Anonymous network services on the World Wide Web have emerged as a new web architecture, called the Dark Web. The Dark Web has been notorious for harboring cybercriminals abusing anonymity. At the same time, the Dark Web has been a last resort for people who seek freedom of the press as well as avoid censorship. This anonymous nature allows website operators to conceal their identity and thereby leads users to have difficulties in determining the authenticity of websites. Phishers abuse this perplexing authenticity to lure victims; however, only a little is known about the prevalence of phishing attacks on the Dark Web.

Software-Defined Networking (SDN) controllers not only provide centralized control of SDNs, but also implement open and programmable APIs to ultimately establish an open network environment, where anyone can develop and deliver useful SDN applications. In such an environment, malicious SDN applications can be easily developed and distributed by untrusted entities and can even possess full control of SDNs. Thus, the security threat of malicious SDN applications must be taken seriously. In this paper, we propose a novel system, called Indago, which statically analyzes SDN applications to model their behavioral profiles, and finally, it automatically detects malicious SDN applications with a machine learning approach. We implement a prototype system and evaluate its effectiveness with real world SDN applications and malware. Our evaluation results show that the system can detect most known SDN malware with a high detection rate and low error rates.

An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment.

Management approaches to modern networks are increasingly influenced by software-defined networks (SDNs), and this increased influence is reflected in the growth of commercially available innovative SDN-based switches, controllers and applications. To date, there have been a number of commercial and open-source SDN operating systems (NOS) introduced for various purposes, including distributed controller frameworks targeting large, carrier-grade networks such as the Open Network Operating System (ONOS) and OpenDayLight (ODL). These frameworks are distinguished by their (i) elastic cluster controller architecture, (ii) network virtualization support, and (iii) modular design. Given their flexible design, growing list of supported features, and collaborative community support, these are attractive hosting platforms for a wide range of third-party distributed network management applications. This paper identifies the common security requirements for policy enforcement in such distributed controller environments. We present the design of a network application permission-enforcement model and an integrated security subsystem (SM-ONOS) for managing distributed applications running on an ONOS controller. We discuss the underlying motivations of its security extensions and their implications for improving our understanding of how to securely manage large-scale SDNs. Our performance assessments demonstrate that the security-mode extension imposed reasonable overheads (ranging from 5 to 20% for 1-7 node clusters).

Emerging software defined network (SDN) stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.

Software-Defined Networking (SDN), which separates the control and data plane of network, is strongly considered as a promising future networking architecture. Compared with legacy networking architecture, it allows to enable a variety of innovative network functions at much less cost and effort. Accordingly, each component of SDN is also being rapidly realized, and one of the most noticeable SDN component implementations would be SDN controllers, such as ONOS or Floodlight. One advantage of these SDN controllers is capability of hosting various network applications to enable innovative network functions, however, it is crucial to analyze these applications before the actual deployment as they may directly affect the performance of the managed network. To be more specific, SDN applications may contain performance bugs that unnecessarily consume significant system resource or produce critical bottlenecks in the controller. In this paper, we introduce an automatic SDN application profiling framework, SPIRIT, which reduces the human effort in revealing any performance bugs that might exist in SDN applications. In order to show the effectiveness of our framework, we reveal new performance bugs exist in ONOS and Floodlight applications.