The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In software system, there are some functions of great importance in controlling the whole process of software execution. When they are damaged, the software will suffer from catastrophic consequences caused by cascading failures. To accurately identify and protect these influential functions has become a necessary method in software security. Thus, in this study a new approach to efficiently mine...
Buffer overflow (BOF) vulnerabilities when present in code can be exploited to violate security objectives such as availability, confidentiality and integrity. They make up substantial portion of input manipulation attacks due to their common presence and ease of exploitation. In this study, the authors propose a hybrid approach combining static and dynamic program analysis with machine learning to...
According to the improvement of data mining technologies, big data now is a hot topic in various areas, such as Internet, finance, healthcare etc. As well as known, big data is collected and accumulated across a wide variety of fields fast and in real time. It is very important to find the structure from big data. In this paper, we focus on the neral network algorithm, Growing Hierarchical Self-Organizing...
This paper presents a learning algorithm for adaptive network intrusion detection based on clustering and naïve Bayesian classifier, which induces a hybridization of unsupervised and supervised learning processes. The proposed approach scales up the balance detection rates for different types of network intrusions, and keeps the false positives at acceptable level in network intrusion detection....
Anomaly detection is a domain that represents the key for the future of data mining. We will try to present some key anomaly detection methods applicable in the data mining process. Some methods are existing techniques as the DBSCAN algorithm and some have just been presented to the public recently and could be the answer to future anomaly detection development. One example is the filtering-and-refinement...
Today's fast developing modern information technology not only has a great impact on the social and economic activities but also more importantly has caused the innovation of modern auditing technology. In order to keep pace with the development of modern audit, the author adopted the method of computer data mining to analyze large quantities of data collected from the audited corporate, and work...
The following topics are dealt with: Internet; crowdsourcing; software engineering; cyber security; successive iterative decoding; correlated Rayleigh fading envelope; pseudorandom phase generation; nonuniform signal constellation; network coding; mobile Web service; mobile Web services; fault tolerant architecture; context aware approach; arabic language processing; smart grid; video techniques;...
This paper proposes a methodology and a tool to evaluate the security risk presented when using software components or systems. The risk is estimated based on known vulnerabilities existing on the software components. An automated tool is used to extract and aggregate information on vulnerabilities reported by users and available on public databases (e.g., OSVDB and NVD). This tool generates comprehensive...
Recently, the leak of domestic core technology of major business in Korea and the subsequent damage, has been increasing every year. Financial losses due to this leak are estimated to be about 220 trillion, which is equivalent to the gross budget of Korea Besides, the majority of the leaks are caused by former and current staff members, cooperated businesses, scientists and investment companies. This...
Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to...
DBSCAN is one of powerful density-based clustering algorithms for detecting outliers, but there are some difficulties in finding its parameters (epsilon and minpts). Currently, there is also no way to use DBSCAN with different parameters for different cluster when it is applied to anomaly detection when network traffic includes multiple traffic types with different characteristics. In this paper,...
The expansion of internet technology has made convenience. On the one hand various malicious code is produced. The number of malicious codes occurrence has dramatically increasing, and new or variant malicious code circulation very serious, So it is time to require analysis about malicious code. The being so malicious code pattern extract for malicious code properties of anti-virus company. Visualization...
Anomaly detection in data streams is the problem of extracting subsequences, which do not match an expected behavior. Its importance originates from its applicability in many fields such as system health monitoring, event detection in sensor networks, and detecting eco-system disturbances, etc. In detecting anomalous subsequences from data streams, the main challenge for the existing techniques is...
The following topics are dealt with: distributed and parallel computing; semantic Web; mobile networks; peer-to-peer computing; sealable computing; Internet; opportunistic and delay tolerant networks; agent and dependable systems; intelligent computing; communication networks; service oriented architecture; security; wireless sensor networks; privacy; ad hoc networks; life science modeling and computing;...
Stepping-stone is the most popular way used to attack other computers. Some insiders use stepping-stone to launch their attacks pretending to be outsiders. In this paper, we propose a novel algorithm to detect stepping-stone insider attacks through comparing outgoing and incoming connections. We modify the existing packet matching algorithm by introducing sliding window to make the algorithm more...
The following topics are dealt with: computer science applications; embedded systems; wireless channels; data analysis; mobility management; mobile robots; loop antenna; Internet; security of data; neural networks; expert systems; data mining; and natural languages.
The following topics are dealt with: vehicle detection; discrete time queueing system; CBMLAD; linear programming model; surface water quality assessment; fuzzy subgroups; computer aided optimum design; aerodynamic modeling; fuzzy logic controller; adaptive neural network; trajectory planning; nurbs model; XML data safety; sparse signal estimation; condition monitoring information model; ECG extraction;...
The alerts produced by the real time intrusion detection systems, e.g. Snort, can be difficult for security administrators to efficiently review and respond to, due to the enormous amount of messages generated in a short time frame. In this research, we developed a technique, the swarm based visual data mining approach (SVDM), to help user gain insight into the alert event data of the intrusion detection...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.