Biblio

Wireless mesh networking (WMN) is a new technology aimed to introduce the benefits of using multi-hop and multi-path to the wireless world. However, the absence of a fast and reliable handoff protocol is a major drawback especially in a technology designed to feature high mobility and scalability. We propose a fast and efficient handoff authentication protocol for wireless mesh networks. It is a token-based authentication protocol using pre-distributed parameters. We provide a performance comparison among our protocol, UFAP, and other protocols including EAP-TLS and EAP-PEAP tested in an actual setup. Performance analysis will prove that our proposed handoff authentication protocol is 250 times faster than EAP-PEAP and 500 times faster than EAP-TLS. The significant improvement in performance allows UFAP to provide seamless handoff and continuous operation even for real-time applications which can only tolerate short delays under 50 ms.

Cyber anonymity tools have attracted wide attention in resisting network traffic censorship and surveillance, and have played a crucial role for open communications over the Internet. The Onion Routing (Tor) is considered the prevailing technique for circumventing the traffic surveillance and providing cyber anonymity. Tor operates by tunneling a traffic through a series of relays, making such traffic to appear as if it originated from the last relay in the traffic path, rather than from the original user. However, Tor faced some obstructions in carrying out its goal effectively, such as insufficient performance and limited capacity. This paper presents a cyber anonymity technique based on software-defined networking; named SOR, which builds onion-routed tunnels across multiple anonymity service providers. SOR architecture enables any cloud tenants to participate in the anonymity service via software-defined networking. Our proposed architecture leverages the large capacity and robust connectivity of the commercial cloud networks to elevate the performance of the cyber anonymity service.

The unauthorized access or theft of sensitive, personal information is becoming a weekly news item. The illegal dissemination of proprietary information to media outlets or competitors costs industry untold millions in remediation costs and losses every year. The 2013 data breach at Target, Inc. that impacted 70 million customers is estimated to cost upwards of 1 billion dollars. Stolen information is also being used to damage political figures and adversely influence foreign and domestic policy. In this paper, we offer some techniques for better understanding the health and security of our networks. This understanding will help professionals to identify network behavior, anomalies and other latent, systematic issues in their networks. Software-Defined Networks (SDN) enable the collection of network operation and configuration metrics that are not readily available, if available at all, in traditional networks. SDN also enables the development of software protocols and tools that increases visibility into the network. By accumulating and analyzing a time series data repository (TSDR) of SDN and traditional metrics along with data gathered from our tools we can establish behavior and security patterns for SDN and SDN hybrid networks. Our research helps provide a framework for a range of techniques for administrators and automated system protection services that give insight into the health and security of the network. To narrow the scope of our research, this paper focuses on a subset of those techniques as they apply to the confidence analysis of a specific network path at the time of use or inspection. This confidence analysis allows users, administrators and autonomous systems to decide whether a network path is secure enough for sending their sensitive information. Our testing shows that malicious activity can be identified quickly as a single metric indicator and consistently within a multi-factor indicator analysis. Our research includes the implementation of - hese techniques in a network path confidence analysis service, called Confidence Assessment as a Service. Using our behavior and security patterns, this service evaluates a specific network path and provides a confidence score for that path before, during and after the transmission of sensitive data. Our research and tools give administrators and autonomous systems a much better understanding of the internal operation and configuration of their networks. Our framework will also provide other services that will focus on detecting latent, systemic network problems. By providing a better understanding of network configuration and operation our research enables a more secure and dependable network and helps prevent the theft of information by malicious actors.

The U.S Government has been the target for cyberattacks from all over the world. Just recently, former President Obama accused the Russian government of the leaking emails to Wikileaks and declared that the U.S. might be forced to respond. While Russia denied involvement, it is clear that the U.S. has to take some defensive measures to protect its data infrastructure. Insider threats have been the cause of other sensitive information leaks too, including the infamous Edward Snowden incident. Most of the recent leaks were in the form of text. Due to the nature of text data, security classifications are assigned manually. In an adversarial environment, insiders can leak texts through E-mail, printers, or any untrusted channels. The optimal defense is to automatically detect the unstructured text security class and enforce the appropriate protection mechanism without degrading services or daily tasks. Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited for detecting unstructured texts. In this paper, we compare two recent approaches in the literature for text security classification, evaluating them on actual sensitive text data from the WikiLeaks dataset.