Matching Items (3)
Description
Web applications continue to remain as the most popular method of interaction for businesses over the Internet. With it's simplicity of use and management, they often function as the "front door" for many companies. As such, they are a critical component of the security ecosystem as vulnerabilities present in these systems could potentially allow malicious users access to sensitive business and personal data.
The inherent nature of web applications enables anyone to access them anytime and anywhere, this includes any malicious actors looking to exploit vulnerabilities present in the web application. In addition, the static configurations of these web applications enables attackers the opportunity to perform reconnaissance at their leisure, increasing their success rate by allowing them time to discover information on the system. On the other hand, defenders are often at a disadvantage as they do not have the same temporal opportunity that attackers possess in order to perform counter-reconnaissance. Lastly, the unchanging nature of web applications results in undiscovered vulnerabilities to remain open for exploitation, requiring developers to adopt a reactive approach that is often delayed or to anticipate and prepare for all possible attacks which is often cost-prohibitive.
Moving Target Defense (MTD) seeks to remove the attackers' advantage by reducing the information asymmetry between the attacker and defender. This research explores the concept of MTD and the various methods of applying MTD to secure Web Applications. In particular, MTD concepts are applied to web applications by implementing an automated application diversifier that aims to mitigate specific classes of web application vulnerabilities and exploits. Evaluation is done using two open source web applications to determine the effectiveness of the MTD implementation. Though developed for the chosen applications, the automation process can be customized to fit a variety of applications.
The inherent nature of web applications enables anyone to access them anytime and anywhere, this includes any malicious actors looking to exploit vulnerabilities present in the web application. In addition, the static configurations of these web applications enables attackers the opportunity to perform reconnaissance at their leisure, increasing their success rate by allowing them time to discover information on the system. On the other hand, defenders are often at a disadvantage as they do not have the same temporal opportunity that attackers possess in order to perform counter-reconnaissance. Lastly, the unchanging nature of web applications results in undiscovered vulnerabilities to remain open for exploitation, requiring developers to adopt a reactive approach that is often delayed or to anticipate and prepare for all possible attacks which is often cost-prohibitive.
Moving Target Defense (MTD) seeks to remove the attackers' advantage by reducing the information asymmetry between the attacker and defender. This research explores the concept of MTD and the various methods of applying MTD to secure Web Applications. In particular, MTD concepts are applied to web applications by implementing an automated application diversifier that aims to mitigate specific classes of web application vulnerabilities and exploits. Evaluation is done using two open source web applications to determine the effectiveness of the MTD implementation. Though developed for the chosen applications, the automation process can be customized to fit a variety of applications.
ContributorsTaguinod, Marthony (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Yau, Sik-Sang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Today the information technology systems have addresses, software stacks and other configuration remaining unchanged for a long period of time. This paves way for malicious attacks in the system from unknown vulnerabilities. The attacker can take advantage of this situation and plan their attacks with sufficient time. To protect our system from this threat, Moving Target Defense is required where the attack surface is dynamically changed, making it difficult to strike.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.
ContributorsBohara, Bhakti (Author) / Huang, Dijiang (Thesis advisor) / Doupe, Adam (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
The field of cyber-defenses has played catch-up in the cat-and-mouse game of finding vulnerabilities followed by the invention of patches to defend against them. With the complexity and scale of modern-day software, it is difficult to ensure that all known vulnerabilities are patched; moreover, the attacker, with reconnaissance on their side, will eventually discover and leverage them. To take away the attacker's inherent advantage of reconnaissance, researchers have proposed the notion of proactive defenses such as Moving Target Defense (MTD) in cyber-security. In this thesis, I make three key contributions that help to improve the effectiveness of MTD.
First, I argue that naive movement strategies for MTD systems, designed based on intuition, are detrimental to both security and performance. To answer the question of how to move, I (1) model MTD as a leader-follower game and formally characterize the notion of optimal movement strategies, (2) leverage expert-curated public data and formal representation methods used in cyber-security to obtain parameters of the game, and (3) propose optimization methods to infer strategies at Strong Stackelberg Equilibrium, addressing issues pertaining to scalability and switching costs. Second, when one cannot readily obtain the parameters of the game-theoretic model but can interact with a system, I propose a novel multi-agent reinforcement learning approach that finds the optimal movement strategy. Third, I investigate the novel use of MTD in three domains-- cyber-deception, machine learning, and critical infrastructure networks. I show that the question of what to move poses non-trivial challenges in these domains. To address them, I propose methods for patch-set selection in the deployment of honey-patches, characterize the notion of differential immunity in deep neural networks, and develop optimization problems that guarantee differential immunity for dynamic sensor placement in power-networks.
First, I argue that naive movement strategies for MTD systems, designed based on intuition, are detrimental to both security and performance. To answer the question of how to move, I (1) model MTD as a leader-follower game and formally characterize the notion of optimal movement strategies, (2) leverage expert-curated public data and formal representation methods used in cyber-security to obtain parameters of the game, and (3) propose optimization methods to infer strategies at Strong Stackelberg Equilibrium, addressing issues pertaining to scalability and switching costs. Second, when one cannot readily obtain the parameters of the game-theoretic model but can interact with a system, I propose a novel multi-agent reinforcement learning approach that finds the optimal movement strategy. Third, I investigate the novel use of MTD in three domains-- cyber-deception, machine learning, and critical infrastructure networks. I show that the question of what to move poses non-trivial challenges in these domains. To address them, I propose methods for patch-set selection in the deployment of honey-patches, characterize the notion of differential immunity in deep neural networks, and develop optimization problems that guarantee differential immunity for dynamic sensor placement in power-networks.
ContributorsSengupta, Sailik (Author) / Kambhampati, Subbarao (Thesis advisor) / Bao, Tiffany (Youzhi) (Committee member) / Huang, Dijiang (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2020