the need for robustness in AI systems.

Adversarial AI is a game-changer in the world of cyber defense. It leverages advanced machine learning techniques to safeguard critical systems and protect sensitive data from malicious actors.

By empowering organizations and governments, Adversarial AI allows for the detection and mitigation of cyber threats in real-time. This proactive approach enables rapid response and reduces the risk of breaches.

Adversarial AI complements government data protection efforts by providing an extra layer of defense against evolving attack vectors.

Comprehensive Threat Modeling is crucial in defending against Adversarial AI. It involves proactively identifying potential threats and vulnerabilities in AI systems. By considering various attack vectors and scenarios, organizations can develop countermeasures that target the specific weak points of their AI systems.

To effectively model threats, security teams must assess the risks associated with their AI systems. This includes understanding the interests of the players and the costs they face, as suggested by game theory literature. By clearly identifying these factors, organizations can develop a high-granularity model of the strategy spaces and define outcomes and game structures.

A comprehensive threat model should include the following key components:

By incorporating these components into a comprehensive threat model, organizations can build a strong foundation for defending against Adversarial AI. Regular model testing and evaluation are also essential to stay ahead of potential attacks.

Understanding the context of network activity is crucial for detecting sophisticated attacks. AI-based Intrusion Detection Systems (IDS) have made significant strides in contextual understanding with the aid of adversarial AI techniques.

Deep learning models enable IDS to analyze multiple data sources simultaneously, including network traffic, system logs, and user behavior. This holistic approach allows them to identify complex attack vectors that would otherwise go unnoticed.

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The ever-changing threat landscape poses a significant challenge to security professionals, with adversarial AI attacks able to exploit vulnerabilities in AI systems.

Adversarial AI attacks can bypass traditional security measures, making it crucial for security professionals to stay one step ahead. This is why comprehensive threat modeling is essential, allowing security teams to assess the risks associated with their AI systems.

Comprehensive threat modeling involves considering various attack vectors and scenarios to develop countermeasures that target the specific weak points of AI systems. By being proactive, organizations can build a strong foundation for defending against adversarial AI.

Real-time threat intelligence is also crucial in the realm of cybersecurity, with AI-based IDS now utilizing adversarial AI techniques to access and analyze real-time threat intelligence feeds. This enables security teams to swiftly respond to emerging risks.

The ability to identify and prioritize potential threats based on their severity and relevance is made possible by incorporating natural language processing (NLP) and sentiment analysis into these systems. This helps minimize downtime and potential damage.

Strength in unity is key to defending against adversarial AI, which is why organizations are increasingly embracing collective intelligence by sharing threat intelligence data and collaborating with industry peers. This collaborative approach facilitates early detection and swift response.

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Government institutions are becoming prime targets for cybercriminals seeking to exploit vulnerabilities and gain unauthorized access to sensitive information. These attacks can have devastating consequences, ranging from the manipulation of sensitive data to the disruption of essential services.

The consequences of such breaches can be dire, leading to compromised national security, financial losses, and erosion of public trust. Public sector security measures must keep pace with these evolving threats.

Government agencies are facing an escalating threat with the rise of targeted cyber attacks on government institutions. These attacks, often sophisticated and relentless, pose significant risks to the stability and integrity of our institutions.

The collaboration between governmental entities and private sectors is essential to mitigate the risks posed by sophisticated cyber attacks. This collaboration enables the implementation of robust and proactive measures, leveraging advanced threat intelligence.

Government funding enables cybersecurity experts to stay ahead of malicious actors by developing innovative solutions and defenses. By investing in cutting-edge technologies and fostering collaboration between academia, industry, and agencies, government can bolster the collective defense against adversarial AI threats.

Public sector security measures must employ robust defense mechanisms, proactive monitoring, and advanced AI-powered solutions to safeguard critical data and maintain operational continuity.

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Detecting adversarial AI attacks is a formidable task due to their ability to mimic legitimate behavior and evade traditional detection methods.

Security professionals face the challenge of developing advanced techniques and algorithms to accurately identify these attacks and attribute them to their sources.

Timely detection is crucial to minimize potential damage and protect critical systems.

Collaboration and sharing are crucial in the fight against adversarial AI. Open dialogue and the exchange of knowledge and best practices can help foster a collective defense against these threats.

Concerns around intellectual property and competitive advantage can hinder effective collaboration, posing an additional challenge. This can make it difficult for security professionals to share information and work together to protect AI systems.

Collaborative efforts among researchers, organizations, and security professionals are essential to stay updated and share knowledge about emerging threats and defense mechanisms. By fostering a community-driven approach, information sharing platforms, and collaborative initiatives, the collective expertise can be leveraged to develop effective countermeasures.

Staying one step ahead of adversaries requires a continuous commitment to innovation and the adoption of proactive security measures. Organizations can bolster their defenses against adversarial AI attacks by adopting security strategies.

Collaborative threat intelligence facilitates early detection, swift response, and the exchange of best practices, ensuring collective defense against Adversarial AI.

Detecting adversarial AI attacks is a formidable task due to their ability to mimic legitimate behavior and evade traditional detection methods.

AI-based Intrusion Detection Systems (IDS) have emerged as a powerful line of defense against malicious attacks, with the integration of adversarial AI techniques allowing them to detect and mitigate threats more effectively.

Employing sophisticated machine learning algorithms, AI-based IDS can identify anomalous behavior patterns and adapt their detection capabilities to evolving threats.

By leveraging the power of generative adversarial networks (GANs), IDS can generate synthetic samples and analyze their impact on the network, enabling them to learn from adversarial examples and improve their detection capabilities.

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Real-time threat intelligence feeds are now being accessed and analyzed by AI-based IDS, incorporating natural language processing (NLP) and sentiment analysis to identify and prioritize potential threats based on their severity and relevance.

This enables security teams to swiftly respond to emerging risks, mitigating potential damage and minimizing downtime.

Collaborative threat intelligence facilitates early detection, swift response, and the exchange of best practices, ensuring collective defense against Adversarial AI.

Simulating Cyber Engagements is a crucial aspect of staying ahead of cyber threats. O’Reilly’s team at MIT’s Computer Science and Artificial Intelligence Laboratory has developed a bio-inspired framework called Rivals that simulates scenarios in which two parties in conflict with each other interact and learn from the experience.

This approach doesn't require large collections of real-world data, but rather self-contained models and simulation. The goal is to inform network designers about the resilience or robustness of their network design before it's deployed.

By simulating adversarial cyber engagements, we can anticipate the most disruptive attack that might result from an attacker-defender arms race. This allows designers to plan courses of action for improving network resilience.

O’Reilly’s work has shown that this approach can help designers prepare for the unexpected, and stay one step ahead of cyber adversaries. By simulating different scenarios, we can identify vulnerabilities and weaknesses in our systems.

This proactive approach can help us detect and mitigate cyber threats in real-time, enabling rapid response and reducing the risk of breaches.

AI-based IDS employ sophisticated machine learning algorithms to identify anomalous behavior patterns. With the advent of adversarial AI techniques, these systems have become even more adept at detecting previously unknown attacks.

By leveraging the power of generative adversarial networks (GANs), IDS can generate synthetic samples and analyze their impact on the network. This enables them to learn from adversarial examples and adapt their detection capabilities to evolving threats.

AI-based IDS can now identify and prioritize potential threats based on their severity and relevance. This is made possible by incorporating natural language processing (NLP) and sentiment analysis into their systems.

Real-time threat intelligence feeds are now being accessed and analyzed by AI-based IDS to provide timely response to emerging risks. This enables security teams to swiftly respond to potential threats, mitigating potential damage and minimizing downtime.

IDS can now generate synthetic samples and analyze their impact on the network, allowing them to learn from adversarial examples and adapt their detection capabilities to evolving threats.

As we explore the world of adversarial AI, it's essential to address the complex ethical considerations that come with it. Adversarial AI raises important questions about who is responsible when an AI system is deceived by an adversarial example and makes a mistake.

The responsibility is a complex issue that doesn't have a clear-cut answer. It's crucial to address this question as AI technology continues to evolve. Should the developer of the AI system, the user of the system, or the person who created the adversarial example be held accountable? These are the kinds of questions that require careful consideration and dialogue among stakeholders.

Transparency is a fundamental ethical consideration in adversarial AI defense. Organizations must maintain transparency in their defense strategies to foster collaboration within the cybersecurity community. By openly sharing information about the adversarial machine learning methods employed, we can stay ahead of emerging threats.

Accountability is also crucial in adversarial AI defense. Organizations and individuals involved in its implementation must be held accountable for the design, development, and deployment of adversarial AI systems. This ensures that they are held to high ethical standards and maintains public trust and confidence in the technology.

In the pursuit of securing AI systems against adversarial attacks, it's essential to safeguard the privacy of individuals and organizations. Adversarial machine learning often relies on vast amounts of data, raising concerns about data privacy and protection.

As we navigate through an era of ever-evolving cyber threats, the importance of staying one step ahead of malicious actors cannot be stressed enough. This is where adversarial AI comes in, helping us anticipate and counter potential dangers.

Cyber threats are becoming increasingly sophisticated, making it crucial to stay ahead of the game. The future of adversarial AI will be shaped by its ability to evolve and adapt to new threats.

Staying one step ahead of malicious actors requires a proactive approach, and adversarial AI is poised to play a key role in this effort.

Selecting the right model is crucial in defending against adversarial AI. The right model might not be obvious, and even a wrong model may work well for a specific purpose.

Simpler or more mathematically tractable models are generally easier to work with and are a good place to start.

To build a strong defense, organizations need to proactively identify potential threats and vulnerabilities through comprehensive threat modeling. This approach helps assess the risks associated with AI systems and develop countermeasures that target specific weak points.

Regular model testing and evaluation is essential to stay ahead of potential attacks. By employing various testing techniques, such as adversarial testing and penetration testing, organizations can simulate real-world attack scenarios and strengthen their models accordingly.

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Model robustness plays a pivotal role in Adversarial AI Defense. Techniques such as adversarial training, robust optimization, and ensemble learning can be used to fortify models against adversarial attacks.

Designing AI systems that are robust to adversarial examples is a key approach in defending against adversarial attacks. This can be achieved by training the system on a diverse range of data, including adversarial examples, and regularly testing its performance against adversarial attacks.

Defensive distillation is another technique that can make an ML algorithm more flexible by having one model predict the outputs of another model that was trained earlier. This can help identify unknown threats.

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To combat evasion attacks, cyber defense strategies must incorporate robust anomaly detection algorithms and vigilant model monitoring. These attacks exploit the imperfections of a trained model by modifying samples to evade detection.

Evasion attacks can be generally split into two categories: black box attacks and white box attacks. Black box attacks involve modifying samples without any knowledge of the model's internal workings, while white box attacks involve exploiting the model's weaknesses by manipulating its inputs.

Robust anomaly detection algorithms can help detect evasion attacks by identifying unusual patterns in the data. Vigilant model monitoring can also help by regularly testing the model's performance against adversarial examples.

Here are some defense mechanisms against evasion attacks:

Network scanning is a crucial aspect of cyber warfare, where attackers use software to probe a system's topology and characteristics to find valuable assets.

The attacker's goal is to launch scans to optimize success, but the defender seeks to prevent these scans from being effective by presenting a deceptive view of the network.

In a simulated SDN environment, the defender can change parameters of the deceptive network to trick the attacker, allowing for experiments to measure the success of both parties.

The defender can use ML to optimally configure the deceptive defense, anticipating how the attacker will learn from scan results.

The attacker can use ML to determine the best attack possible against each particular defense configuration, but the defender can also use ML to find the best defense against the attacker's scans.

Real-world networks and attackers do not have the same degree of agility, so the simulation operates in a lockstep fashion, allowing one side to learn while the other stays static.

This simulation reveals that the same strategy may have different levels of success when one side or the other can learn versus when they both can.

The defender's choices, such as creating a deceptive view, affect the attacker's options and outcomes, creating complexity in the environment.

The attacker's decisions, like how many IP addresses to scan and in what order, impact the defender's ability to detect and defend against the scans.

The defender can use ML to learn from the measured outcomes of a particular strategy, tactic, or implementation, and optimize a new approach toward achieving the objective.

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Modalities involve the different ways attackers can interact with a model. A white box attack allows the attacker to access model parameters and architecture. This can be used to maliciously tweak or alter the model's inner workings to produce faulty outputs. In a black box attack, the attacker doesn't have access to the model's inner workings and can only know its outputs.

Tactical learning is a key aspect of modalities, where the attacker and defender can learn from each other's actions. The attacker can decide how many IP addresses to scan and how to batch them, while the defender can create a deceptive view to confuse the attacker. This creates complexity in the environment and requires the use of machine learning (ML) to optimize strategies.

The ever-changing threat landscape poses a significant challenge to security professionals, making it crucial to stay one step ahead. Adversarial AI attacks can exploit vulnerabilities in AI systems, leveraging their own sophisticated algorithms to deceive and manipulate. These attacks can bypass traditional security measures.

Evasion attacks involve exploiting the imperfection of a trained model. This can be done through black box attacks or white box attacks, where the attacker modifies samples to evade detection. For example, spammers and hackers may obfuscate the content of spam emails and malware to evade detection by anti-spam filters.

As we explore the mechanisms to counter evasion attacks, it's essential to understand the various defense strategies that can be employed. Secure learning algorithms are one such mechanism that can help protect against evasion attacks.

Byzantine-resilient algorithms are another crucial mechanism that can detect and prevent evasion attacks. These algorithms are designed to work even when some of the data is malicious.

Multiple classifier systems can also be effective in detecting evasion attacks. This is because the attacker may be able to evade detection by one classifier, but not by a combination of multiple classifiers.

AI-written algorithms can also be used to detect evasion attacks. These algorithms can learn from the data and identify patterns that may indicate an evasion attack.

Privacy-preserving learning is another mechanism that can help protect against evasion attacks. This involves designing algorithms that protect sensitive information and prevent it from being accessed by unauthorized parties.

Sanitizing training data is also an essential mechanism in preventing evasion attacks. This involves removing any malicious data from the training set to prevent the model from learning to evade detection.

Adversarial training is another mechanism that can help protect against evasion attacks. This involves training the model on a mix of clean and adversarial data to make it more robust against evasion attacks.

Backdoor detection algorithms can also be used to detect evasion attacks. These algorithms are designed to detect any backdoors or malicious code that may have been embedded in the model.

Gradient masking/obfuscation techniques can also be used to prevent evasion attacks. However, these techniques are deemed unreliable as they can be circumvented by the attacker.

Ensembles of models have been proposed in the literature, but caution should be applied when relying on them. This is because ensembling weak classifiers may not result in a more accurate model in the adversarial context.

Here is a summary of the defense mechanisms against evasion attacks:

Addressing the challenges of adversarial AI attacks requires significant resources in terms of both time and expertise.

Security professionals face the challenge of allocating sufficient resources to research, develop, and implement robust security measures. It's a daunting task that can be hindered by inadequate funding.

Adequate funding, skilled personnel, and state-of-the-art tools are necessary to effectively combat the evolving threats posed by adversarial AI attacks. Without them, security measures may be inadequate or ineffective.

As technology advances, so do the techniques employed by those seeking to exploit vulnerabilities in artificial intelligence (AI) systems.

One type of adversarial attack is the Evasion Attack, which involves modifying input data to deceive AI systems into making incorrect decisions.

These attacks can be particularly problematic in applications like self-driving cars, where even a slight mistake can have serious consequences.

Another type of attack is the Poisoning Attack, which involves manipulating training data to corrupt the AI system's learning process.

This can lead to AI systems making biased or inaccurate decisions, which can have far-reaching consequences in fields like healthcare and finance.

Adversarial AI attacks can be broadly categorized into three main types: evasion attacks, data poisoning, and model extraction or stealing.

Evasion attacks are the most common type of attack variant, where attackers manipulate input data to trick machine learning algorithms into misclassifying them.

Data poisoning attacks occur when an attacker modifies the ML process by placing bad or poisoned data into a data set, making the outputs less accurate.

Model extraction or stealing attacks involve an attacker probing a target model for enough information or data to create an effective reconstruction of that model or steal data that was used to train the model.

There are several methods attackers can use to target a model. These methods take the following approaches:

Some specific attack types that can be used against machine learning systems include adversarial examples, Trojan attacks or backdoor attacks, model inversion, and membership inference.

There are several specific types of adversarial attacks that can be used against machine learning systems. These include Adversarial Examples, Trojan Attacks / Backdoor Attacks, Model Inversion, and Membership Inference.

Adversarial Examples are a type of attack where input data is manipulated to trick ML algorithms into misclassifying them. This can be done by introducing subtle yet deliberate noise or perturbations into the input data.

Trojan Attacks / Backdoor Attacks involve an attacker modifying the ML process by placing bad or poisoned data into a data set, making the outputs less accurate. The goal of a poisoning attack is to compromise the machine learning process and minimize the algorithm's usefulness.

Model Inversion attacks aim to extract sensitive information from AI models by exploiting their response patterns. These adversaries reverse-engineer the models, uncovering valuable insights that were intended to remain hidden.

Membership Inference is a targeted model extraction attack, which infers the owner of a data point, often by leveraging the overfitting resulting from poor machine learning practices.

Here are some common methods attackers use to target a model:

Deep reinforcement learning is a research area that focuses on the vulnerabilities of learned policies. This area has shown that reinforcement learning policies are susceptible to imperceptible adversarial manipulations.

Some studies have proposed methods to overcome these susceptibilities, but recent research has shown that these solutions are far from providing an accurate representation of current vulnerabilities of deep reinforcement learning policies.

Linear models have been found to be effective in analyzing adversarial attacks. They allow for simplified computation of adversarial attacks in linear regression and classification problems.

In fact, linear models can be used to explain the trade-off between robustness and accuracy. This is because they allow for analytical analysis while still reproducing phenomena observed in state-of-the-art models.

The analysis of linear models is also simplified because adversarial training is convex in this case. This makes it easier to work with linear models, as they are generally easier to analyze and understand.

One of the benefits of using linear models is that they can be used to explain complex phenomena in a simple and intuitive way. For example, they can be used to analyze the trade-off between robustness and accuracy, which is a crucial consideration in many machine learning applications.

Linear models have been used in a variety of applications, including tax avoidance and cybersecurity. In these applications, linear models have been used to model complex systems and identify potential vulnerabilities.

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The history of adversarial AI is fascinating, and it all started with a conference in 2004. John Graham-Cumming showed that a machine-learning spam filter could be used to defeat another machine-learning spam filter by automatically learning which words to add to a spam email to get the email classified as not spam.

In 2004, Nilesh Dalvi and others noted that linear classifiers used in spam filters could be defeated by simple "evasion attacks" as spammers inserted "good words" into their spam emails. This was a major wake-up call for the AI community.

The taxonomy of adversarial attacks was later developed, categorizing them along three primary axes: influence on the classifier, security violation, and specificity. Here's a breakdown of these axes:

These categories provide a framework for understanding the different types of adversarial attacks, and they've been extended to include dimensions for defense strategies against such attacks.

The history of adversarial machine learning is a fascinating topic. It all started in 2004 at the MIT Spam Conference, where John Graham-Cumming demonstrated that a machine-learning spam filter could be used to defeat another machine-learning spam filter.

In the early 2000s, researchers began to realize that machine-learning models could be vulnerable to attacks. Nilesh Dalvi and others noted that linear classifiers used in spam filters could be defeated by simple "evasion attacks" as spammers inserted "good words" into their spam emails.

By 2006, researchers had started to outline a broad taxonomy of attacks, as seen in Marco Barreno's publication "Can Machine Learning Be Secure?". This marked a turning point in the field, as experts began to understand the scope of the problem.

The 2010s saw significant advancements in machine learning, with deep neural networks becoming increasingly popular. However, this also made them more susceptible to attacks. In 2012, deep neural networks began to dominate computer vision problems, but by 2014, Christian Szegedy and others had demonstrated that they could be fooled by adversaries using a gradient-based attack.

As researchers continued to push the boundaries of machine learning, they also discovered new ways to defend against attacks. Today, big tech companies like Microsoft and Google are taking preventive measures, such as making their code open source to assist in detecting vulnerabilities.

Machine learning algorithms have been categorized along three primary axes: influence on the classifier, security violation, and specificity. These categories help us understand the different types of attacks that can occur.

An attack can influence the classifier by disrupting the classification phase, which may be preceded by an exploration phase to identify vulnerabilities. The attacker's capabilities might be restricted by the presence of data manipulation constraints.

Security violations occur when an attack supplies malicious data that gets classified as legitimate. This can cause legitimate data to be rejected after training.

Attacks can be targeted or indiscriminate. A targeted attack attempts to allow a specific intrusion or disruption, while an indiscriminate attack creates general mayhem.

This taxonomy has been extended to include dimensions for defense strategies against adversarial attacks.