A common way to construct a fault model is injecting the fault into the system and observing the subsequent symptoms, e. g. event logs. However, fault features would vary during the propagation period, and present dif...
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A common way to construct a fault model is injecting the fault into the system and observing the subsequent symptoms, e. g. event logs. However, fault features would vary during the propagation period, and present different symptoms at different stage of the fault propagation process. The exiting detection window based feature extraction methods can only identify the early symptoms of a fault, but fail to detect the latter symptoms and cause false alarms. To solve the problem, we present a fault feature extraction method, called Companion State Tracer (CSTracer), which consists of 3 integrated steps: (1) pre-process logs to remove the unrelated logs;(2) construct a general identifier for the early symptoms of a fault;(3) construct a finite state machine model for the fault to trace the latter symptoms. CSTracer can persistently monitor a fault after the fault has been identified. We have justified the effectiveness of CSTracer in an enterprise cloud system. Compared with the existing, the results show that CSTracer has a better detection accuracy.
Spectrum trading is the promising method to improve spectrum efficiency from the perspective of economics. In this paper we propose a queueing-theory based spectrum trading model, where the primary user plays the serv...
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Spectrum trading is the promising method to improve spectrum efficiency from the perspective of economics. In this paper we propose a queueing-theory based spectrum trading model, where the primary user plays the server role providing spectrum to the secondary user who acts as the customer. The most significant challenge is how to optimize the spectrum trading model considering the server uncertainty which includes service state, service time, service area, service content and service price. We design a STACP queueing model according to the server attributes, so that the secondary user can choose the right queue quickly and reasonably according to its demand. Moreover, we further analyze the optimizing strategies for STACP model which can maximize the profit of the primary user and minimize the service cost of the secondary user. The simulation results demonstrate the analysis results.
As a complex, flexible and effective platform for network attacking, the botnet spreads widely in the Internet. Botnets can provide the botmasters with the ability to launch large-scale malicious activities such as sp...
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As a complex, flexible and effective platform for network attacking, the botnet spreads widely in the Internet. Botnets can provide the botmasters with the ability to launch large-scale malicious activities such as spamming and DDoS (distributed Denial of Service) attacks. Botnets are continuously bringing more and more severe threats, so that the study on botnets has already become one of the focuses in the field of network security. However, in recent years, some new developments of botnets are challenging the existing understanding on botnets. In this paper, according to the new conditions of botnets and the researches in existence, a definition of botnet is proposed based on the works of other researchers, taxonomies of botnets are introduced respectively from the views of network structure, dependency and delivery pattern of C&C (Command and Control) information. Then the techniques on detecting, measuring and restraining botnets are analyzed systematically. In the end, we give the evolution trends of botnets and the future research trends in this area.
As the foundation of cloud computing, Server consolidation allows multiple computer infrastructures running as virtual machines in a single physical node. It improves the utilization of most kinds of resource but memo...
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As the foundation of cloud computing, Server consolidation allows multiple computer infrastructures running as virtual machines in a single physical node. It improves the utilization of most kinds of resource but memory under x86 architecture. Because of inaccurate memory usage estimate and the lack of memory resource management, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. Furthermore, memory becomes insufficient for a physical server when a lot of virtual machines depend on it. In order to improve this, we present a dynamic memory scheduling system called DMSS, which can manage memory resources in server consolidation environments and allocate memory among virtual machines on demand. We have designed and implemented the corresponding memory scheduling policy based on Xen virtualization platform to enhance memory efficiency and achieve service level agreement. The benchmark shows that DMSS can make an accurate and rapid response to memory changes and save more than 30% physical memory with less than 5% performance degradation. DMSS actually brings in economic benefits to cloud service providers because more virtual machines can be accommodated at lower costs.
The malicious code detection based on behaviors has proved effective. But there are high false positives and high false negatives when using this method. Because the behaviors are always out-of-order and redundant. To...
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The malicious code detection based on behaviors has proved effective. But there are high false positives and high false negatives when using this method. Because the behaviors are always out-of-order and redundant. To solve these problems, this paper proposes a detection method based on statistical analysis. Firstly, this method uses association rules to sort out the behaviors, and then we can get the integrated and accurate behavior sequences. Secondly, by using the association algorithm we can pick up the signatures of behavior sequences. In addition, this method can detect the signatures to judge the threat based on statistical analysis. Experimental results indicate that it can reduce both the false positives and the false negatives effectively.
Malicious code detection based on behaviors is the development direction of anti-virus techniques. However, the current detection methods based on this theory expose several problems such as the unclearness of behavio...
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Malicious code detection based on behaviors is the development direction of anti-virus techniques. However, the current detection methods based on this theory expose several problems such as the unclearness of behavior sequence analysis and the high false negatives. For this situation, this paper proposes a malicious code detection method based on least-squares estimation. In this method, it correlates program behaviors with time and subject-object, and then constitutes an accurate and complete behavior sequence. It can provide a preprocessing method for the subsequent detection. In order to improve the accuracy and intelligence of malicious code detection, we introduce the concept of expert subjective degree. By modeling malicious samples based on least-squares estimation we can train the Expert Subjective Degree Vector (ESDV) and simulate experts to judge the threat values of malicious codes. Experiments show that this method is more accurate than the current ways to detect the malicious codes which execute themselves in sub-period and sub-process ways, so it can be used as an effective complement of the current anti-virus software.
In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource ef...
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In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource efficiently and fairly is an interesting and practical problem. In this paper we proposed a cooperative spectrum sharing strategy (RT-CSS) for the relay-trading mode from the fairness view. The cooperative SUs are gathered in a cooperative sharing group (CSG), and contribution metric (CM) is proposed to measure each CSG member's contribution to CSG as well as benefit from CSG. The adjustment of CM can guarantee the fairness and efficiency of spectrum sharing. The numerical simulation shows that RT-CSS can achieve better performance than the sense-uncooperative mode.
Developing parallel applications on heterogeneous processors is facing the challenges of 'memory wall',due to limited capacity of local storage,limited bandwidth and long latency for memory access. Aiming at t...
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Developing parallel applications on heterogeneous processors is facing the challenges of 'memory wall',due to limited capacity of local storage,limited bandwidth and long latency for memory access. Aiming at this problem,a parallelization approach was proposed with six memory optimization schemes for CG,four schemes of them aiming at all kinds of sparse matrix-vector multiplication (SPMV) operation. Conducted on IBM QS20,the parallelization approach can reach up to 21 and 133 times speedups with size A and B,respectively,compared with single power processor element. Finally,the conclusion is drawn that the peak bandwidth of memory access on Cell BE can be obtained in SPMV,simple computation is more efficient on heterogeneous processors and loop-unrolling can hide local storage access latency while executing scalar operation on SIMD cores.
Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendatio...
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Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendation framework to help the user transfer from intentions to itinerary planning, which join physical trajectories and information of location based social networks. The main contributions are: (1) Build the category based activity scheduling model;(2) Design and implement the category tree based POI (point or interest) query strategy and algorithm;(3) Propose the Voronoi graph based GPS trajectory analysis method to build traffic information networks;(4) Combine social networks with traffic information networks to implement category based recommendation by ant colony algorithm. The study conducts experiments on datasets from FourSquare and GeoLife project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.
Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer ...
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Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circlecovering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.
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