Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.

https://cial.csie.ncku.edu.tw/presentation/group_pdf/(IFIP%202013)%20Interest%20Flooding%20Attack%20and%20Countermeasures%20in%20Named%20Data%20Networking.pdf

Interest flooding attack and countermeasures in Named Data Networking

The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time, and in any place, to each other and to the Internet. One of the major problems associated with the IoT is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly, in the areas of security and privacy. Specifically, security testing and analysis of IoT devices is considered a very complex task, as different security testing methodologies, including software and hardware security testing approaches, are needed. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed is aimed at testing all types of IoT devices, with different software/hardware configurations, by performing standard and advanced security testing. Advanced analysis processes based on machine learning algorithms are employed in the testbed in order to monitor the overall operation of the IoT device under test. The architectural design of the proposed security testbed along with a detailed description of the testbed implementation is discussed. The testbed operation is demonstrated on different IoT devices using several specific IoT testing scenarios. The results obtained demonstrate that the testbed is effective at detecting vulnerabilities and compromised IoT devices.

/pdf/security-testbed-for-internet-of-things-devices-2uzc88lf9e.pdf

Security Testbed for Internet-of-Things Devices

Methods, apparatus, and computer readable storage media reduce or eliminate network traffic meeting criteria. In some aspects, network traffic transmitted by one or more source nodes to one or more destination nodes may comprise a denial of service attack against the destination node(s). At least a portion of the denial of service attack traffic may be reduced or eliminated with the disclosed methods and apparatus. In one aspect, a method of managing undesirable network traffic transmitted from a source node to a destination node over a communications network includes receiving a notification of a routing rule change, authenticating the notification, determining a network routing rule based on the notification, applying the network routing rule, determining a network path toward the source node, determining an entity based on the network path, and transmitting a notification of the routing rule change to the entity.

Methods and apparatus for managing network traffic

Researchers in the denial-of-service (DoS) field lack accurate, quantitative, and versatile metrics to measure service denial in simulation and testbed experiments. Without such metrics, it is impossible to measure severity of various attacks, quantify success of proposed defenses, and compare their performance. Existing DoS metrics equate service denial with slow communication, low throughput, high resource utilization, and high loss rate. These metrics are not versatile because they fail to monitor all traffic parameters that signal service degradation. They are not quantitative because they fail to specify exact ranges of parameter values that correspond to good or poor service quality. Finally, they are not accurate since they were not proven to correspond to human perception of service denial. We propose several DoS impact metrics that measure the quality of service experienced by users during an attack. Our metrics are quantitative: they map QoS requirements for several applications into measurable traffic parameters with acceptable, scientifically determined thresholds. They are versatile: they apply to a wide range of attack scenarios, which we demonstrate via testbed experiments and simulations. We also prove metrics' accuracy through testing with human users.

/pdf/accurately-measuring-denial-of-service-in-simulation-and-3jurud62ks.pdf

Accurately Measuring Denial of Service in Simulation and Testbed Experiments

Taxonomy of Distributed Denial of Service mitigation approaches for cloud computing

Increasing use of the Internet for critical services makes flooding distributed denial-of-service (DDoS) a top security threat. A distributed nature of DDoS suggests that a distributed mechanism is necessary for a successful defense. Three main DDoS defense functionalities -- attack detection, rate limiting and traffic differentiation -- are most effective when performed at the victim-end, core and sourceend respectively. Many existing systems are successful in one aspect of defense, but none offers a comprehensive solution and none has seen a wide deployment. We propose to harvest the strengths of existing defenses by organizing them into a collaborative overlay, called DefCOM, and augmenting them with communication and collaboration functionalities. Nodes collaborate during the attack to spread alerts and protect legitimate traffic, while rate limiting the attack. DefCOM can accommodate existing defenses, provide synergistic response to attacks and naturally lead to an Internet-wide response to DDoS threat.

/pdf/a-framework-for-a-collaborative-ddos-defense-38eotn1ult.pdf

G. Oikonomou

Papers

A Framework for a Collaborative DDoS Defense