Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. Many IFA detection and mitigation solutions have been proposed in the literature. However, there is no comprehensive review study of these solutions that has been proposed so far. Therefore, in this paper, we propose a survey of the various IFAs with a detailed comparative study of all the relevant proposed solutions as counter-measures against IFAs. We also review the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms through a series of attack scenarios. Finally, in this survey, we offer an analysis of the open issues and future research directions regarding IFAs.
more »
« less
PERSIA: a PuzzlE-based InteReSt FloodIng Attack Countermeasure
With the proliferation of smart and connected mobile, wireless devices at the edge, Distributed Denial of Service (DDoS) attacks are increasing. Weak security, improper commissioning, and the fast, non-standardized growth of the IoT industry are the major contributors to the recent DDoS attacks, e.g., Mirai Botnet attack on Dyn and Memcached attack on GitHub. Similar to UDP/TCP flooding (common DDoS attack vector), request flooding attack is the primary DDoS vulnerability in the Named-Data Networking (NDN) architecture.In this paper, we propose PERSIA, a distributed request flooding prevention and mitigation framework for NDN-enabled ISPs, to ward-off attacks at the edge. PERSIA's edge-centric attack prevention mechanism eliminates the possibility of successful attacks from malicious end hosts. In the presence of compromised infrastructure (routers), PERSIA dynamically deploys an in-network mitigation strategy to minimize the attack's magnitude. Our experimentation demonstrates PERSIA's resiliency and effectiveness in preventing and mitigating DDoS attacks while maintaining legitimate users' quality of experience (> 99.92% successful packet delivery rate).
more »
« less
- NSF-PAR ID:
- 10208723
- Date Published:
- Journal Name:
- Proceedings of the 7th ACM Conference on Information-Centric Networking
- Page Range / eLocation ID:
- 117 to 128
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.more » « less
-
Network-on-Chip (NoC) is widely employed by multi-core System-on-Chip (SoC) architectures to cater to their communication requirements. Increasing NoC complexity coupled with its widespread usage has made it a focal point of potential security attacks. Distributed Denial-of-Service (DDoS) is one such attack that is caused by malicious intellectual property (IP) cores flooding the network with unnecessary packets causing significant performance degradation through NoC congestion. In this paper, we propose an efficient framework for real-time detection and localization of DDoS attacks. This paper makes three important contributions. We propose a real-time and lightweight DDoS attack detection technique for NoC-based SoCs by monitoring packets to detect any violations. Once a potential attack has been flagged, our approach is also capable of localizing the malicious IPs using the latency data in the NoC routers. The applications are statically profiled during design time to determine communication patterns. These patterns are then used for real-time detection and localization of DDoS attacks. We have evaluated the effectiveness of our approach against different NoC topologies and architecture models using both real benchmarks and synthetic traffic patterns. Our experimental results demonstrate that our proposed approach is capable of real-time detection and localization of DDoS attacks originating from multiple malicious IPs in NoC-based SoCs.more » « less
-
more » « less
Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, called
Scotch , to enable the network to scale up its capability and handle the DDoS attack traffic. In our design, the congestion works as an indicator to trigger the mitigation mechanism.Scotch elastically scales up the control plane capacity by using an Open vSwitch-based overlay.Scotch takes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under abnormal (e.g., DDoS attacks) traffic surges. We have implemented a prototype and experimentally evaluatedScotch . Our experiments in the small-scale lab environment and large-scale GENI testbed demonstrate thatScotch can elastically scale up the control channel bandwidth upon attacks. -
null ; null ; null ; null (Ed.)Distributed reflective denial of service (DRDoS) attacks are a popular choice among adversaries. In fact, one of the largest DDoS attacks ever recorded, reaching a peak of 1.3 Tbps against GitHub, was a memcached-based DRDoS attack. More recently, a record-breaking 2.3 Tbps attack against Amazon AWS was due to a CLDAP-based DRDoS attack. Although reflective attacks have been known for years, DRDoS attacks are unfortunately still popular and largely unmitigated. In this paper, we measure in-the-wild DRDoS attacks as observed from a large Internet exchange point (IXP) and provide a number of security-relevant insights. To enable our measurements, we first developed IXmon, an open-source DRDoS detection system specifically designed for deployment at large IXP-like network connectivity providers and peering hubs. We deployed IXmon at Southern Crossroads (SoX), an IXP-like hub that provides both peering and upstream Internet connectivity services to more than 20 research and education (R&E) networks in the South-East United States. In a period of about 21 months, IXmon detected more than 900 DRDoS attacks towards 31 different victim ASes. An analysis of the real-world DRDoS attacks detected by our system shows that most DRDoS attacks are short lived, lasting only a few minutes, but that large-volume, long-lasting, and highly-distributed attacks against R&E networks are not uncommon. We then use the results of our analysis to discuss possible attack mitigation approaches that can be deployed at the IXP level, before the attack traffic overwhelms the victim’s network bandwidth.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3405656.3418709
