36 research outputs found

    PROVIDE: hiding from automated network scans with proofs of identity

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    Network scanners are a valuable tool for researchers and administrators, however they are also used by malicious actors to identify vulnerable hosts on a network. Upon the disclosure of a security vulnerability, scans are launched within hours. These opportunistic attackers enumerate blocks of IP addresses in hope of discovering an exploitable host. Fortunately, defensive measures such as port knocking protocols (PKPs) allow a service to remain stealth to unauthorized IP addresses. The service is revealed only when a client includes a special authentication token (AT) in the IP/TCP header. However this AT is generated from a secret shared between the clients/servers and distributed manually to each endpoint. As a result, these defense measures have failed to be widely adopted by other protocols such as HTTP/S due to challenges in distributing the shared secrets. In this paper we propose a scalable solution to this problem for services accessed by domain name. We make the following observation: automated network scanners access servers by IP address, while legitimate clients access the server by name. Therefore a service should only reveal itself to clients who know its name. Based on this principal, we have created a proof of the verifier’s identity (a.k.a. PROVIDE) protocol that allows a prover (legitimate user) to convince a verifier (service) that it is knowledgeable of the verifier’s identity. We present a PROVIDE implementation using a PKP and DNS (PKP+DNS) that uses DNS TXT records to distribute identification tokens (IDT) while DNS PTR records for the service’s domain name are prohibited to prevent reverse DNS lookups. Clients are modified to make an additional DNS TXT query to obtain the IDT which is used by the PKP to generate an AT. The inclusion of an AT in the packet header, generated from the DNS TXT query, is proof the client knows the service’s identity. We analyze the effectiveness of this mechanism with respect to brute force attempts for various strength ATs and discuss practical considerations.This work has been supported by the National Science Foundation (NSF) awards #1430145, #1414119, and #1012798

    Towards automated distributed containment of zero-day network worms

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    Worms are a serious potential threat to computer network security. The high potential speed of propagation of worms and their ability to self-replicate make them highly infectious. Zero-day worms represent a particularly challenging class of such malware, with the cost of a single worm outbreak estimated to be as high as US$2.6 Billion. In this paper, we present a distributed automated worm detection and containment scheme that is based on the correlation of Domain Name System (DNS) queries and the destination IP address of outgoing TCP SYN and UDP datagrams leaving the network boundary. The proposed countermeasure scheme also utilizes cooperation between different communicating scheme members using a custom protocol, which we term Friends. The absence of a DNS lookup action prior to an outgoing TCP SYN or UDP datagram to a new destination IP addresses is used as a behavioral signature for a rate limiting mechanism while the Friends protocol spreads reports of the event to potentially vulnerable uninfected peer networks within the scheme. To our knowledge, this is the first implementation of such a scheme. We conducted empirical experiments across six class C networks by using a Slammer-like pseudo-worm to evaluate the performance of the proposed scheme. The results show a significant reduction in the worm infection, when the countermeasure scheme is invoked

    Model-driven situational awareness for moving target defense

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    Moving Target Defense (MTD) presents dynamically changing attack surfaces and system configurations to attackers. This approach decreases the success probabilities of attacks and increases attacker's workload since she must continually re-assess, re-engineer and re-launch her attacks. Existing research has provided a number of MTD techniques but approaches for gaining situational awareness and deciding when/how to apply these techniques are not well studied. In this paper, we present a conceptual framework that closely integrates a set of models with the system and obtains up-to-date situational awareness following the OODA loop methodology. To realize the framework, as the first step, we propose a modelling approach that provides insights about the dynamics between potential attacks and defenses, impact of attacks and adaptations on the system, and the state of the system. Based on these models, we demonstrate techniques to quantitatively assess the effectiveness of MTD and show how to formulate decision-making problems

    Geometry-based Detection of Flash Worms

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    While it takes traditional internet worms hours to infect all the vulnerable hosts on the Internet, a flash worm takes seconds. Because of the rapid rate with which flash worms spread, the existing worm defense mechanisms cannot respond fast enough to detect and stop the flash worm infections. In this project, we propose a geometric-based detection mechanism that can detect the spread of flash worms in a short period of time. We tested the mechanism on various simulated flash worm traffics consisting of more than 10,000 nodes. In addition to testing on flash worm traffics, we also tested the mechanism on non-flash worm traffics to see if our detection mechanism produces false alarms. In order to efficiently analyze bulks of various network traffics, we implemented an application that can be used to convert the network traffic data into graphical notations. Using the application, the analysis can be done graphically as it displays the large amount of network relationships as tree structures

    Characterizing the Power of Moving Target Defense via Cyber Epidemic Dynamics

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    Moving Target Defense (MTD) can enhance the resilience of cyber systems against attacks. Although there have been many MTD techniques, there is no systematic understanding and {\em quantitative} characterization of the power of MTD. In this paper, we propose to use a cyber epidemic dynamics approach to characterize the power of MTD. We define and investigate two complementary measures that are applicable when the defender aims to deploy MTD to achieve a certain security goal. One measure emphasizes the maximum portion of time during which the system can afford to stay in an undesired configuration (or posture), without considering the cost of deploying MTD. The other measure emphasizes the minimum cost of deploying MTD, while accommodating that the system has to stay in an undesired configuration (or posture) for a given portion of time. Our analytic studies lead to algorithms for optimally deploying MTD.Comment: 12 pages; 4 figures; Hotsos 14, 201

    Алгоритм ΠΈ тСхничСскиС Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ динамичСского конфигурирования ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй

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    ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ основныС Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹, ΠΎΠ±ΡƒΡΠ»Π°Π²Π»ΠΈΠ²Π°ΡŽΡ‰ΠΈΠ΅ Ρ€Π°ΡΡˆΠΈΡ€Π΅Π½ΠΈΠ΅ возмоТностСй ΠΈ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈΠ²Π½ΠΎΡΡ‚ΠΈ сСтСвой Ρ€Π°Π·Π²Π΅Π΄ΠΊΠΈ ΠΏΠΎ ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ состава ΠΈ структуры ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй вслСдствиС стационарности ΠΈΡ… структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик. ВскрытыС особСнности Π·Π°Ρ‰ΠΈΡ‚Ρ‹ ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй, основанных Π½Π° Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΎΠ² пространствСнного обСспСчСния бСзопасности, Π° Ρ‚Π°ΠΊΠΆΠ΅ формализация ΠΈ Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΠ΅ мноТСства Π·Π°ΠΏΡ€Π΅Ρ‰Π°ΡŽΡ‰ΠΈΡ… Ρ€Π΅Π³Π»Π°ΠΌΠ΅Π½Ρ‚ΠΎΠ² ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Ρ‹Π²Π°ΡŽΡ‚ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ Π·Π°Π΄Π°Ρ‡ΠΈ динамичСского управлСния структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ характСристиками ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй, Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½ΠΈΡ€ΡƒΡŽΡ‰ΠΈΡ… Π² условиях сСтСвой Ρ€Π°Π·Π²Π΅Π΄ΠΊΠΈ. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Π° матСматичСская модСль, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰Π°Ρ Π½Π°Ρ…ΠΎΠ΄ΠΈΡ‚ΡŒ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹Π΅ Ρ€Π΅ΠΆΠΈΠΌΡ‹ динамичСского конфигурирования структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй для Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ситуаций. ΠŸΡ€ΠΈΠ²Π΅Π΄Π΅Π½Ρ‹ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ расчСтов. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ‡ΠΈ динамичСской ΠΊΠΎΠ½Ρ„ΠΈΠ³ΡƒΡ€Π°Ρ†ΠΈΠΈ структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрной Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ сСти, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΠΉ ΡƒΠΌΠ΅Π½ΡŒΡˆΠ΅Π½ΠΈΠ΅ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ достовСрности Π΄ΠΎΠ±Ρ‹Π²Π°Π΅ΠΌΡ‹Ρ… сСтСвой Ρ€Π°Π·Π²Π΅Π΄ΠΊΠΎΠΉ Π΄Π°Π½Π½Ρ‹Ρ…. ΠŸΠΎΠΊΠ°Π·Π°Π½Ρ‹ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ практичСских испытаний Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠ³ΠΎ Π½Π° основС Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° динамичСского конфигурирования ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠ³ΠΎ обСспСчСния. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΡΠ²ΠΈΠ΄Π΅Ρ‚Π΅Π»ΡŒΡΡ‚Π²ΡƒΡŽΡ‚, Ρ‡Ρ‚ΠΎ использованиС прСдставлСнного Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΏΠΎ динамичСскому ΠΊΠΎΠ½Ρ„ΠΈΠ³ΡƒΡ€ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡŽ ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй позволяСт ΠΏΠΎΠ²Ρ‹ΡΠΈΡ‚ΡŒ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ Π·Π°Ρ‰ΠΈΡ‚Ρ‹ Π·Π° счСт измСнСния структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй Π² Ρ€Π°ΠΌΠΊΠ°Ρ… Π½Π΅ΡΠΊΠΎΠ»ΡŒΠΊΠΈΡ… подсСтСй. ΠŸΡ€ΠΈ этом достигнуто ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠ°Π½ΠΈΠ΅ критичСски Π²Π°ΠΆΠ½Ρ‹Ρ… соСдинСний, Π° ΠΈΠ½Ρ‚Π΅Ρ€Π²Π°Π»Ρ‹ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ измСнСния структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½Ρ‹ ΠΊ условиям функционирования ΠΈ дСйствиям Π·Π»ΠΎΡƒΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΈΠΊΠ°. Новизна Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π·Π°ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ΡΡ Π² ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ матСматичСского Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚Π° Ρ‚Π΅ΠΎΡ€ΠΈΠΈ марковских случайных процСссов ΠΈ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ ΡƒΡ€Π°Π²Π½Π΅Π½ΠΈΠΉ ΠšΠΎΠ»ΠΌΠΎΠ³ΠΎΡ€ΠΎΠ²Π° для обоснования Π²Ρ‹Π±ΠΎΡ€Π° Ρ€Π΅ΠΆΠΈΠΌΠΎΠ² динамичСского конфигурирования структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй. Новизна Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° состоит Π² ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ динамичСского конфигурирования структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрных Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… сСтСй для динамичСского управлСния структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ характСристиками ΠΊΠ»ΠΈΠ΅Π½Ρ‚-сСрвСрной Π²Ρ‹Ρ‡ΠΈΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ сСти Π² условиях сСтСвой Ρ€Π°Π·Π²Π΅Π΄ΠΊΠΈ

    Evaluating the Effectiveness of IP Hopping via an Address Routing Gateway

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    This thesis explores the viability of using Internet Protocol (IP) address hopping in front of a network as a defensive measure. This research presents a custom gateway-based IP hopping solution called Address Routing Gateway (ARG) that acts as a transparent IP address hopping gateway. This thesis tests the overall stability of ARG, the accuracy of its classifications, the maximum throughput it can support, and the maximum rate at which it can change IPs and still communicate reliably. This research is accomplished on a physical test network with nodes representing the types of hosts found on a typical, corporate-style network. Direct measurement is used to obtain all results for each factor level. Tests demonstrate ARG classifies traffic correctly, with no false negatives and less than a 0.15% false positive rate on average. The test environment conservatively shows this to be true as long as the IP address change interval exceeds two times the network\u27s round-trip latency; real-world deployments may allow for more frequent hopping. Results show ARG capably handles traffic of at least four megabits per second with no impact on packet loss. Fuzz testing validates the stability of ARG itself, although additional packet loss of around 23% appears when under attack
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