Query: Social engineering attack framework

Relevant passages:
Ontology Development 101 : A Guide to Creating Your First Ontology <=SEP=> 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

Generic Taxonomy of Social Engineering Attack <=SEP=> Social engineering is a type of attack that allows unauthorized access to a system to achieve specific objective. Commonly, the purpose is to obtain information for social engineers. Some successful social engineering attacks get victims’ information via human based retrieval approach, example technique terms as dumpster diving or shoulder surfing attack to get access to password. Alternatively, victims’ information also can be stolen using technical-based method such as from pop-up windows, email or web sites to get the password or other sensitive information. This research performed a preliminary analysis on social engineering attack taxonomy that emphasized on types of technical-based social engineering attack. Results from the analysis become a guideline in proposing a new generic taxonomy of Social Engineering Attack (SEA).

Dissecting social engineering <=SEP=> 

The urgency for effective user privacy-education to counter social engineering attacks on secure computer systems <=SEP=> Trusted people can fail to be trustworthy when it comes to protecting their aperture of access to secure computer systems due to inadequate education, negligence, and various social pressures. People are often the weakest link in an otherwise secure computer system and, consequently, are targeted for social engineering attacks. Social Engineering is a technique used by hackers or other attackers to gain access to information technology systems by getting the needed information (for example, a username and password) from a person rather than breaking into the system through electronic or algorithmic hacking methods. Such attacks can occur on both a physical and psychological level. The physical setting for these attacks occurs where a victim feels secure: often the workplace, the phone, the trash, and even on-line. Psychology is often used to create a rushed or officious ambiance that helps the social engineer to cajole information about accessing the system from an employee.
 Data privacy legislation in the United States and international countries that imposes privacy standards and fines for negligent or willful non-compliance increases the urgency to measure the trustworthiness of people and systems. One metric for determining compliance is to simulate, by audit, a social engineering attack upon an organization required to follow data privacy standards. Such an organization commits to protect the confidentiality of personal data with which it is entrusted.
 This paper presents the results of an approved social engineering audit made without notice within an organization where data security is a concern. Areas emphasized include experiences between the Social Engineer and the audited users, techniques used by the Social Engineer, and other findings from the audit. Possible steps to mitigate exposure to the dangers of Social Engineering through improved user education are reviewed.

Ontologies: principles, methods and applications <=SEP=> This paper is intended to serve as a comprehensive introduction to the emerging eld concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld, considering various case studies, software tools for ontology development, key research issues and future prospects. AIAI-TR-191 Ontologies Page i

Highly Sensitive, Flexible, and Wearable Pressure Sensor Based on a Giant Piezocapacitive Effect of Three-Dimensional Microporous Elastomeric Dielectric Layer. <=SEP=> We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pressure-sensing device for pulse monitoring at the human wrist. Finally, we demonstrate a pressure sensor array pad for the recognition of spatially distributed pressure information on a plane. Our sensor, with its excellent pressure-sensing performance, marks the realization of a true tactile pressure sensor presenting highly sensitive responses to the entire tactile pressure range, from ultralow-force detection to high weights generated by human activity.

Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning <=SEP=> Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.

A step toward irrationality: using emotion to change belief <=SEP=> Emotions have a powerful impact on behavior and beliefs. The goal of our research is to create general computational models of this interplay of emotion, cognition and behavior to inform the design of virtual humans. Here, we address an aspect of emotional behavior that has been studied extensively in the psychological literature but largely ignored by computational approaches, emotion-focused coping. Rather than motivating external action, emotion-focused coping strategies alter beliefs in response to strong emotions. For example an individual may alter beliefs about the importance of a goal that is being threatened, thereby reducing their distress. We present a preliminary model of emotion-focused coping and discuss how coping processes, in general, can be coupled to emotions and behavior. The approach is illustrated within a virtual reality training environment where the models are used to create virtual human characters in high-stress social situations.

Perching and takeoff of a robotic insect on overhangs using switchable electrostatic adhesion <=SEP=> For aerial robots, maintaining a high vantage point for an extended time is crucial in many applications. However, available on-board power and mechanical fatigue constrain their flight time, especially for smaller, battery-powered aircraft. Perching on elevated structures is a biologically inspired approach to overcome these limitations. Previous perching robots have required specific material properties for the landing sites, such as surface asperities for spines, or ferromagnetism. We describe a switchable electroadhesive that enables controlled perching and detachment on nearly any material while requiring approximately three orders of magnitude less power than required to sustain flight. These electroadhesives are designed, characterized, and used to demonstrate a flying robotic insect able to robustly perch on a wide range of materials, including glass, wood, and a natural leaf.

Key parameters influencing the behavior of Steel Plate Shear Walls (SPSW) <=SEP=> The complex behavior of Steel Plate Shear Walls (SPSW) is investigated herein through nonlinear FE simulations. A 3D detailed FE model is developed and validated utilizing experimental results available in the literature. The influence of key parameters on the structural behavior is investigated. The considered parameters are: the infill plate thickness, the beam size, the column size, the infill plate material grade and the frame material grade. Several structural responses are used as criteria to quantify their influence on the SPSW behavior. The evaluated structural responses are: yield strength, yield displacement, ultimate strength, initial stiffness and secondary stiffness. The results show that, overall the most influential parameter is the infill plate thickness followed by the beam size. Also, it was found that the least influential parameter is the frame material grade.

Design for Reliability of Low-voltage, Switched-capacitor Circuits <=SEP=> Design for Reliability of Low-voltage, Switched-capacitor Circuits by Andrew Masami Abo Doctor of Philosophy in Engineering University of California, Berkeley Professor Paul R. Gray, Chair Analog, switched-capacitor circuits play a critical role in mixed-signal, analogto-digital interfaces. They implement a large class of functions, such as sampling, filtering, and digitization. Furthermore, their implementation makes them suitable for integration with complex, digital-signal-processing blocks in a compatible, low-cost technology–particularly CMOS. Even as an increasingly larger amount of signal processing is done in the digital domain, this critical, analogto-digital interface is fundamentally necessary. Examples of some integrated applications include camcorders, wireless LAN transceivers, digital set-top boxes, and others. Advances in CMOS technology, however, are driving the operating voltage of integrated circuits increasingly lower. As device dimensions shrink, the applied voltages will need to be proportionately scaled in order to guarantee long-term reliability and manage power density. The reliability constraints of the technology dictate that the analog circuitry operate at the same low voltage as the digital circuitry. Furthermore, in achieving low-voltage operation, the reliability constraints of the technology must not be violated. This work examines the voltage limitations of CMOS technology and how analog circuits can maximize the utility of MOS devices without degrading relia-

DWCMM: The Data Warehouse Capability Maturity Model <=SEP=> Data Warehouses and Business Intelligence have become popular fields of research in recent years. Unfortunately, in daily practice many Data Warehouse and Business Intelligence solutions still fail to help organizations make better decisions and increase their profitability, due to intransparent complexities and project interdependencies. In addition, emerging application domains such as Mobile Learning & Analytics heavily depend on a wellstructured data foundation with a longitudinally prepared architecture. Therefore, this research presents the Data Warehouse Capability Maturity Model (DWCMM) which encompasses both technical and organizational aspects involved in developing a Data Warehouse environment. The DWCMM can be used to help organizations assess their current Data Warehouse solution and provide them with guidelines for future improvements. The DWCMM consists of a maturity matrix and a maturity assessment questionnaire with 60 questions. The DWCMM has been evaluated empirically through expert interviews and case studies. We conclude that the DWCMM can be successfully applied in practice and that organizations can intelligibly utilize the DWCMM as a quickscan instrument to jumpstart their Data Warehouse and Business Intelligence improvement processes.

Multiple-resonator-based bandpass filters <=SEP=> This article describes a class of recently developed multiple-mode-resonator-based bandpass filters for ultra-wide-band (UWB) transmission systems. These filters have many attractive features, including a simple design, compact size, low loss and good linearity in the UWB, enhanced out-of-band rejection, and easy integration with other circuits/antennas. In this article, we present a variety of multiple-mode resonators with stepped-impedance or stub-loaded nonuniform configurations and analyze their properties based on the transmission line theory. Along with the frequency dispersion of parallel-coupled transmission lines, we design and implement various filter structures on planar, uniplanar, and hybrid transmission line geometries.

Enhanced ARP: preventing ARP poisoning-based man-in-the-middle attacks <=SEP=> In this letter, an enhanced version of Address Resolution Protocol (ARP) is proposed to prevent ARP poisoning-based Man-in-the-Middle (MITM) attacks. The proposed mechanism is based on the following concept. When a node knows the correct Media Access Control (MAC) address for a given IP address, if it retains the IP/MAC address mapping while that machine is alive, then MITM attack is impossible for that IP address. In order to prevent MITM attacks even for a new IP address, a voting-based resolution mechanism is proposed. The proposed scheme is backward compatible with existing ARP and incrementally deployable.

Time series classification with ensembles of elastic distance measures <=SEP=> Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area.

Recovering punctuation marks for automatic speech recognition <=SEP=> This paper shows results of recovering punctuation over speech transcriptions for a Portuguese broadcast news corpus. The approach is based on maximum entropy models and uses word, part-of-speech, time and speaker information. The contribution of each type of feature is analyzed individually. Separate results for each focus condition are given, making it possible to analyze the differences of performance between planned and spontaneous speech.

Optical character recognition of the Orthodox Hellenic Byzantine Music notation <=SEP=> In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the Orthodox Hellenic Byzantine Music Notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a Nearest Neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4 % was achieved, in a database of about 18,000 byzantine character patterns that have been developed for the needs of the system. Optical music recognition Off-line character recognition, Byzantine Music, Byzantine Music Notation, Wavelets, Projections, Neural Networks Contour processing, Nearest Neighbor Classifier Byzantine Music Data Base

DRAW: A Recurrent Neural Network For Image Generation <=SEP=> This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

A Comparison of Features for Android Malware Detection <=SEP=> With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.

CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings <=SEP=> This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying fine-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.

A 5-DOF rotation-symmetric parallel manipulator with one unconstrained tool rotation <=SEP=> This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.

A Framework based on SDN and Containers for Dynamic Service Chains on IoT Gateways <=SEP=> In this paper, we describe a new approach for managing service function chains in scenarios where data from Internet of Things (IoT) devices is partially processed at the network edge. Our framework is enabled by two emerging technologies, Software-Defined Networking (SDN) and container based virtualization, which ensure several benefits in terms of flexibility, easy programmability, and versatility. These features are well suitable with the increasingly stringent requirements of IoT applications, and allow a dynamic and automated network service chaining. An extensive performance evaluation, which has been carried out by means of a testbed, seeks to understand how our proposed framework performs in terms of computational overhead, network bandwidth, and energy consumption. By accounting for the constraints of typical IoT gateways, our evaluation tries to shed light on the actual deployability of the framework on low-power nodes.

Diagnostic Accuracy of Clinical Tests for Morton's Neuroma Compared With Ultrasonography. <=SEP=> The aim of the present study was to assess the diagnostic accuracy of 7 clinical tests for Morton's neuroma (MN) compared with ultrasonography (US). Forty patients (54 feet) were diagnosed with MN using predetermined clinical criteria. These patients were subsequently referred for US, which was performed by a single, experienced musculoskeletal radiologist. The clinical test results were compared against the US findings. MN was confirmed on US at the site of clinical diagnosis in 53 feet (98%). The operational characteristics of the clinical tests performed were as follows: thumb index finger squeeze (96% sensitivity, 96% accuracy), Mulder's click (61% sensitivity, 62% accuracy), foot squeeze (41% sensitivity, 41% accuracy), plantar percussion (37% sensitivity, 36% accuracy), dorsal percussion (33% sensitivity, 26% accuracy), and light touch and pin prick (26% sensitivity, 25% accuracy). No correlation was found between the size of MN on US and the positive clinical tests, except for Mulder's click. The size of MN was significantly larger in patients with a positive Mulder's click (10.9 versus 8.5 mm, p = .016). The clinical assessment was comparable to US in diagnosing MN. The thumb index finger squeeze test was the most sensitive screening test for the clinical diagnosis of MN.

Why Lurkers Lurk <=SEP=> The goal of this paper is to address the question: ‘why do lurkers lurk?’ Lurkers reportedly makeup the majority of members in online groups, yet little is known about them. Without insight into lurkers, our understanding of online groups is incomplete. Ignoring, dismissing, or misunderstanding lurking distorts knowledge of life online and may lead to inappropriate design of online environments. To investigate lurking, the authors carried out a study of lurking using in-depth, semi-structured interviews with ten members of online groups. 79 reasons for lurking and seven lurkers’ needs are identified from the interview transcripts. The analysis reveals that lurking is a strategic activity involving more than just reading posts. Reasons for lurking are categorized and a gratification model is proposed to explain lurker behavior.

Learning Better Embeddings for Rare Words Using Distributional Representations <=SEP=> There are two main types of word representations: low-dimensional embeddings and high-dimensional distributional vectors, in which each dimension corresponds to a context word. In this paper, we initialize an embedding-learning model with distributional vectors. Evaluation on word similarity shows that this initialization significantly increases the quality of embeddings for rare words.

Text comparison using word vector representations and dimensionality reduction <=SEP=> This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (tSNE) and how it can be implemented using Python. The technique provides a bird’s-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.

Engineering Quality and Reliability in Technology-Assisted Review <=SEP=> The objective of technology-assisted review ("TAR") is to find as much relevant information as possible with reasonable effort. Quality is a measure of the extent to which a TAR method achieves this objective, while reliability is a measure of how consistently it achieves an acceptable result. We are concerned with how to define, measure, and achieve high quality and high reliability in TAR. When quality is defined using the traditional goal-post method of specifying a minimum acceptable recall threshold, the quality and reliability of a TAR method are both, by definition, equal to the probability of achieving the threshold. Assuming this definition of quality and reliability, we show how to augment any TAR method to achieve guaranteed reliability, for a quantifiable level of additional review effort. We demonstrate this result by augmenting the TAR method supplied as the baseline model implementation for the TREC 2015 Total Recall Track, measuring reliability and effort for 555 topics from eight test collections. While our empirical results corroborate our claim of guaranteed reliability, we observe that the augmentation strategy may entail disproportionate effort, especially when the number of relevant documents is low. To address this limitation, we propose stopping criteria for the model implementation that may be applied with no additional review effort, while achieving empirical reliability that compares favorably to the provably reliable method. We further argue that optimizing reliability according to the traditional goal-post method is inconsistent with certain subjective aspects of quality, and that optimizing a Taguchi quality loss function may be more apt.

Design of Highly Integrated Mechatronic Gear Selector Levers for Automotive Shift-by-Wire Systems <=SEP=> Increased requirements regarding ergonomic comfort, limited space, weight reduction, and electronic automation of functions and safety features are on the rise for future automotive gear levers. At the same time, current mechanical gear levers have restrictions to achieve this. In this paper, we present a monostable, miniaturized mechatronic gear lever to fulfill these requirements for automotive applications. This solution describes a gear lever for positioning in the center console of a car to achieve optimal ergonomics for dynamic driving, which enables both automatic and manual gear switching. In this paper, we describe the sensor and actuator concept, safety concept, recommended shift pattern, mechanical design, and the electronic integration of this shift-by-wire system in a typical automotive bus communication network. The main contribution of this paper is a successful system design and the integration of a mechatronic system in new applications for optimizing the human-machine interface inside road vehicles.

Batch Identification Game Model for Invalid Signatures in Wireless Mobile Networks <=SEP=> Secure access is one of the fundamental problems in wireless mobile networks. Digital signature is a widely used technique to protect messages’ authenticity and nodes’ identities. From the practical perspective, to ensure the quality of services in wireless mobile networks, ideally the process of signature verification should introduce minimum delay. Batch cryptography technique is a powerful tool to reduce verification time. However, most of the existing works focus on designing batch verification algorithms for wireless mobile networks without sufficiently considering the impact of invalid signatures, which can lead to verification failures and performance degradation. In this paper, we propose a Batch Identification Game Model (BIGM) in wireless mobile networks, enabling nodes to find invalid signatures with reasonable delay no matter whether the game scenario is complete information or incomplete information. Specifically, we analyze and prove the existence of Nash Equilibriums (NEs) in both scenarios, to select the dominant algorithm for identifying invalid signatures. To optimize the identification algorithm selection, we propose a self-adaptive auto-match protocol which estimates the strategies and states of attackers based on historical information. Comprehensive simulation results in terms of NE reasonability, algorithm selection accuracy, and identification delay are provided to demonstrate that BIGM can identify invalid signatures more efficiently than existing algorithms.

International Journal of Advanced Robotic Systems Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Regular Paper <=SEP=> The  autonomous  vehicle  is  a  mobile  robot integrating  multi‐sensor  navigation  and  positioning, intelligent decision making and control  technology. This paper  presents  the  control  system  architecture  of  the autonomous vehicle, called “Intelligent Pioneer”, and the path  tracking  and  stability  of  motion  to  effectively navigate  in unknown  environments  is discussed.  In  this approach,  a  two  degree‐of‐freedom  dynamic  model  is developed to formulate the path‐tracking problem in state space format. For controlling the instantaneous path error, traditional  controllers  have  difficulty  in  guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive‐PID  controller  will  be  used.  By  using  this approach the flexibility of the vehicle control system will be  increased  and  achieving  great  advantages. Throughout,  we  provide  examples  and  results  from Intelligent Pioneer and the autonomous vehicle using this approach  competed  in  the  2010  and  2011  Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won  first position  in 2010 and third position in 2011.