Abstract: With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.
Abstract: Deep Venous Thrombosis (DVT) occurs when a
thrombus is formed within a deep vein (most often in the legs). This
disease can be deadly if a part or the whole thrombus reaches the
lung and causes a Pulmonary Embolism (PE). This disorder, often
asymptomatic, has multifactorial causes: immobilization, surgery,
pregnancy, age, cancers, and genetic variations. Our project aims to
relate the thrombus epidemiology (origins, patient predispositions,
PE) to its structure using ultrasound images. Ultrasonography and
elastography were collected using Toshiba Aplio 500 at Brest
Hospital. This manuscript compares two classification approaches:
spectral clustering and scattering operator. The former is based on
the graph and matrix theories while the latter cascades wavelet
convolutions with nonlinear modulus and averaging operators.
Abstract: This paper proposes a novel solution for optimizing
the size and communication overhead of a distributed multiagent
system without compromising the performance. The proposed approach
addresses the challenges of scalability especially when the
multiagent system is large. A modified spectral clustering technique
is used to partition a large network into logically related clusters.
Agents are assigned to monitor dedicated clusters rather than monitor
each device or node. The proposed scalable multiagent system is
implemented using JADE (Java Agent Development Environment)
for a large power system. The performance of the proposed topologyindependent
decentralized multiagent system and the scalable multiagent
system is compared by comprehensively simulating different
fault scenarios. The time taken for reconfiguration, the overall computational
complexity, and the communication overhead incurred are
computed. The results of these simulations show that the proposed
scalable multiagent system uses fewer agents efficiently, makes faster
decisions to reconfigure when a fault occurs, and incurs significantly
less communication overhead.