206594758
Detecting Collusion Attacks in Security Protocols
Security protocols have been widely used to safeguard secure electronic transactions. We usually assume that principals are credible and shall not maliciously disclose their individual secrets to someone else. Nevertheless, it is impractical to completely ignore the possibility that some principals may collude in private to achieve a fraudulent or illegal purpose. Therefore, it is critical to address the possibility of collusion attacks in order to correctly analyse security protocols. This paper proposes a framework by which to detect collusion attacks in security protocols. The possibility of security threats from insiders is especially taken into account. The case study demonstrates that our methods are useful and promising in discovering and preventing collusion attacks.
10107398
Yet Another Induction Algorithm
Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate that the rules produced by our induction algorithm have high consistency and simplicity.
39700388
Surface Spatial Index Structure of High-Dimensional Space
This paper proposes a spatial index structure based on a new space-partitioning method. Previous research proposed various high dimensional index structures. However, when dimensionality becomes high, the effectiveness of the spatial index structure disappears. This problem is called the “curse of dimensionality”. This paper focuses on the fact that the volume of high dimensional space is mostly occupied by its surface and then proposes a new surface index structure. The utility of this new surface spatial index structure is illustrated through a series of experiments.
2536834
Extensible detection and indexing of highlight events in broadcasted sports video
Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used.
24794561
Finding Short Patterns to Classify Text Documents
Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
12775848
Analyzing Inconsistency Toward Enhancing Integration of Biological Molecular Databases
The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.
5913277
OceanStore: an architecture for global-scale persistent storage
OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.
3132727
Query Processing over Incomplete Autonomous Databases
Incompleteness due to missing attribute values (aka "null values") is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such possible answers and gauge their relevance by accessing their likelihood of being pertinent answers to the query. The autonomous nature of web databases poses several challenges in realizing this objective. Such challenges include the restricted access privileges imposed on the data, the limited support for query patterns, and the bounded pool of database and network resources in the web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges. Our technique involves reformulating the user query based on mined correlations among the database attributes. The reformulated queries are aimed at retrieving the relevant possible answers in addition to the certain answers. QPIAD is able to gauge the relevance of such queries allowing tradeoffs in reducing the costs of database query processing and answer transmission. To support this framework, we develop methods for mining attribute correlations (in terms of Approximate Functional Dependencies), value distributions (in the form of Naive Bayes Classifiers), and selectivity estimates. We present empirical studies to demonstrate that our approach is able to effectively retrieve relevant possible answers with high precision, high recall, and manageable cost.
19799763
An approach to isochronous slot reuse for multimedia services on DQDB network
This paper discusses an isochronous connection management protocol and its slot-reuse scheme for multimedia services on DQDB network. For asynchronous services, slot-reuse improves slot utilization, which has already been discussed in depth. However only few literature addressed the slot-reuse for synchronous services. Our proposed scheme reuses video slots for either synchronous or asynchronous services according to the need of downstream nodes. Simulation shows that the scheme significantly increases the maximum number of video connections as well as reduces the failure rate of connection request comparing with that without slot-reuse.
10399949
Quantifying Network Denial of Service: A Location Service Case Study
Network Denial of Service (DoS) attacks are increasing in frequency, severity and sophistication, making it desirable to measure the resilience of systems to DoS attacks. In this paper, we propose a simulation-based methodology and apply it to attacks on object location services such as DNS. Our results allow us to contrast the DoS resilience of three distinct architectures for object location.
406686
Filtering of shrew DDoS attacks in frequency domain
The shrew distributed denial of service (DDoS) attacks are periodic, bursty, and stealthy in nature. They are also known as reduction of quality (RoQ) attacks. Such attacks could be even more detrimental than the widely known flooding DDoS attacks because they damage the victim servers for a long time without being noticed, thereby denying new visitors to the victim servers, which are mostly e-commerce sites. Thus, in order to minimize the huge monetary losses, there is a pressing need to effectively detect such attacks in real-time. Unfortunately, effective detection of shrew attacks remains an open problem. In this paper, we meet this challenge by proposing a new signal processing approach to identifying and detecting the attacks by examining the frequency-domain characteristics of incoming traffic flows to a server. A major strength of our proposed technique is that its detection time is less than a few seconds. Furthermore, the technique entails simple software or hardware implementations, making it easily deployable in a real-life network environment