Behavior-based Techniques Research Articles

An exploratory analysis of behavior-based and other management techniques to improve avian conservation translocations

A diverse array of management techniques is used to improve the success of captive bred animals after they are released into the wild. These techniques include behavior-based management methods, such as predator aversion and foraging trainings. However, it is uncertain which management methods are useful for increasing survival of captive bred birds during the post-release establishment phase. Here we performed an exploratory multimodel inference analysis to examine which management methods, including behavior-based, help improve avian survival within the first year post-release. Using both peer-reviewed articles and grey literature from 76 sources over the last 37 years, we identified 91 avian conservation translocation events with first year survival data from 15 taxonomic orders with an emphasis on Galliformes, Gruiformes, and Otidiformes. A priori models were created, representing each management method and its potential effect on survival, and ranked using Kullback-Leibler information. Behavior-based techniques, such as wild food exposure and acclimating birds to the release area pre-release, and non-behavior techniques, such as in situ management, protected release area, and providing post-release food aid, all led to increases in bird survival after release. These methods should be explored further for increasing avian survival, improving conservation translocations, and advancing endangered species conservation.

Evolution of Driver Fatigue Detection Techniques—A Review From 2007 to 2021

Driver fatigue is the most important factor in the increase in the frequency of traffic accidents and fatalities every year. Fatigue impairs driving performance through a lack of concentration and slower reaction time. Therefore, a fatigue detection system is very important for safe driving. This paper presents a systematic literature review of the research conducted over the last 15 years to provide information about the evolution of various driver fatigue detection (DFD) systems with the advancement of technologies. In the domain of DFD, researchers have used different approaches such as physiological, behavioral, vehicular, and mixed. Findings from the study indicate that physiological and behavior-based techniques are widely used by the authors, whereas vehicular features are very scarcely used. Analysis of papers shows that researchers are more likely to utilize a combination of physiological and behavior-based approaches to identify driving fatigue or drowsiness. The outcome of this literature review could help practitioners to improve existing fatigue detection technologies by application of the different approaches for fatigue identification and measurement.

Detecting Malware based on Analyzing Abnormal behaviors of PE File

Attack by spreading malware is a dangerous attack form that is very difficult to detect and prevent. Attack techniques that spread malware through users and then escalate privileges in the system are increasingly used by attackers. The three main methods and techniques for tracking and detecting malware that is being currently studied and applied include signature-based, behavior-based, and hybrid techniques. In particular, the behavior-based technique with the support of machine learning algorithms has given high efficiency. On the other hand, in reality, attackers often find various ways and techniques to hide behaviors of the malware based on the Portable Executable File Format (PE File) of the malware. This makes it difficult for surveillance systems to detect malware. From the above reasons, in this paper, we propose a malware detection method based on the PE File analysis technique using machine learning and deep learning algorithms. Our main contribution in this paper is proposing some features that represent abnormal behaviors of malware based on PE File and the efficiency of some machine learning algorithms in the classification process.

Minimal contrast frequent pattern mining for malware detection

Currently, most of widely-used malware detection software products utilize signature-based algorithms to recognize threats. However, this approach is problematic because it relies on file hashes and byte (or instruction) signatures. Consequently, obfuscation techniques are straightforward ways to modify these features syntactically and evade detection. Since it is harder for an attacker to radically change the behavior of a malware than to morph its syntactic structure, behavior-based detection techniques are a promising solution to this problem. However behavior-based techniques can be applied using static analysis or dynamic analysis or hybrid analysis. While dynamic behavior-based detection methods are time consuming and fail to obtain all possible malicious execution traces, most of static behavior-based approaches suffer from a high growth rate in the number of behavioral signatures and suffer from high false positive rates. In this paper, we present a new graph mining method to detect variants of malware using static analysis, while covering the existing defects. We propose a novel algorithm, called minimal contrast frequent subgraph miner algorithm (MCFSM), for extracting minimal discriminative and widely employed malicious behavioral patterns which can identify precisely an entire family of malicious programs, in contrast to another set of benign programs. The proposed method shows high detection rates and low false positive rates and generates a limited number of behavioral malware signatures.

Going beyond sprays and killing agents: Exclusion, sterilization and disruption for insect pest control in pome and stone fruit orchards

Because of their perennial nature, orchards harbor one of the most complex ecosystems in agriculture. Nevertheless, crop protection programs still mainly focus on pesticides (synthetic or organic-approved) to prevent or limit the action of so-called noxious species in these systems. Killing agents represent the dominant paradigm and have been used in agriculture for decades. This paper synthesizes the available literature about the other approaches, more suited to organic farming, which recognize that the radicalness of killing is not necessary to prevent crop losses. Exclusion barriers represent one of the most readily available means of protecting the crop that way, but other behavior-based techniques have been developed, such as sterile insect technique and mating disruption. While there are many other possibilities, these are the three approaches that are currently getting the most interest in tree fruit production, due to ecological and agronomical characteristics, some of which will be detailed in this review.

Behavior-based features model for malware detection

The sharing of malicious code libraries and techniques over the Internet has vastly increased the release of new malware variants in an unprecedented rate. Malware variants share similar behaviors yet they have different syntactic structure due to the incorporation of many obfuscation and code change techniques such as polymorphism and metamorphism. The different structure of malware variants poses a serious problem to signature-based detection technique, yet their similar exhibited behaviors and actions can be a remarkable feature to detect them by behavior-based techniques. Malware instances also largely depend on API calls provided by the operating system to achieve their malicious tasks. Therefore, behavior-based detection techniques that utilize API calls are promising for the detection of malware variants. In this paper, we propose a behavior-based features model that describes malicious action exhibited by malware instance. To extract the proposed model, we first perform dynamic analysis on a relatively recent malware dataset inside a controlled virtual environment and capture traces of API calls invoked by malware instances. The traces are then generalized into high-level features we refer to as actions. We assessed the viability of actions by various classification algorithms such as decision tree, random forests, and support vector machine. The experimental results demonstrate that the classifiers attain high accuracy and satisfactory results in the detection of malware variants.

Comparison of behavior-based and planning techniques on the small robot maze exploration problem

A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered-a radial basis function neural network trained by evolutionary algorithm and a traditional reinforcement learning algorithm over a finite agent state space. The control architecture based on localization and planning is compared to the former method.

Modeling interaction strategies using POS: An application to soccer robots

As seen earlier POS is a general agent conversation protocol engineering formalism that has proved efficient when used in communities of software information agents. The aim of this paper is to show how much POS is simple, easy to use, and very appropriate for implementing interaction protocols in a collaborative agent setting. In order to exemplify our approach we focus on an application domain with real time constraints such as soccer robots and show how an inherently symbolic abstract system like POS can be neatly integrated with agents whose internal architecture is reactive and relies on bottom-up behavior-based techniques. First, we shortly discuss aspects of heterogeneity with respect to soccer robots and present the RoboCup experimental setting. Then we sketch the difficulties that arise when trying to coordinate an heterogeneous team with conventional methods. In response, we introduce some elements of the POS (Protocol Operational Semantics) model. The following sections examine several basic behaviors of the JavaSoccer simulation environment as it is later used along with TeamBots to highlight POS' capabilities, demonstrate how POS is a suited theoretical tool and leads to extremely compact and modular code due to its high expressive power, and discuss the obtained results. Finally we conclude the article with a comparison to related works as well as some perspectives about the engineering of interaction protocols.

An environment for security protocol intrusion detection

Secure electronic communication relies on cryptography. Even with perfect encryption, communication may be compromised without effective security protocols for key exchange, authentication, etc. We are now seeing proliferation of large secure environments characterized by high volume, encrypted traffic between principals, facilitated by Public Key Infrastructures (PKI). PKIs are dependent on security protocols. Unfortunately, security protocols are susceptible to subtle errors. To date, we have relied on formal methods to tell us if security protocols are effective. These methods do not provide complete or measurable protocol security. Security protocols are also subject to the same implementation and administrative vulnerabilities as communication protocols. As a result, we will continue to operate security protocols that have flaws. In this paper, we describe a method and architecture to detect intrusions in security protocol environments such as Public Key Infrastructures. Our method is based on classic intrusion detection techniques of knowledge-based and behavior-based techniques detection. 2 Section

Cooperation without deliberation: A minimal behavior-based approach to multi-robot teams

While terminology and some concepts of behavior-based robotics have become widespread, the central ideas are often lost as researchers try to scale behavior to higher levels of complexity. “Hybrid systems” with model-based strategies that plan in terms of behaviors rather than simple actions have become common for higher-level behavior. We claim that a strict behavior-based approach can scale to higher levels of complexity than many robotics researchers assume, and that the resulting systems are in many cases more efficient and robust than those that rely on “classical AI” deliberative approaches. Our focus is on systems of cooperative autonomous robots in dynamic environments. We will discuss both claims that deliberation and explicit communication are necessary to cooperation and systems that cooperate only through environmental interaction. In this context we introduce three design principles for complex cooperative behavior— minimalism, statelessness and tolerance—and present a RoboCup soccer system that matches the sophistication of many deliberative soccer systems while exceeding their robustness, through the use of strict behavior-based techniques with no explicit communication.

Experimental evaluation of behavior-based failure-detection schemes in real-time communication networks

Effective detection of failures is essential for reliable communication services. Traditionally, non-real-time computer networks have relied on behavior-based techniques for detecting communication failures. That is, each node uses heartbeats to detect the failure of its neighbors and the end-to-end transport protocol (e.g., TCP) achieves reliable communication by acknowledgment/retransmission. Recently, there has been a growing demand for reliable "real-time" communication, but little research has been done on the failure detection problem. In this paper, we present two behavior-based failure-detection schemes-neighbor detection and end-to-end detection-for reliable real-time communication services and experimentally evaluate their effectiveness. Specifically, we measure and analyze the coverage and latency of these detection schemes through fault-injection experiments. The experimental results have shown that nearly all failures can be detected very quickly by the neighbor detection scheme, while the end-to-end detection scheme uncovers the remaining failures with larger detection latencies.