3557483
Streaming encryption for a secure wavelength and time domain hopped optical network
We describe a working implementation of a streaming encryption system for optical networks. The 10 Gbps data stream is encrypted on the physical level in both the wavelength and time domains. Security is obtained by applying a strong pseudo-random hopping pattern to both. The AES algorithm in counter mode is used to control the switches that implement the hopping permutations. Because of the high throughput requirements, aggressive parallelizing and pipelining techniques are used to achieve data rates of 10 Gbps. The core logic of the system was implemented on the Virtex2-XC2V1000 FPGA. Using four FPGA boards and four serializer and deserializer chips, a secure switch of 4 users over 4 wavelengths can be realized, resulting in a total throughput of 10 Gbps. This system is important as a platform for further research in the area of secure optical networks.
5337517
Throughput Optimized SHA-1 Architecture Using Unfolding Transformation
In this paper, the authors analyze the theoretical delay bound of the SHA-1 algorithm and propose architectures to achieve high throughput hardware implementations which approach this bound. According to the results of FPGA implementations, 3,541 Mbps with a pipeline and 893 Mbps without a pipeline were achieved. Moreover, synthesis results using 0.18mum CMOS technology showed that 10.4 Gbps with a pipeline and 3.1 Gbps without a pipeline can be achieved. These results are much faster than previously published results. The high throughputs are due to the unfolding transformation, which reduces the number of required cycles for one block hash. The authors reduced the required number of cycles to 12 cycles for a 512 bit block and showed that 12 cycles is the optimal in our design
2159703
High Speed Channel Coding Architectures for the Uncoordinated OR Channel
Though it promises high bandwidths, the optical medium is not popular in local area networks. This is because current optical networks do not offer the ease of use and setup that an uncoordinated multiple access network such as Ethernet offers. By careful design and implementation of high speed channel coding architectures, we show that it is possible for optical networks to exhibit these desirable properties while maintaining high optical transmission rates. This paper presents an interleaver-division multiple access (IDMA) architecture implemented with a rate 1/20, 64-state Viterbi decoder and a word-based interleaver. These structures allowed us to achieve optical data rates of 2Gbps in FPGA implementation and 5.4Gbps for 0.18mum ASIC implementation. The techniques presented can be adapted for other similar architectures
15483673
Process Isolation for Reconfigurable Hardware
One of the pillars of trust-worthy computing is process isolation, the ability to keep process data private from other processes running on the same device. While embedded operating systems provide isolation for the software part of these processes, there is no commonly accepted isolation mechanism for the hardware resources. As a result, systems may remain vulnerable to hardware-based attacks. This paper presents a secure coprocessor interface that extends the concept of process isolation into reconfigurable hardware. In the resulting coprocessor design, context information for different processes concurrently accessing the coprocessor is physically kept private. The coprocessor interface can handle context switches between different processes without assistance of the operating system. Because of this, reconfiguration of computation units can occur independent of the main processor. Moreover, it does so with greater efficiency than what is possible using software only.
6124330
ACE: An Emergent Algorithm for Highly Uniform Cluster Formation
The efficient subdivision of a sensor network into uniform, mostly non-overlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing.
182657
Key infection: smart trust for smart dust
Future distributed systems may include large self-organizing networks of locally communicating sensor nodes, any small number of which may be subvened by an adversary. Providing security for these sensor networks is important, but the problem is complicated by the fact that managing cryptographic key material is hard: low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. We show how the key distribution problem can be dealt with in environments with a partially present, passive adversary: a node wishing to communicate securely with other nodes simply generates a symmetric key and sends it in the clear to its neighbours. Despite the apparent insecurity of this primitive, we can use mechanisms for key updating, multipath secrecy amplification and multihop key propagation to build up extremely resilient trust networks where at most a fixed proportion of communications links can be eavesdropped. We discuss applications in which this assumption is sensible. Many systems must perforce cope with principals who are authenticated weakly, if at all; the resulting issues have often been left in the 'too hard' tray. One particular interest of sensor networks is that they present a sufficiently compact and tractable version of this problem. We can perform quantitative analyses and simulations of alternative strategies, some of which we present here. We also hope that This work may start to challenge the common belief that authentication is substantially about bootstrapping trust. We argue that, in distributed systems where the opponent can subvert any small proportion of nodes, it is more economic to invest in resilience than in bootstrapping.
12254807
PIKE: peer intermediaries for key establishment in sensor networks
The establishment of shared cryptographic keys between communicating neighbor nodes in sensor networks is a challenging problem due to the unsuitability of asymmetric key cryptography for these resource-constrained platforms. A range of symmetric-key distribution protocols exist, but these protocols do not scale effectively to large sensor networks. For a given level of security, each protocol incurs a linearly increasing overhead in either communication cost per node or memory per node. We describe peer intermediaries for key establishment (PIKE), a class of key-establishment protocols that involves using one or more sensor nodes as a trusted intermediary to facilitate key establishment. We show that, unlike existing key-establishment protocols, both the communication and memory overheads of PIKE protocols scale sub-linearly (O(/spl radic/n)) with the number of nodes in the network yet achieving higher security against node compromise than other protocols.
9263402
Artificial convolution neural network for medical image pattern recognition
Abstract We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution neural network. Weighting coefficients of convolution kernels are formed by the neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts. Our training techniques involve (a) radiologists' rating for each suspected image area, (b) backpropagation of generalized distribution, (c) trainer imposed functions, (d) shift and rotation invariance of diagnosis interpretation, and (e) consistency of clinical input data using appropriate background reduction functions. We have tested these methods for detecting lung nodules on chest radiographs and microcalcications on mammograms. The performance studies have shown the potential use of this technique in a clinical environment. We also used a profile double-matching technique for initial nodule search and used a wavelet high pass filtering technique to enhance subtle clustered microcalcifications. We set searching parameters at a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine whether a disease pattern is shown on the suspected image area.
2565301
Visualizing input and output analysis for simulation (VINOAS)
Abstract Input analysis which involves a large amount of statistical analysis is one of the major steps in simulation. A software package with a well-designed user-friendly graphical window interface has been developed to assist modelers in identifying the underlying statistical distribution of the input data. It integrates techniques for assessing sample independence, hypothesizing families of distributions, estimation of parameters, and performing goodness-of-fit tests in a graphical environment. This allows the modelers to use the package in an interactive way and efforts can be saved in specifying the input probability distribution.