Touching from a distance

Googling for Health Information

Local and global topics in text modeling of web pages nested in web sites

In this paper, we study the problem of traffic analysis attacks in wireless body area sensor networks. When these networks are used in health-care for remote patient monitoring, traffic analysis can reveal the type of medical sensors mounted on the patient, and this information may be used to infer the patient's health problems. We show that simple signal processing methods can be used effectively for performing traffic analysis attacks and identifying the sensor types in a rather weak adversary model. We then investigate possible traffic obfuscation mechanisms aiming at hiding the regular patterns in the observable wireless traffic. Among the investigated countermeasures, traffic shaping, a mechanism that introduces carefully chosen delays for message transmissions, appears to be the best choice, as it achieves close to optimal protection and incurs no overhead.

The Tor anonymity network is one of the most popular and widely used tools to protect the privacy of online users. Tor provides defenses against multiple adversarial activities aiming to identify or trace the users. Traffic analysis is a very strong tool that can be used for internet surveillance. Traffic analysis attacks against Tor's anonymity network has been known as an open question in research. Moreover, the low-latency feature Tor tries to provide to its users imposes limitations in defending against traffic analysis attacks. In our study, we examine traffic analysis attacks from the perspective of the adopted adversary model and how much it fits within Tor's threat model. The purpose of this study is to evaluate how practical these attacks are on real-time Tor network.

We investigate the feasibility of mounting a de-anonymization attack against Tor and similar low-latency anonymous communication systems by using NetFlow records. Previous research has shown that adversaries with the ability to eavesdrop in real time at a few internet exchange points can effectively monitor a significant part of the network paths from Tor nodes to destination servers. However, the capacity of current networks makes packet-level monitoring at such a scale quite challenging. We hypothesize that adversaries could use less accurate but readily available monitoring facilities, such as Cisco’s NetFlow, to mount large-scale traffic analysis attacks. In this paper, we assess the feasibility and effectiveness of traffic analysis attacks against Tor using NetFlow data. We present an active traffic analysis technique based on perturbing the characteristics of user traffic at the server side, and observing a similar perturbation at the client side through statistical correlation. We evaluate the accuracy of our method using both in-lab testing and data gathered from a public Tor relay serving hundreds of users. Our method revealed the actual sources of anonymous traffic with 100% accuracy for the in-lab tests, and achieved an overall accuracy of 81.6% for the real-world experiments with a false positive rate of 5.5%.

The capability that a server can hide its location while offering various kinds of services to its clients is called hidden services or location-hiding. Almost previous low-latency anonymous communication systems such as Tor, MorphMix, etc. that can be used to implement hidden services are vulnerable against end-to-end traffic analysis attack. In this paper, we introduce a novel architecture for implementing hidden services which is robust against end-to-end traffic analysis attack. Moreover, our scheme is more robust against various traffic analysis attacks than previous low-latency anonymous communication architectures.KeywordsOverlay NetworkMalicious NodeClockwise DirectionResponse PacketRendezvous PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

It is well known that already the length of encrypted messages may reveal sensitive information about encrypted data. Fingerprinting attacks enable an adversary to determine web pages visited by a user and even the language and phrases spoken in voice-over-IP conversations. Prior research has established the general perspective that a length-hiding padding which is long enough to improve security significantly incurs an unfeasibly large bandwidth overhead. We argue that this perspective is a consequence of the choice of the security models considered in prior works, which are based on classical indistinguishability of two messages, and that this does not reflect the attacker model of typical fingerprinting attacks well. Therefore we propose a new perspective on length-hiding encryption, which aims to capture security against fingerprinting attacks more accurately. This makes it possible to concretely quantify the security provided by length-hiding padding against fingerprinting attacks, depending on the real message distribution of an application. We find that for many real-world applications (such as webservers with static content, DNS requests, Google search terms, or Wikipedia page visits) and their specific message distributions, even length-hiding padding with relatively small bandwidth overhead of only 2–5% can already significantly improve security against fingerprinting attacks. This gives rise to a new perspective on length-hiding encryption, which helps understanding how and under what conditions length-hiding encryption can be used to improve security.

With the phenomenal growth of World Wide Web and the huge number of web sites with so many web pages, finding relevant information becomes quite difficult for the web users. Web site promoters also need to ensure that their web site is on the top of the search results for better market share. Finding the right combination of keywords and placing them at the appropriate places may help in bringing the web site on the top of search results when a search engine searches the web sites and creates indexes. Over the years web site optimization research has reported encouraging results. Despite promising results, data mining techniques for web sites classification have hardly been applied. An appropriate implementation of classification of web pages may help designing the web pages accordingly. Here in this work data mining using R’s web scraping technique has been used for performing extraction of web pages and also pair of java tools called “website” and “webscrap” were developed to automatically get pages and for further analysis of the downloaded pages. This is the first attempt to analyze medical related nomenclature using.

In many regions of the world, nation-states enforce Internet censorship policies that prevent unrestricted access to information and services by their citizens. Over the years, many censorship circumvention tools have been proposed which, however, require either the deployment of a dedicated infrastructure within trusted ISPs, or are vulnerable to state-of-the-art traffic analysis techniques. To fill this gap, we propose to build a practical censorship-circumvention service that exhibits strong resistance against traffic analysis attacks. By relying on a recent proposal for creating covert channels through WebRTC streams, we discuss the design of a distributed system named Censorship-Resistant Overlay Network (CRON). CRON aims at offering to the users located in censored regions a set of services that allow them to locate proxies positioned in the free Internet region, and set up secure covert tunnels for accessing arbitrary sites on the Internet. We present the key challenges and explore the solutions in making CRON robust against state-level attacks.

Virtualization is the cornerstone of the developing third party compute industry, allowing cloud providers to instantiate multiple virtual machines (VMs) on a single set of physical resources. Customers utilize cloud resources alongside unknown and untrusted parties, creating the co-resident threat -- unless perfect isolation is provided by the virtual hypervisor, there exists the possibility for unauthorized access to sensitive customer information through the exploitation of covert side channels.

With the exponentially growing amount of information available on the Internet, an effective technique for users to discern the useful information from the unnecessary information is urgently required. Cleaning web pages for web data extraction becomes critical for improving performance of information retrieval and information extraction. So, we investigate to remove various noise patterns in Web pages instead of extracting relevant content from Web pages to get main content information. In this paper, we propose an approach that detect multiple noise patterns and remove these noise patterns from Web pages of any Web sites. The method first build DOM tree for any Web page. Our approach is based on the basic idea of Case-Based Reasoning (CBR) to find noise pattern in current Web page by matching similar noise pattern kept in Case-Based. We also apply a back propagation neural network algorithm to classify the stored various noise patterns by matching similar noise data in current Web page. We have implemented our method on several commercial Web sites and News Web sites to evaluate the performance and improvement of our approach. Experiments show that results leads to a more accurate and effectiveness of the approach.

The development of digital media, the increasing use of social networks, the easier access to modern technological devices, is perturbing thousands of people in their public and private lives. People love posting their personal news without consider the risks involved. Privacy has never been more important. Privacy enhancing technologies research have attracted considerable international attention after the recent news against users personal data protection in social media websites like Facebook. It has been demonstrated that even when using an anonymous communication system, it is possible to reveal user’s identities through intersection attacks or traffic analysis attacks. Combining a traffic analysis attack with Analysis Social Networks (SNA) techniques, an adversary can be able to obtain important data from the whole network, topological network structure, subset of social data, revealing communities and its interactions. The aim of this work is to demonstrate how intersection attacks can disclose structural properties and significant details from an anonymous social network composed of a university community.

The behavioral web users’ access patterns help website administrator/web site owners to take major decisions in categorizing web pages of the web site as highly demanding pages and medium demanding pages. Human beings act as a spider surfing the web pages of the website in search of required information. Most of the traditional mining algorithms concentrate only on frequency/support of item sets (web pages set denoted as ps in a given web site), which may not bring considerably more amount of profit. The utility mining model focuses on only high utilities item sets (ps). General utility mining model was proposed to overcome weakness of the frequency and utility mining models. General utility mining does not encompass website topology. This limitation is overcome by a novel model called human behavioral patterns’ web pages categorizer (HBP-WPC) which considers structural statistics of the web page in addition to support and utility. The topology of the web site along with log file statistics plays a vital role in categorizing web pages of the web site. The web pages of the website along with log file statistics forms a population. Suitable auto optimization metric is defined which provides guidelines for website designers/owners to restructure the website based on behavioral patterns of web users.KeywordsWeb miningReorganizationLog fileWeb site topology

With the maturity of network technology, the deterministic factor of a 'successful' web site does not as much depend on its connectivity to the Internet, rather the content of web pages plays a more important role. The inclusion of dynamic data, for example, has become an increasingly desirable feature of a web page. Web servers that support dynamic web pages are capable of retrieving data at the time of user requests. Rather than being statically included in the web page, these dynamically retrieved data may physically reside in various file systems or database servers across the Internet.JAVA, a new object-oriented programming language, has become a buzzword since its introduction by Sun Microsystems in 1994 [1, 2, 3]. It was claimed that JAVA, via its ability to embed applets in a web page, would make the content of a web page alive and dynamic. Data and information on a web page can now be updated dynamically when users request them.In this paper, we discuss our experience of developing JAVA applets that use various types of JDBC (JAVA Database Connectivity) drivers to connect to and then manipulate data stored in SQL (Structured Query Language) database servers. We first start with the discussion of general client/server model involving JDBC, and then introducing the procedure of setting up programming projects employing this approach in an undergraduate database course.

At present, there are many security risks from the network, so it has practical significance for students in the university to learn the knowledge about Web pages and website security performance and have Web pages and web site security performance detection capability. It is described that the recent organization of a number of students to carry out a Web page and Web site security performance experiments. In experiments students’ security awareness on the Web page and website are trained through the use of security vulnerability detection tools and enable students to have basic security performance testing capabilities of web page and website