Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-001087.pdf

Catastrophic cascade of failures in interdependent networks

Application layer HTTP-GET flood DDoS attacks

Cooperative trust relaying and privacy preservation via edge-crowdsourcing in social Internet of Things

Attacks against web servers and web-based applications remain a serious global network security threat. Attackers are able to compromise web services, collect confidential information from web data bases, interrupt or completely paralyze web servers. In this study, we consider the analysis of HTTP logs for the detection of network intrusions. First, a training set of HTTP requests which does not contain any attacks is analyzed. When all relevant information has been extracted from the logs, several clustering and anomaly detection algorithms are employed to describe the model of normal users behavior. This model is then used to detect network attacks as deviations from the norms in an online mode. The simulation results presented show that, compared to other data mining algorithms, the method results in a higher accuracy rate.

Analysis of HTTP Requests for Anomaly Detection of Web Attacks

Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

Summary
With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource-intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource-providing model called “crowd-funding” is proposed. In the crowd-funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC-GA (genetic algorithm for task scheduling based on a resource crowd-funding model). For crowd-funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC-GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC-GA can achieve good results.

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

HTTP-flooding attack is a much stealthier distributed denial of service (DDoS) attack, challenging the survivability of the web services seriously. Observing the web access behavior, we find that the surfing preference of normal users is much more consistent with the webpage popularity than that of malicious users. Based on this observation, this paper proposes a novel detection scheme for HTTP-flooding (HTTP-SoLDiER). Specifically, HTTP-SoLDiER first quantifies the consistency between web users surfing preference and the webpage popularity with large-deviation principle. Then HTTP-SoLDiER distinguishes the malicious users from normal ones according to the large-deviation probability. In practice, the webpage popularity plays a key role in attack detection of HTTP-SoLDiER. Due to the never-ending updating of the webpage content and the disturbance induced by attackers, the webpage popularity often varies over time. Thus, it is critical for HTTP-SoLDiER to dynamically update the webpage popularity. We design a reversible exponentially weighted moving average (EWMA) algorithm to solve the problem. Finally, we evaluate the effectiveness of this scheme in terms of true positive (TP) and false positive (FP) probabilities with NS-3 simulations. The simulation results show that HTTP-SoLDiER can detect all random HTTP-flooding attackers and most of the perfect-knowledge HTTP-flooding attackers at little false positive.

HTTP-SoLDiER: An HTTP-flooding attack detection scheme with the large deviation principle

HTTP-flooding attack disables the victimized web server by sending a large number of HTTP Get requests. Recent research tends to detect HTTP-flooding with the anomaly-based approaches, which detect the HTTP-flooding by modeling the behavior of normal web surfers. However, most of the existing anomaly-based detection approaches usually cannot filter the web-crawling traces from unknown searching bots mixed in normal web browsing logs. These web-crawling traces can bias the base-line profile of anomaly-based schemes in their training phase, and further degrade their detection performance. This paper proposes a novel web-crawling traces-tolerated method to build baseline profile, and designs a new anomaly-based HTTP-flooding detection scheme (abbr. HTTP-sCAN). The simulation results show that HTTP-sCAN is immune to the interferences of unknown web-crawling traces, and can detect all HTTP-flooding attacks.

HTTP-sCAN: Detecting HTTP-flooding attack by modeling multi-features of web browsing behavior from noisy web-logs

HTTP-flooding attack disables the victimized Web server by sending a large number of HTTP Get requests. Recent research tends to detect the attacks with the anomaly-based approaches, which detect the HTTP-flooding by modeling the behavior of normal Web users. However, most of the existing anomaly-based detection approaches usually cannot filter the Web crawling traces of the unknown search bots mixed in the normal Web browsing logs. These Web-crawling traces can bias the detection model in the training phase, thus further influencing the performance of the anomaly-based detection schemes. This paper proposes a novel anomaly-based HTTP-flooding detection scheme (HTTP-sCAN), which can eliminate the influence of the Web-crawling traces with the cluster algorithm. The simulation results show that HTTP-sCAN is immune to the interferences of unknown search sessions, and can detect all HTTP-flooding attacks.

HTTP-sCAN: Detecting HTTP-flooding attaCk by modeling multi-features of web browsing behavior from noisy dataset

With electric vehicle’s charging information, the utilities can increase the efficiency and reliability of Vehicle-to-Grid (V2G) while the electric vehicle consumers can better manage their energy consumption and costs However, since charging information are commonly transmitted via public network or wireless link, their communications lack trusted third party for identity authentication and key distribution, and are constantly exposed to traffic analysis attacks In this article, we propose a secure and anonymous communication scheme for delivering charging information in V2G Different from previous works, the scheme in this article creatively incorporates identity authentication into key distribution without trusted third party, which improves the security of communication in public networks Moreover, the proposed scheme is more resistant to traffic analysis since it preserves the anonymity of charging information by splitting and forwarding them pseudo-randomly As the performance analysis reveals, our scheme is able to provide better anonymity without the support of trusted third party, and what’s more, it can achieve high cryptography capability as traditional communication schemes do Therefore, it is more practical in real-world V2G

/pdf/a-secure-and-anonymous-communication-scheme-for-charging-2r1fz159oj.pdf

Min Zhang

Papers

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

HTTP-SoLDiER: An HTTP-flooding attack detection scheme with the large deviation principle

HTTP-sCAN: Detecting HTTP-flooding attack by modeling multi-features of web browsing behavior from noisy web-logs

HTTP-sCAN: Detecting HTTP-flooding attaCk by modeling multi-features of web browsing behavior from noisy dataset

A Secure and Anonymous Communication Scheme for Charging Information in Vehicle-to-Grid