A Signature-Behavior-Based P2P Worm Detection Approach

2009 ◽  
Author(s):  
Yu Yao ◽  
Yong Li ◽  
Fu-xiang Gao ◽  
Ge Yu
Keyword(s):  
Worm Detection ◽  
Detection Approach ◽  
Behavior Based ◽  
P2p Worm

A Behavior-Based Autonomous Agent Possessing System Detectable Error

2000 ◽  
Author(s):  
Keith L. Bearden ◽  
Mark L. Nowack ◽  
Wade O. Troxell
Keyword(s):  
Error Detection ◽  
System Level ◽  
Autonomous Agent ◽  
Main Function ◽  
External Observer ◽  
Processor Time ◽  
Detection Approach ◽  
Error Space ◽  
Behavior Based ◽  
Sensor Errors

Abstract A great deal of recent research is devoted to increasing the robustness and capability of behavior-based robotic systems. Behavior-based systems are extremely susceptible to sensor errors. To overcome this, most researchers have added processors to the basic system to compare multiple redundant sensors. This is an effective error detection approach, but it costs processor time, increases complexity, and can actually reduce reliability. Most importantly such systems lack the ability to self-detect error. All other forms of representation are unable to determine system level functional failures without the use of an external observer. This paper proposes a divergence from detecting sensor error to detecting functional error. By looking at the functional error space, the system can determine an error and move away from the error. This method will not determine a sensory failure as the cause of the functional failure; rather, this method determines that the system is not performing its main function and then tries something else. This leads to a system that can function with the loss of forty percent of its sensory capability for either the case of a disconnected sensor or a stuck sensor.

MoG: Behavior-Obfuscation Resistance Malware Detection

2019 ◽  
Vol 62 (12) ◽  
pp. 1734-1747
Author(s):  
Binlin Cheng ◽  
Jinjun Liu ◽  
Jiejie Chen ◽  
Shudong Shi ◽  
Xufu Peng ◽  
...  
Keyword(s):  
Malware Detection ◽  
Dependency Graph ◽  
The Internet ◽  
Security Threat ◽  
Detection Approach ◽  
The Family ◽  
Complete Resistance ◽  
Effectiveness And Efficiency ◽  
Behavior Based

Abstract Malware brings a big security threat on the Internet today. With the great increasing malware attacks. Behavior-based detection approaches are one of the major method to detect zero-day malware. Such approaches often use API calls to represent the behavior of malware. Unfortunately, behavior-based approaches suffer from behavior obfuscation attacks. In this paper, we propose a novel malware detection approach that is both effective and efficient. First, we abstract the API call to object operation. And then we generate the object operation dependency graph based on these object operations. Finally, we construct the family dependency graph for a malware family. Our approach use family dependency graph to represent the behavior of malware family. The evaluation results show that our approach can provide a complete resistance to all types of behavior obfuscation attacks, and outperforms existing behavior-based approaches in terms of better effectiveness and efficiency.

Game Playing Tactic as a Behavioral Biometric for Human Identification

2010 ◽  
pp. 385-413 ◽  
Author(s):  
Roman V. Yampolskiy ◽  
Venu Govindaraju
Keyword(s):  
Intrusion Detection ◽  
Human Identification ◽  
Experimental Results ◽  
Game Playing ◽  
Behavioral Profiles ◽  
Security Breaches ◽  
Detection Approach ◽  
Behavior Based ◽  
Behavioral Signature

This chapter expends behavior based intrusion detection approach to a new domain of game networks. Specifically, our research shows that a behavioral biometric signature can be generated based on the strategy used by an individual to play a game. We wrote software capable of automatically extracting behavioral profiles for each player in a game of poker. Once a behavioral signature is generated for a player, it is continuously compared against player’s current actions. Any significant deviations in behavior are reported to the game server administrator as potential security breaches. In this chapter, we report our experimental results with user verification and identification, as well as our approach to generation of synthetic poker data and potential spoofing approaches of the developed system. We also propose utilizing techniques developed for behavior based recognition of humans to the identification and verification of intelligent game bots. Our experimental results demonstrate feasibility of such methodology.

P2P worm detection based on application identification

2007 ◽  
Vol 1 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Chunhe Xia ◽  
Yunping Shi ◽  
Xiaojian Li ◽  
Wei Gao
Keyword(s):  
Worm Detection ◽  
Application Identification ◽  
P2p Worm
Sign in / Sign up

Export Citation Format

Share Document