{"title":"Session details: Session 3: Systematization of MTD 2","authors":"Hamed Okhravi","doi":"10.1145/3433235","DOIUrl":"https://doi.org/10.1145/3433235","url":null,"abstract":"","PeriodicalId":288218,"journal":{"name":"Proceedings of the 7th ACM Workshop on Moving Target Defense","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122441802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hengwei Zhang, Jinglei Tan, Xiaohu Liu, Jin-dong Wang
Today most of the moving target defense decision-making methods are based on models of a discrete dynamic game. To more accurately study network attack-defense strategies against continuous confrontations, we analyze offensive and defensive behavior from a dynamic perspective. We propose a moving target defense decision-making method based on a model of a dynamic Markov differential game. We implement dynamic analysis and deduction of multi-stage continuous attack and defense confrontations for scenarios of continuous real-time network attack-defense. We take into account the influence of random factors and changes of the network system in the gaming process, combine differential gaming with the Markov decision-making method, and construct models of attack-defense games. We propose a solution for game equilibrium based on an objective function designed according to the total discounted payoff of the offensive and defensive game and the analysis of the characteristics of multi-staged game equilibrium. On this basis an optimal strategy selection method is designed. We apply and verify the game model and the defense strategy selection algorithm by using the moving target defense technique. We conduct simulations to verify the effectiveness and feasibility of the model and algorithm.
{"title":"Moving Target Defense Decision-Making Method: A Dynamic Markov Differential Game Model","authors":"Hengwei Zhang, Jinglei Tan, Xiaohu Liu, Jin-dong Wang","doi":"10.1145/3411496.3421222","DOIUrl":"https://doi.org/10.1145/3411496.3421222","url":null,"abstract":"Today most of the moving target defense decision-making methods are based on models of a discrete dynamic game. To more accurately study network attack-defense strategies against continuous confrontations, we analyze offensive and defensive behavior from a dynamic perspective. We propose a moving target defense decision-making method based on a model of a dynamic Markov differential game. We implement dynamic analysis and deduction of multi-stage continuous attack and defense confrontations for scenarios of continuous real-time network attack-defense. We take into account the influence of random factors and changes of the network system in the gaming process, combine differential gaming with the Markov decision-making method, and construct models of attack-defense games. We propose a solution for game equilibrium based on an objective function designed according to the total discounted payoff of the offensive and defensive game and the analysis of the characteristics of multi-staged game equilibrium. On this basis an optimal strategy selection method is designed. We apply and verify the game model and the defense strategy selection algorithm by using the moving target defense technique. We conduct simulations to verify the effectiveness and feasibility of the model and algorithm.","PeriodicalId":288218,"journal":{"name":"Proceedings of the 7th ACM Workshop on Moving Target Defense","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Languages in the C family are distinguished by their efficiency, maturity, and their lack of guardrails compared to other mainstream language in use today. Their efficiency properties kept these languages relevant as new ones appeared. Their lack of memory safety and the resulting vulnerabilities is an ongoing challenge. Code randomization, a moving target defense technique, is one among many competing answers to this challenge. Many techniques have been proposed and evaluated extensively in academic conferences but adoption in the field is lagging. The goal of this paper is to highlight why adoption is so hard and what can be done about it. Code randomization techniques offer much flexibility in their design and implementation. We encourage research that investigates the complex trade-offs between security and many equally important concerns that must be made for enhanced code randomization defenses to make their way into production.
{"title":"Adoption Challenges of Code Randomization","authors":"Per Larsen, M. Franz","doi":"10.1145/3411496.3421226","DOIUrl":"https://doi.org/10.1145/3411496.3421226","url":null,"abstract":"Languages in the C family are distinguished by their efficiency, maturity, and their lack of guardrails compared to other mainstream language in use today. Their efficiency properties kept these languages relevant as new ones appeared. Their lack of memory safety and the resulting vulnerabilities is an ongoing challenge. Code randomization, a moving target defense technique, is one among many competing answers to this challenge. Many techniques have been proposed and evaluated extensively in academic conferences but adoption in the field is lagging. The goal of this paper is to highlight why adoption is so hard and what can be done about it. Code randomization techniques offer much flexibility in their design and implementation. We encourage research that investigates the complex trade-offs between security and many equally important concerns that must be made for enhanced code randomization defenses to make their way into production.","PeriodicalId":288218,"journal":{"name":"Proceedings of the 7th ACM Workshop on Moving Target Defense","volume":"224 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moving-Target Defenses seek to introduce dynamism, randomness, and diversity into computer systems in an effort to make these systems harder to explore, predict, and exploit. Over the past decade a variety of work has explored applying these kinds of defenses to applications? runtime environments, to the operating systems and architectures running the applications, and to networks. In this paper, we report on lessons learned from seven years of building and evaluating moving-target defenses, primarily for process memory layouts and networks. We identify six major lessons learned from our experience that we believe to be broadly applicable to moving-target defenses, focusing around the importance and impact of threat models and characteristics of effective moving-target defenses. We then offer suggestions for the future direction of the field based on our experience.
{"title":"Lessons Learned in Network and Memory-Based Moving Target Defenses","authors":"R. Skowyra, Samuel Jero","doi":"10.1145/3411496.3421227","DOIUrl":"https://doi.org/10.1145/3411496.3421227","url":null,"abstract":"Moving-Target Defenses seek to introduce dynamism, randomness, and diversity into computer systems in an effort to make these systems harder to explore, predict, and exploit. Over the past decade a variety of work has explored applying these kinds of defenses to applications? runtime environments, to the operating systems and architectures running the applications, and to networks. In this paper, we report on lessons learned from seven years of building and evaluating moving-target defenses, primarily for process memory layouts and networks. We identify six major lessons learned from our experience that we believe to be broadly applicable to moving-target defenses, focusing around the importance and impact of threat models and characteristics of effective moving-target defenses. We then offer suggestions for the future direction of the field based on our experience.","PeriodicalId":288218,"journal":{"name":"Proceedings of the 7th ACM Workshop on Moving Target Defense","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 7th ACM Workshop on Moving Target Defense","authors":"","doi":"10.1145/3411496","DOIUrl":"https://doi.org/10.1145/3411496","url":null,"abstract":"","PeriodicalId":288218,"journal":{"name":"Proceedings of the 7th ACM Workshop on Moving Target Defense","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：