8746552
A queueing analysis for the denial of service (DoS) attacks in computer networks
In most network security analysis, researchers mainly focus on qualitative studies on security schemes and possible attacks, and there are few papers on quantitative analysis in the current literature. In this paper, we propose one queueing model for the evaluation of the denial of service (DoS) attacks in computer networks. The network under DoS attacks is characterized by a two-dimensional embedded Markov chain model. With this model, we can develop a memory-efficient algorithm for finding the stationary probability distribution which can be used to find other interesting performance metrics such as the connection loss probability and buffer occupancy percentages of half-open connections for regular traffic and attack traffic. Different from previous works in the literature, this paper gives a more general analytical approach to the study of security measures of a computer network under DoS attacks. We hope that our approach opens a new avenue to the quantitative evaluation of more complicated security schemes in computer networks.
18765129
Security Analysis of Enterprise Network Based on Stochastic Game Nets Model
In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.
23190531
Algorithm for yield driven correction of layout
As the development of VLSI technique, the critical dimension of IC has become smaller than the exposure wavelength. Due to the diffraction and interaction of optical waves, deformations between the image on wafer and the feature on layout are undeniable. This results in bad performance or even invalid circuits of the chips. OPC is critical compensation technique to correct the deformations on wafer images. This work presents a layout correction and optimization algorithm called MOPC; it's a flexible and efficient core for the model-based OPC system. Since we divide the target features into different types before correction, the OPE between the target features and the environment features and the OPE between the neighboring segments of the inside feature are both considered during the correction.
27638687
A latent class modeling approach to detect network intrusion
This study presents a latent class modeling approach to examine network traffic data when labeled abnormal events are absent in training data, or such events are insufficient to fit a conventional regression model. Using six anomaly-associated risk factors identified from previous studies, the latent class model based on an unlabeled sample yielded acceptable classification results compared with a logistic regression model based on a labeled sample (correctly classified: 0.95 vs. 0.98, sensitivity: 0.99 vs. 0.99, and specificity: 0.77 vs. 0.97). The study demonstrates a great potency for using the latent class modeling technique to analyze network traffic data.
6062507
On Data Fusion and Lifetime Constraints in Wireless Sensor Networks
The problems of energy efficient sensor network configuration and local fusion of sensed data have been addressed separately in the previous works. In this paper, we propose to analyze the sensing accuracy with the consideration of the node distribution and fusion overhead in terms of the energy consumption. By extending existing sensing model in the literature, we quantify how the sensing ranges and node densities of sensor nodes impact the sensing accuracy, and how it affects the fusion overhead and the sensing lifetime of the network. We identify the tradeoff between sensing accuracy and energy consumption in an analytical framework. It can be used to choose optimal parameters of sensor networks to meet the specified sensing or lifetime requirements.
9142519
Utilizing field usage patterns for Java heap space optimization
This research studies the characteristics of field usage patterns in the SpecJVM98 benchmarks. It finds that multiple object instances of the same class often exhibit different field-usage patterns. Motivated by this observation, we designed a heap compression mechanism that classifies object instances at runtime based on their field-usage patterns and eliminates unused fields to save space. To achieve the maximum space savings while minimizing the space and time overhead, our design combines three interrelated techniques in a novel manner: runtime object instance classification, field virtualization, and bidirectional object layout. An experimental evaluation reveals that this mechanism can reduce the maximum heap occupancy of SpecJVM98 benchmarks by up to 18% and 14% on average while keeping the application execution overhead low.
16798311
Parameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four “real world” medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.
10093847
The Evil Twin
A girl falls in love with a man at her mother's funeral. After the funeral, she finds the best way to see that man again and realises who the man is ...