Farshid Farhat

Game-theoretic approach to mitigate packet dropping in wireless Ad-hoc networks

Performance of routing is severely degraded when misbehaving nodes drop packets instead of properly forwarding them. In this paper, we propose a Game-Theoretic Adaptive Multipath Routing (GTAMR) protocol to detect and punish selfish or malicious nodes which try to drop information packets in routing phase and defend against collaborative attacks in which nodes try to disrupt communication or save their power. Our proposed algorithm outranks previous schemes because it is resilient against attacks in which more than one node coordinate their misbehavior and can be used in networks which wireless nodes use directional antennas. We then propose a game theoretic strategy, ERTFT, for nodes to promote cooperation. In comparison with other proposed TFT-like strategies, ours is resilient to systematic errors in detection of selfish nodes and does not lead to unending death spirals.

An Extended Authentication and Key Agreement Protocol of UMTS

Identification, authentication and key agreement protocol of UMTS networks have some weaknesses to provide DoS-attack resistance, mutual freshness, and efficient bandwidth consumption. In this article we consider UMTS AKA and some other proposed schemes. Then we explain the known weaknesses in the previous frameworks suggested for UMTS AKA protocol. After that we propose a new UMTS AKA protocol (called EAKAP) for UMTS mobile network that combines identification stage and AKA stage of UMTS AKA protocol as well as eliminating disadvantages of related works and bringing some new features to improve the UMTS AKA mechanism such as reducing the interactive rounds of the UMTS AKA protocol.

Evaluating the Combined Impact of Node Architecture and Cloud Workload Characteristics on Network Traffic and Performance/Cost

The combined impact of node architecture and workload characteristics on off-chip network traffic with performance/cost analysis has not been investigated before in the context of emerging cloud applications. Motivated by this observation, this paper performs a thorough characterization of twelve cloud workloads using a full-system datacenter simulation infrastructure. We first study the inherent network characteristics of emerging cloud applications including message inter-arrival times, packet sizes, inter-node communication overhead, self-similarity, and traffic volume. Then, we study the effect of hardware architectural metrics on network traffic. Our experimental analysis reveals that (1) the message arrival times and packet-size distributions exhibit variances across different cloud applications, (2) the inter-arrival times imply a large amount of self-similarity as the number of nodes increase, (3) the node architecture can play a significant role in shaping the overall network traffic, and finally, (4) the applications we study can be broadly divided into those which perform better in a scale-out or scale-up configuration at node level and into two categories, namely, those that have long-duration, low-burst flows and those that have short-duration, high-burst flows. Using the results of (3) and (4), the paper discusses the performance/cost trade-offs for scale-out and scale-up approaches and proposes an analytical model that can be used to predict the communication and computation demand for different configurations. It is shown that the difference between two different node architectures performance per dollar cost (under same number of cores system wide) can be as high as 154 percent which disclose the need for accurate characterization of cloud applications before wasting the precious cloud resources by allocating wrong architecture. The results of this study can be used for system modeling, capacity planning and managing heterogeneous resources for large-scale system designs.

Stochastic Modeling and Optimization of Stragglers

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This paper analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

Towards blind detection of low-rate spatial embedding in image steganalysis

Steganalysis of least significant bit (LSB) embedded images in spatial domain has been investigated extensively over the past decade and most well-known LSB steganography methods have been shown to be detectable. However, according to the latest findings in the area, two major issues of very low-rate (VLR) embedding and content-adaptive steganography have remained hard to resolve. The problem of VLR embedding is indeed a generic problem to any steganalyser, while the issue of adaptive embedding specifically depends on the hiding algorithm employed. The latter challenge has recently been brought up again to the area of LSB steganalysis by highly undetectable stego image steganography that offers a content-adaptive embedding scheme for grey-scale images. The authors new image steganalysis method suggests analysis of the relative norm of the image Clouds manipulated in an LSB embedding system. The method is a self-dependent image analysis and is capable of operating on low-resolution images. The proposed algorithm is applied to the image in spatial domain through image Clouding, relative auto-decorrelation features extraction and quadratic rate estimation, as the main steps of the proposed analysis procedure. The authors then introduce and use new statistical features, Clouds-Min-Sum and Local-Entropies-Sum, which improve both the detection accuracy and the embedding rate estimation. They analytically verify the functionality of the scheme. Their simulation results show that the proposed approach outperforms some well known, powerful LSB steganalysis schemes, in terms of true and false detection rates and mean squared error.

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

Risk of attack coefficient effect on availability of Ad-hoc networks

Security techniques have been designed to obtain certain objectives. One of the most important objectives all security mechanisms try to achieve is the availability, which insures that network services are available to various entities in the network when required. But there has not been any certain parameter to measure this objective in network. In this paper we consider availability as a security parameter in ad-hoc networks. However this parameter can be used in other networks as well. We also present the connectivity coefficient of nodes in a network which shows how important is a node in a network and how much damage is caused if a certain node is compromised.

Locally multipath adaptive routing protocol resilient to selfishness and wormholes

Locally multipath adaptive routing (LMAR) protocol, classified as a new reactive distance vector routing protocol for MANETs is proposed in this paper. LMAR can find an ad-hoc path without selfish nodes and wormholes using a random search algorithm in polynomial-time. Also when the primary path fails, it discovers an alternative safe path if network graph remains connected after eliminating selfish/malicious nodes. The main feature of LMAR to seek safe route free of selfish and malicious nodes in polynomial time is its searching algorithm and flooding stage that its generated traffic is equiloaded compared to single-path routing protocols but its ability to bypass the attacks is much better than the other multi-path routing protocols. LMAR concept is introduced to provide the security feature known as availability and a simulator has been developed to analyze its behavior. Efficiency of the route discovery stage is analyzed and compared with the previous algorithms.

Discovering Triangles in Portraits for Supporting Photographic Creation

Incorporating the concept of triangles in photos is an effective composition technique used by professional photographers for making pictures more interesting or dynamic. Information on the locations of the embedded triangles is valuable for comparing the composition of portrait photos which can be further leveraged by a retrieval system or used by the photographers. This paper presents a system to automatically detect embedded triangles in portrait photos. The problem is challenging because the triangles used in portraits are often not clearly defined by straight lines. The system first extracts a set of filtered line segments as candidate triangle sides and then utilizes a modified random sample consensus algorithm to fit triangles onto the set of line segments. We propose two metrics Continuity Ratio and Total Ratio to evaluate the fitted triangles; those with high fitting scores are taken as detected triangles. Experimental results have demonstrated high accuracy in locating preeminent triangles in portraits without dependence on the camera or lens parameters. To demonstrate the benefits of our method to digital photography we have developed two novel applications that aim to help users compose high-quality photos. In the first application we develop a human position and pose recommendation system by retrieving and presenting compositionally similar photos taken by competent photographers. The second application is a novel sketch-based triangle retrieval system which searches for photos containing a specific triangular configuration. User studies have been conducted to validate the effectiveness of these approaches.

Shape matching using skeleton context for automated bow echo detection

Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive thunderstorm conditions such as damaging winds, hail and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images, and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions include a bow echo with over 97% accuracy.