47302704
On the performance of group key agreement protocols
Group key agreement (GKA) is a fundamental building block for securing peer group communication systems (GCS). Several group key agreement protocols were proposed in the past, all assuming an underlying group communication infrastructure. This paper presents a performance evaluation of 5 notable GKA protocols integrated with a reliable group communication system (Spread). They are: centralized group key distribution (CKD), Burmester-Desmedt (BD), Steer et al. (STR), group Diffie-Hellman GDH) and tree-based group Diffie-Hellman (TGDH).. We present concrete results obtained in experiments on local- and wide-area networks. Our analysis of these results offers insights into their relative scalability and practicality.
11689182
Decentralized Authentication Mechanisms for Object-based Storage Devices
Network-attached object-based storage separates data-path from control-path and enables direct interaction between clients and the storage devices. Clients interact with the file manager only to acquire the meta-data information and some cryptographic primitives, for example, access keys. Most of the current schemes rely on a centralized file manager to support these activities. This paper presents security mechanisms for decentralized authentication for object-based storage. The schemes are novel in several ways. First of all, they reduce the load on the file manager and free the system from central point of failure and denial of service attacks. We exploit Role-based Access Control (RBAC) to provide scalability and design authentication schemes that efficiently utilize RBAC. In most of the cases, the client needs to acquire only one access key from the file manager, which can be used by the client to further derive role-keys for the roles that he/she is permitted to play within an organization. Further, the number of cryptographic keys required for the purpose of authentication in these schemes is less as compared to the existing schemes. Finally, we also present two simple schemes that enable the clients to access objects stored on any device on the network using a single identity key.
8456310
Detecting Phantom Nodes in Wireless Sensor Networks
In an adversarial environment, various kinds of security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located. In this paper, we propose a secure localization mechanism that detects the existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in quite strong results: even when the number of phantom nodes is greater than that of honest nodes, we can Alter out most phantom nodes. Our analysis and simulations under realistic noisy settings demonstrate our scheme is effective in the presence of a large number of phantom nodes.
260546
Realistic Sensing Area Modeling
Despite the well-known fact that sensing patterns in reality are highly irregular, researchers continue to develop protocols with simplifying assumptions about the sensing. For example, a circular 0/1 sensing model is widely used in most existing simulators and analysis. While this model provides high-level guidelines, it could cause wrong estimation of system performance in the real world. In this project, we design and implement a practical Sensing Area Modeling technique, called SAM. By injecting events through regular and hierarchical training, SAM estimates the sensing areas of individual sensor nodes accurately. Especially, this work is the first to investigate the impact of irregular sensing area on application performance, such as coverage scheduling. We evaluate SAM using outdoor experiments with XSM motes, indoor experiment with 40 MicaZ motes as well as an extensive 1000-node simulation. Our evaluation results reveal serious problems caused by circular sensing model, while demonstrating significant performance improvements in major applications when SAM is used.
18915158
Revisiting random key pre-distribution schemes for wireless sensor networks
Key management is one of the fundamental building blocks of security services. In a network with resource constrained nodes like sensor networks, traditional key management techniques, such as public key cryptography or key distribution center (e.g., Kerberos), are often not effective. To solve this problem, several key pre-distribution schemes have been proposed for sensor networks based on random graph theory. In these schemes, a set of randomly chosen keys or secret information is pre-distributed to each sensor node and a network is securely formed based on this information. Most of the schemes assumed that the underlying physical network is dense enough, that is, the degree of each node is hig.
 In this paper, we revisit the random graph theory and use giant component theory by Erdos and Renyi to show that even if the node degree is small, most of the nodes in the network can be connected. Further, we use this fact to analyze the Eschenhauer et. al's, Du et. al's, and Chan et. al's key pre-distribution schemes and evaluate the relation between connectivity, memory size, and security. We show that we can reduce the amount of memory required or improve security by trading-off a very small number of isolated nodes. Our simulation results show that the communication overhead does not increase significantly even after reducing the node degree. In addition, we present an approach by which nodes can dynamically adjust their transmission power to establish secure links with the targeted networked neighbors. Finally, we propose an effcient path-key identification algorithm and compare it with the existing scheme.
6315809
On the performance of group key agreement protocols
Group key agreement is a fundamental building block for secure peer group communication systems. Several group key management techniques were proposed in the last decade, all assuming the existence of an underlying group communication infrastructure to provide reliable and ordered message delivery as well as group membership information. Despite analysis, implementation, and deployment of some of these techniques, the actual costs associated with group key management have been poorly understood so far. This resulted in an undesirable tendency: on the one hand, adopting suboptimal security for reliable group communication, while, on the other hand, constructing excessively costly group key management protocols.This paper presents a thorough performance evaluation of five notable distributed key management techniques (for collaborative peer groups) integrated with a reliable group communication system. An in-depth comparison and analysis of the five techniques is presented based on experimental results obtained in actual local- and wide-area networks. The extensive performance measurement experiments conducted for all methods offer insights into their scalability and practicality. Furthermore, our analysis of the experimental results highlights several observations that are not obvious from the theoretical analysis.
8330560
Shape Comparison of the Hippocampus Using a Multiresolution Representation and ICP Normalization
It is known that deformation of the hippocampus shape is involved with several neurological diseases. In this paper, we propose a hybrid shape representation scheme, which consists of multiresolution skeletons, voxels and meshes for the shape analysis of the hippocampus. Initially, a hippocampal surface model is reconstructed from MRI and then it is placed into a canonical coordinate system, where the position, orientation and scaling are normalized. From the voxel representation of the hippocampus, multiresolution skeletons are extracted and Iterative Closest Point normalization is carried out. Then the shape similarity of two hippocampal models is computed with a hierarchical fashion. In addition, we have implemented a neural network based classifier to discriminate whether a hippocampal model is normal or not. Results indicate that the proposed hybrid representation and the skeleton-based normalization using ICP are very effective in 3D shape analysis of the hippocampus.
13345946
Efficient Implementation of Image Warping on a Multimedia Processor
The spatial transformation of images, commonly known as image warping, is fundamental to many applications, e.g. remote sensing, medical imaging, computer vision, and computer graphics. Computational demands in image warping are high, requiring a geometric transformation, address and coefficient generation, and some form of interpolation. However, unlike most image processing algorithms, the data flow for image warping can be highly irregular, which makes any efficient implementation challenging.This paper describes an efficient algorithm which addresses these challenges by making use of the capabilities of a single-chip multiprocessing microprocessor, the Texas Instruments TMS320C80 MVP (multimedia video processor). the MVP's advanced digital signal processors (ADSPs) offer tremendous computational power through instruction-level parallelism and several key features designed for image processing. The MVP's intelligent input/output interface via the transfer controller (TC) permits efficient irregular memory accesses.Affine and perspective warps have been implemented for 8-bit, 16-bit and and RGB color data using bilinear interpolation.The affine warp can generate 512 × 512 warped output images faster than real-time video rates require. For 8-bit images, the performance is 14.1 ms. Although the amount of computation necessary is the same for 16-bit images, the execution time increases to 15.2 ms since twice as many bytes need to be transferred. For RGB color images, it takes 28.0 ms. The perspective warp requires 46.3 ms for 8-bit and 16-bit images, and 60.4 ms for RGB color images. This unprecedented performance for software-based image warping exceeds many hardware approaches reported in the literature.
33400437
Facial Expression Recognition Using Active Appearance Model
This paper describes a facial expression recognition system based upon Active Appearance Model (AAM), which has been typically used for the face recognition task. Given that AAM has been also used in tracking the moving object, we thought it could be effective in recognizing the facial expressions of humans. Our results show that the performance of the facial expression recognition using AAM is reliably high when it combined with an enhanced Fisher classification model.
30845722
Accurate exploration of timing and area trade-offs in arithmetic optimization using carry-save-adders
Timing and area of circuits are two of the most important design criteria to be optimized in data path synthesis. In addition, carry-save-adder (CSA) has been proven to be one of the most efficient implementation units in optimizing timing and/or area of arithmetic circuits. However, the existing approaches are restricted in using CSAs, i.e., optimizing operation trees separately without any interaction between them, resulting in a locally optimized CSA circuit. To overcome this limitation, we propose a practically efficient solution to the problem of an accurate exploration of timing and area trade-offs in optimizing arithmetic circuits in the presence of multiple operation trees using CSAs. The application of our approach is able to find a best CSA implementation of circuit in terms of timing and area.
37111410
Performance Comparison Between Backpropagation, Neuro-Fuzzy Network, and SVM
In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.