Flow Reconnaissance via Timing Attacks on SDN Switches

Liu, Sheng; Reiter, Michael K.; Sekar, Vyas

doi:10.1109/icdcs.2017.281

Cited by 30 publications

(8 citation statements)

References 15 publications

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“…First, network switches (including software switches) can be compromised. Actually, many serious vulnerabilities have been found in SDN-enabled switches [3], [2], [36], [26]. Second, in many cases, attackers might not need to compromise network switches; instead, they just compromise normal hosts or virtual machines to launch attacks.…”

Section: A Scenariomentioning

confidence: 99%

“…There are existing studies that target the data plane to launch attacks[36],[26],[13]; however, such attacks are not the focus of this paper.2 Many real-world networks take this in-band operation approach due to its cost-efficiency[12],[42].3 Note that the control channel does not enforce SSL/TLS by default[9],[8], and it can be very complex to manage in real-world deployments especially when there are multiple controllers[32].…”

mentioning

confidence: 99%

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Unexpected Data Dependency Creation and Chaining: A New Attack to SDN

Xiao

Zhang

Huang

et al. 2020

2020 IEEE Symposium on Security and Privacy (SP)

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Software-Defined Networking (SDN) is an emerging network architecture that provides programmable networking through a logically centralized controller. As SDN becomes more prominent, its security vulnerabilities become more evident than ever. Serving as the "brain" of a software-defined network, how the control plane (of the network) is exposed to external inputs (i.e., data plane messages) is directly correlated with how secure the network is. Fortunately, due to some unique SDN design choices (e.g., control plane and data plane separation), attackers often struggle to find a reachable path to those vulnerable logic hidden deeply within the control plane.In this paper, we demonstrate that it is possible for a weak adversary who only controls a commodity network device (host or switch) to attack previously unreachable control plane components by maliciously increasing reachability in the control plane. We introduce D 2 C 2 (data dependency creation and chaining) attack, which leverages some widely-used SDN protocol features (e.g., custom fields) to create and chain unexpected data dependencies in order to achieve greater reachability. We have developed a novel tool, SVHunter, which can effectively identify D 2 C 2 vulnerabilities. Till now we have evaluated SVHunter on three mainstream open-source SDN controllers (i.e., ONOS, Floodlight, and Opendaylight) as well as one security-enhanced controller (i.e., SE-Floodlight). SVHunter detects 18 previously unknown vulnerabilities, all of which can be exploited remotely to launch serious attacks such as executing arbitrary commands, exfiltrating confidential files, and crashing SDN services.

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Section: A Scenariomentioning

confidence: 99%

mentioning

confidence: 99%

Unexpected Data Dependency Creation and Chaining: A New Attack to SDN

Xiao

Zhang

Huang

et al. 2020

2020 IEEE Symposium on Security and Privacy (SP)

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“…Finally, the flow rules information can be captured from the switch via side channel attacking techniques. 63 Network topology: the controller builds up its topology record by looking at the topology protocol's packet, for example, LLDP (Link Layer Discovery Protocol), IGMP (Internet Group Management Protocol), and ARP (Address Resolution Protocol) generated by the switches.…”

Section: Security In Sdnmentioning

confidence: 99%

“…Finally, the flow rules information can be captured from the switch via side channel attacking techniques. 63…”

Section: Security Threats and Challenges To Sdwsnmentioning

confidence: 99%

A critical review on security approaches to software-defined wireless sensor networking

Saqib

Khan

Ahmed

et al. 2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are very prone to ongoing security threats due to its resource constraints and unprotected transmission medium. WSN contains hundreds and thousands of resource-constrained and self-organized sensor nodes. These sensor nodes are usually organized in a distributed manner; thus, it permits the creation of an ad hoc network without predefined infrastructure or centralized management. As WSNs are going to get control of real-time applications, where a malicious activity can cause serious damage, the inherent challenge is to fortify the security enforcement in these networks. As a solution, software-defined network (SDN) has come out and has been merged with WSN to form what is known as software-defined wireless sensor network (SDWSN). SDWSN has come into existence, and it legitimizes network operators with more flexibility and control over the network. SDWSN has more tightened the security enforcement based on the global view and centralized control of the network topology. Moreover, machine learning (ML)–based and deep learning (DL)–based network intrusion detection systems (NIDS) have been introduced to the SDN environment to protect the networks against anomaly threats. In this review article, we illustrated the SDN–based security approaches to WSN followed by its architectures, advantages, and possible security threats. Finally, ML/DL–based NIDS integrated with the SDN controller is proposed as a complete solution for the WSN environment to confront the ongoing anomaly threats and to sufficiently protect the network against both known and unknown attacks.

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“…If |ΔRTT| ≫ 0, it can be speculated that the target network is an SDN and the detection packet leads to the installation of flow table rules. Furthermore, the attacker can infer more fingerprint information such as flow matching rules of the target network by carefully constructing detection packets [2][3][4], thereby exposing the entire SDN to a sea of threats as well as laying the foundation for the attacker to carry out the next more threatening and precise attack, such as Distributed Denial of Service (DDoS) attacks through forged data packets, etc. Panjwani et al concluded that up to 70% of network attacks were carried out after fingerprint attacks [5].…”

Section: Introductionmentioning

confidence: 99%

Research on SDN Fingerprint Attack Defense Mechanism Based on Dynamic Disturbance and Information Entropy Detection

Fang

Zhang

Lin

2022

Security and Communication Networks

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As an emerging type of network architecture, SDN is widespread used and security issues have also received more and more attention. Fingerprint attacks represent one of the most significant threats to network security. Attackers obtain key fingerprint information of the target network, which lays the foundation for subsequent more threatening attacks. Currently, research on domestic and international SDN fingerprint attacks focuses on how to attack, and less research is being done on how to defend against fingerprinting attacks. This paper proposes a mechanism for defending fingerprint attacks that combines dynamic disturbance and information entropy detection. This mechanism adopts the principle of fingerprint attack, combined with a moving average algorithm, Bloom Filter, and packet delay tool, to confuse opponents by disturbing a small number of packets, simultaneously, combined with the information entropy detection to make real-time processing feedback to the network. The experimental results show that this mechanism works effectively to defend SDNs against fingerprint attacks without affecting the normal network communication.

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Flow Reconnaissance via Timing Attacks on SDN Switches

Cited by 30 publications

References 15 publications

Unexpected Data Dependency Creation and Chaining: A New Attack to SDN

Unexpected Data Dependency Creation and Chaining: A New Attack to SDN

A critical review on security approaches to software-defined wireless sensor networking

Research on SDN Fingerprint Attack Defense Mechanism Based on Dynamic Disturbance and Information Entropy Detection

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