There is a wide range of interdisciplinary intersections between cyber security and artificial intelligence(AI). On one hand, AI technologies, such as deep learning, can be introduced into cyber security to construct ...There is a wide range of interdisciplinary intersections between cyber security and artificial intelligence(AI). On one hand, AI technologies, such as deep learning, can be introduced into cyber security to construct smart models for implementing malware classification and intrusion detection and threating intelligence sensing. On the other hand, AI models will face various cyber threats, which will disturb their sample, learning, and decisions. Thus, AI models need specific cyber security defense and protection technologies to combat adversarial machine learning, preserve privacy in machine learning, secure federated learning, etc. Based on the above two aspects, we review the intersection of AI and cyber security. First, we summarize existing research efforts in terms of combating cyber attacks using AI, including adopting traditional machine learning methods and existing deep learning solutions. Then, we analyze the counterattacks from which AI itself may suffer, dissect their characteristics, and classify the corresponding defense methods. Finally, from the aspects of constructing encrypted neural network and realizing a secure federated deep learning, we expatiate the existing research on how to build a secure AI system.展开更多
The advancement of technology and the digitization of organizational functions and services have propelled the world into a new era of computing capability and sophistication. The proliferation and usability of such c...The advancement of technology and the digitization of organizational functions and services have propelled the world into a new era of computing capability and sophistication. The proliferation and usability of such complex technological services raise several security concerns. One of the most critical concerns is cross-site scripting (XSS) attacks. This paper has concentrated on revealing and comprehensively analyzing XSS injection attacks, detection, and prevention concisely and accurately. I have done a thorough study and reviewed several research papers and publications with a specific focus on the researchers’ defensive techniques for preventing XSS attacks and subdivided them into five categories: machine learning techniques, server-side techniques, client-side techniques, proxy-based techniques, and combined approaches. The majority of existing cutting-edge XSS defensive approaches carefully analyzed in this paper offer protection against the traditional XSS attacks, such as stored and reflected XSS. There is currently no reliable solution to provide adequate protection against the newly discovered XSS attack known as DOM-based and mutation-based XSS attacks. After reading all of the proposed models and identifying their drawbacks, I recommend a combination of static, dynamic, and code auditing in conjunction with secure coding and continuous user awareness campaigns about XSS emerging attacks.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61431008 and 61571300)
文摘There is a wide range of interdisciplinary intersections between cyber security and artificial intelligence(AI). On one hand, AI technologies, such as deep learning, can be introduced into cyber security to construct smart models for implementing malware classification and intrusion detection and threating intelligence sensing. On the other hand, AI models will face various cyber threats, which will disturb their sample, learning, and decisions. Thus, AI models need specific cyber security defense and protection technologies to combat adversarial machine learning, preserve privacy in machine learning, secure federated learning, etc. Based on the above two aspects, we review the intersection of AI and cyber security. First, we summarize existing research efforts in terms of combating cyber attacks using AI, including adopting traditional machine learning methods and existing deep learning solutions. Then, we analyze the counterattacks from which AI itself may suffer, dissect their characteristics, and classify the corresponding defense methods. Finally, from the aspects of constructing encrypted neural network and realizing a secure federated deep learning, we expatiate the existing research on how to build a secure AI system.
文摘The advancement of technology and the digitization of organizational functions and services have propelled the world into a new era of computing capability and sophistication. The proliferation and usability of such complex technological services raise several security concerns. One of the most critical concerns is cross-site scripting (XSS) attacks. This paper has concentrated on revealing and comprehensively analyzing XSS injection attacks, detection, and prevention concisely and accurately. I have done a thorough study and reviewed several research papers and publications with a specific focus on the researchers’ defensive techniques for preventing XSS attacks and subdivided them into five categories: machine learning techniques, server-side techniques, client-side techniques, proxy-based techniques, and combined approaches. The majority of existing cutting-edge XSS defensive approaches carefully analyzed in this paper offer protection against the traditional XSS attacks, such as stored and reflected XSS. There is currently no reliable solution to provide adequate protection against the newly discovered XSS attack known as DOM-based and mutation-based XSS attacks. After reading all of the proposed models and identifying their drawbacks, I recommend a combination of static, dynamic, and code auditing in conjunction with secure coding and continuous user awareness campaigns about XSS emerging attacks.
