Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyberattacks more than ever. The complexity and dynamics of cyberattacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This article presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multiagent DRL-based game theory simulations for defense strategies against cyberattacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.

pdf/deep-reinforcement-learning-for-cyber-security-1tymm8gk3f.pdf

Deep Reinforcement Learning for Cyber Security.

A deep reinforcement learning (DRL) agent observes its states through observations, whichmay contain natural measurement errors or adversarial noises. Since the observations deviatefrom the true states, they can mislead the agent into making suboptimal actions. Several workshave shown this vulnerability via adversarial attacks, but existing approaches on improving therobustness of DRL under this setting have limited success and lack for theoretical principles. Weshow that naively applying existing techniques on improving robustness for classification tasks,like adversarial training, are ineffective for many RL tasks. We propose the state-adversarialMarkov decision process (SA-MDP) to study the fundamental properties of this problem, anddevelop a theoretically principled policy regularization which can be applied to a large familyof DRL algorithms, including proximal policy optimization (PPO), deep deterministic policygradient (DDPG) and deep Q networks (DQN), for both discrete and continuous action controlproblems. We significantly improve the robustness of PPO, DDPG and DQN agents under asuite of strong white box adversarial attacks, including new attacks of our own. Additionally, wefind that a robust policy noticeably improves DRL performance even without an adversary in anumber of environments. Our code is available at this https URL

/pdf/robust-deep-reinforcement-learning-against-adversarial-dspe5m9f57.pdf

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

The increasing computational power and proliferation of big data are now empowering Artificial Intelligence (AI) to achieve massive adoption and applicability in many fields. The lack of explanation when it comes to the decisions made by today's AI algorithms is a major drawback in critical decision‐making systems. For example, deep learning does not offer control or reasoning over its internal processes or outputs. More importantly, current black‐box AI implementations are subject to bias and adversarial attacks that may poison the learning or the inference processes. Explainable AI (XAI) is a new trend of AI algorithms that provide explanations of their AI decisions. In this paper, we propose a framework for achieving a more trustworthy and XAI by leveraging features of blockchain, smart contracts, trusted oracles, and decentralized storage. We specify a framework for complex AI systems in which the decision outcomes are reached based on decentralized consensuses of multiple AI and XAI predictors. The paper discusses how our proposed framework can be utilized in key application areas with practical use cases.

Blockchain for explainable and trustworthy artificial intelligence

Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.

/pdf/adversarial-attack-and-defense-in-reinforcement-learning-2x7gcvpw7p.pdf

Adversarial attack and defense in reinforcement learning-from AI security view

Reinforcement learning is a core learning approach of machine learning in artificial intelligence, which has been widely used to realize the module of automatic path finding for robots. However, recent researches have shown that the designed adversarial examples can make an algorithm-level attack on reinforcement learning in the Atari game. Obviously, such attack may be also brought into the automatic path finding based on reinforcement learning. In this paper, we focus on the adversarial example-based attack on a representative reinforcement learning named Q-learning in automatic path finding. We propose a probabilistic output model based on the influence factors and the corresponding weights to predict the adversarial examples. Through calculation on five factors including the energy point gravitation, the key point gravitation, the path gravitation, the included angle and the placid point, a natural linear model is constructed to fit these factors with the weight parameters computation based on the principal component analysis(PCA). Through massive experiments, we successfully find the adversarial examples for the first time on Q-learning in path finding and our model can make a satisfactory prediction. Under a guaranteed recall, the precision of the proposed model can reach to 70% with the proper parameter setting.

A PCA-Based Model to Predict Adversarial Examples on Q-Learning of Path Finding

As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial examples are effective when targeting A3C path finding, and design a Common Dominant Adversarial Examples Generation Method (CDG) to generate dominant adversarial examples against any given map. In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples. Extensive experimental results show that, the lowest generation precision for CDG algorithm is 91.91%, and the lowest immune precision for Gradient Band-based Adversarial Training is 93.89%, which can prove that our method can realize the generalized attack immunity of A3C path finding with a high confidence.

pdf/gradient-band-based-adversarial-training-for-generalized-49euo3volb.pdf

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

Reinforcement Learning has been used on path planning for a long time, which is thought to be very effective, especially the Value Iteration Networks (VIN) with strong generalization ability. In this paper, we analyze the path planning of VIN and propose a method that can effectively find vulnerable points in VIN. We build a 2D navigation task to test our method. The experiment for interfering VIN is conducted for the first time. The experimental results show that our method has good performance on finding vulnerabilities and could automatically adding obstacles to obstruct VIN path planning.

A Method to Effectively Detect Vulnerabilities on Path Planning of VIN

"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused huge losses inadvertently. However, to our knowledge, all previous works use common methods such as Causal-Correlation or Cased-based to detect outdated multi-stage attacks. Few attentions have been paid to detect the "Feint Attack", because the difficulty of detection lies in the diversification of the concept of "Feint Attack" and the lack of professional datasets, many detection methods ignore the semantic relationship in the attack. Aiming at the existing challenge, this paper explores a new method to solve the problem. In the attack scenario, the fuzzy clustering method based on attribute similarity is used to mine multi-stage attack chains. Then we use a few-shot deep learning algorithm (SMOTE&CNN-SVM) and bidirectional Recurrent Neural Network model (Bi-RNN) to obtain the "Feint Attack" chains. "Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain. So we used Bi-RNN coding to obtain the hidden feature of "Feint Attack" chain. In the end, our method achieved the goal to detect the "Feint Attack" accurately by using the LLDoS1.0 and LLDoS2.0 of DARPA2000 and CICIDS2017 of Canadian Institute for Cybersecurity.

Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

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