Cellular Neural Network

Cellular Neural Network is a revolutionary concept and an experimentally proven new computing paradigm for analog computers. Looking at the technological advancement in the last 50 years ; we see the first revolution which led to pc industry in 1980’s, second revolution led to internet industry in 1990’s cheap sensors & mems arrays in desired forms of artificial eyes, nose, ears etc. this third revolution owes due to C.N.N.This technology is implemented using CNN-UM and is also used in imageprocessing. It can also implement any Boolean functions.

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Wireless Internet Security

In the past few years, there has been an explosive growth in the popularity and availability of small, handheld devices (mobile phones, PDAs, pagers), that can wirelessly connect to the Internet. These devices are predicted to soon outnumber traditional Internet hosts like PCs and workstations [1]. With their convenient form factor and falling prices, these devices hold the promise of ubiquitous (“anytime, anywhere”) access to a wide array of interesting services. However, these batterydriven devices are characterized by limited storage (volatile and non-volatile memory), minimal computational capability, and screen sizes that vary from small to very small. These limitations make the task of creating secure, useful applications for these devices especially challenging.

It is easy to imagine a world in which people rely on connected handheld devices not only to store their personal data, check news and weather reports, but also for more security sensitive applications like on-line banking, stock trading and shopping - all while being mobile. Such transactions invariably require the exchange of private information like passwords, PINs and credit card numbers and ensuring their secure transport through the network becomes an important concern.

On the wired Internet, Secure Sockets Layer (SSL) [3] is the most widely used security protocol.Between its conception at Netscape in the mid-90s and standardization within the IETF in the late-90s, the protocol and its implementations have been subjected to careful scrutiny by some of the world’s foremost security experts [5]. No wonder then, that SSL (in the form of HTTPS which is simply HTTP over SSL) is trusted to secure transactions for sensitive applications ranging from web banking to securities trading to e-commerce. One could easily argue that without SSL, there would be no e-commerce on the web today. Almost all web servers on the Internet support some version of SSL [6]. Unfortunately, none of the popular wide-area wireless data services today offer this protocol on a handheld device. Driven by perceived inadequacies of SSL in a resource constrained environment, architects of both WAP [7] and Palm.net [8] chose a different (and incompatible) security protocol (e.g., WTLS[9] for WAP) for their mobile clients and inserted a proxy/gateway in their architecture to perform protocol conversions. A WAP gateway, for instance, decrypts encrypted data sent by a WAP phone using WTLS and re-encrypts it using SSL before forwarding it to the eventual destination server. The reverse process is used for traffic flowing in the opposite direction.

Such a proxy-based architecture has some serious drawbacks. The proxy is not only a potential performance bottleneck, but also represents a “man-in-the-middle” which is privy to all “secure” communications. This lack of end-to-end security is a serious deterrent for any organization thinking of extending a security-sensitive Internet-based service to wireless users. Banks and brokerage houses are uncomfortable with the notion that the security of their customers’ wireless transactions depends on the integrity of the proxy under the control of an untrusted third party [10].

We found it interesting that the architects of WAP and Palm.net made tacit assumptions about the unsuitability of standard Internet protocols (especially SSL) for mobile devices without citing any studies that would warrant such a conclusion [11]. This prompted our experiments in evaluating standard security algorithms and protocols (considered too “big” by some) for small devices. We sought answers to some key questions: Is it possible to develop a usable implementation of SSL for a mobile device and thereby provide end-to-end security? How would near-term technology trends impact the conclusions of our investigation?

The rest of this report describes our experiments in greater detail. Section 2 reviews the security architecture of current wireless Internet offerings and analyses its shortcomings. Section 3 provides an overview of the SSL protocol. In particular, we highlight aspects that make it easier to implement SSL on weak CPUs than it might appear at first. Section 4 discusses our implementation of an SSL client, called KSSL, on a Palm PDA and evaluates its performance. Section 5 describes an application we’ve developed for secure, mobile access to enterprise resources through sun. netTM based on KSSL. Section 6 talks about mobile technology trends relevant to application and protocol developers

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Digital Watermarking

In recent years, the distribution of works of art, including pictures, music, video and textual documents, has become easier. With the widespread and increasing use of the Internet, digital forms of these media (still images, audio, video, text) are easily accessible. This is clearly advantageous, in that it is easier to market and sell one's works of art. However, this same property threatens copyright protection. Digital documents are easy to copy and distribute, allowing for pirating. There are a number of methods for protecting ownership. One of these is known as digital watermarking.

Digital watermarking is the process of inserting a digital signal or pattern (indicative of the owner of the content) into digital content. The signal, known as a watermark, can be used later to identify the owner of the work, to authenticate the content, and to trace illegal copies of the work.

Watermarks of varying degrees of obtrusiveness are added to presentation media as a guarantee of authenticity, quality, ownership, and source.
To be effective in its purpose, a watermark should adhere to a few requirements. In particular, it should be robust, and transparent. Robustness requires that it be able to survive any alterations or distortions that the watermarked content may undergo, including intentional attacks to remove the watermark, and common signal processing alterations used to make the data more efficient to store and transmit. This is so that afterwards, the owner can still be identified. Transparency requires a watermark to be imperceptible so that it does not affect the quality of the content, and makes detection, and therefore removal, by pirates less possible.

The media of focus in this paper is the still image. There are a variety of image watermarking techniques, falling into 2 main categories, depending on in which domain the watermark is constructed: the spatial domain (producing spatial watermarks) and the frequency domain (producing spectral watermarks). The effectiveness of a watermark is improved when the technique exploits known properties of the human visual system. These are known as perceptually based watermarking techniques. Within this category, the class of image-adaptive watermarks proves most effective.
In conclusion, image watermarking techniques that take advantage of properties of the human visual system, and the characteristics of the image create the most robust and transparent watermarks.

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PARASITIC COMPUTING

The net is a fertile place where new ideas/products surface quite often. We have already come across many innovative ideas such as Peer-to-Peer file sharing, distributed computing etc. Parasitic computing is a new in this category. Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. The Notre Dame computer scientist showed that these protocols could be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node.

In this model, known as “parasitic computing”, one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. As experimental evidence of the principle of parasitic computing, the scientists harnessed the power of several web servers across the globe, which–unknown to them–work together to solve an NP complete problem.

Sending a message through the Internet is a sophisticated process regulated by layers of complex protocols. For example, when a user selects a URL (uniform resource locator), requesting a web page, the browser opens a transmission control protocol (TCP) connection to a web server. It then issues a hyper-text transmission protocol (HTTP) request over the TCP connection. The TCP message is carried via the Internet protocol (IP), which might break the message into several packages, which navigate independently through

numerous routers between source and destination. When an HTTP request reaches its target web server, a response is returned via the same TCP connection to the user's browser. The original message is reconstructed through a series of consecutive steps, involving IP and TCP; it is finally interpreted at the HTTP level, eliciting the appropriate response (such as sending the requested web page). Thus, even a seemingly simple request for a web page involves a significant amount of computation in the network and at the computers at the end points.

In essence, a `parasitic computer' is a realization of an abstract machine for a distributed computer that is built upon standard Internet communication protocols. We use a parasitic computer to solve the well known NP-complete satisfiability problem, by engaging various web servers physically located in North America, Europe, and Asia, each of which unknowingly participated in the experiment. Like the SETI@home project, parasitic computing decomposes a complex problem into computations that can be evaluated independently and solved by computers connected to the Internet; unlike the SETI project, however, it does so without the knowledge of the participating servers. Unlike `cracking' (breaking into a computer) or computer viruses, however, parasitic computing does not compromise the security of the targeted servers, and accesses only those parts of the servers that have been made explicitly available for Internet communication.

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