Topic
Evolving classification function
About: Evolving classification function is a research topic. Over the lifetime, 4 publications have been published within this topic receiving 52 citations.
Papers
16 Jun 2014
TL;DR: This paper proposes a novel approach, which uses minimum computational power and resources, to indentify Packed Executable (PEX), so as to spot the existence of malware software.
Abstract: Recent malware developments have the ability to remain hidden during infection and operation. They prevent analysis and removal, using various techniques, namely: obscure filenames, modification of file attributes, or operation under the pretense of legitimate programs and services. Also, the malware might attempt to subvert modern detection software, by hiding running processes, network connections and strings with malicious URLs or registry keys. The malware can go a step further and obfuscate the entire file with a packer, which is special software that takes the original malware file and compresses it, thus making all the original code and data unreadable. This paper proposes a novel approach, which uses minimum computational power and resources, to indentify Packed Executable (PEX), so as to spot the existence of malware software. It is an Evolving Computational Intelligence System for Malware Detection (ECISMD) which performs classification by Evolving Spiking Neural Networks (eSNN), in order to properly label a packed executable. On the other hand, it uses an Evolving Classification Function (ECF) for the detection of malwares and applies Genetic Algorithms to achieve ECF Optimization.
59 citations
27 Jun 2005
TL;DR: Evolving classification function (ECF), a special evolving connectionist systems (ECOS), is used to identify the suitable role of a robot from the data collected from the robot system in real time.
Abstract: For a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile robots perform soccer playing behaviors. Traditionally, a robot's role is determined by a closed-form function of a robot's postures relative to the target which usually cannot accurately describe real situations. In this paper, the robot role allocation problem is converted to the one of pattern classification. Evolving classification function (ECF), a special evolving connectionist systems (ECOS), is used to identify the suitable role of a robot from the data collected from the robot system in real time. The software and hardware platforms are established for data collection, learning and verification for this approach. The effectiveness of the approach are verified by the experimental studies
9 citations
TL;DR: It is demonstrated, through experiments, that the proposed evolutionary approach for malware detection in dual stack IPv4/IPv6 networks successfully evolved, and detect known and new, previously-unseen malware with high detection accuracy of 98.59% and low false positive rate of 0.26.
Abstract: The advent of internet protocol version 6 (IPv6) as a replacement of internet protocol version 4 (ipv4) has raised the necessity for efficient and effective malware detection techniques for IPv6 networks. Because of the evolvable and polymorphic malware, current malware detection technologies cannot cope with the exponential growth of malwares. This paper proposes a new intelligent approach based on adapted evolving classification function, for malware detection in dual stackIPv4/IPv6 networks, the proposed integrated approach consist of three modules, the first module is a malware portable executable (PE) file analyzer which generates a features of a malware from its executable file; the second module is a feature selector which selects the most important and informative features; and third module is an adapted evolving classification function that uses genetic algorithm to detect the malware in evolvable manner. A controlled environment of a dual stack IP4/IPv6 network was deployed to conduct a comprehensive experiment to validate our proposed intelligent malware detection approach. It is demonstrated, through experiments, that the proposed evolutionary approach for malware detection in dual stack IPv4/IPv6 networks successfully evolved, and detect known and new, previously-unseen malware with high detection accuracy of 98.59% and low false positive rate of 0.26.
Key words: Malware detection, evolving classification, genetic algorithm.
1 Jan 2008
TL;DR: In this paper, an evolving classification function (ECF) and a special evolving connectionist system (ECOS) are used to assign a suitable role for each robot in a team.
Abstract: Robotic soccer is an intelligent system where a group of mobile robots are controlled to perform soccer play (http://www.fira.net). The allocation of a suitable role for each robot in a team is a key for the success of the play. The paper treats this issue as one of pattern classification, and solves it with an Evolving classification function (ECF), a special evolving connectionist system (ECOS). A robot's role is determined by and evolves with the states of system ( robots and target ) in real time. The software and hardware platforms are set up for data collection and learning. The effectiveness of the proposed approach is verified by the experimental studies.
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2014 | 1 |
| 2012 | 1 |
| 2008 | 1 |
| 2005 | 1 |