KEYWORDS: Sensors, Data modeling, Data fusion, Information fusion, Artificial intelligence, Systems modeling, Machine learning, Data processing, Radar, Statistical analysis
The data fusion information group (DFIG) model is widely popular, extending and replacing the joint director of the labs (JDL) model as a data fusion processing framework that considers data/information exchange, user/team involvement, and mission/task design. The DFIG/JDL provides an initial design from which enhancements in analytics, learning, and teaming result in opportunities to improve data fusion methodologies. This paper highlights recent artificial intelligence/machine learning (AI/ML), deep learning, reinforcement learning, and active learning capabilities with that of the DFIG model for analysis and systems engineering designs. The general DFIG construct is applicable to many AI/ML systems; however, the focus of the paper provides useful considerations for the data fusion community to consider based on prior implemented approaches. The main ideas are: level 0 DFIG data preprocessing through AI/ML methods for data reduction, level 1/2/3 DFIG object/situation/impact assessment using AI/ML/DL methods for awareness, level 4 DFIG process refinement with reinforcement learning for control, and level 5/6 DFIG user/mission refinement with active learning for human-machine teaming.
During the 2016 SPIE DSS conference, nine panelists were invited to highlight the trends and opportunities in
cyber-physical systems (CPS) and Internet of Things (IoT) with information fusion. The world will be ubiquitously
outfitted with many sensors to support our daily living thorough the Internet of Things (IoT), manage infrastructure
developments with cyber-physical systems (CPS), as well as provide communication through networked information
fusion technology over the internet (NIFTI). This paper summarizes the panel discussions on opportunities of
information fusion to the growing trends in CPS and IoT. The summary includes the concepts and areas where
information supports these CPS/IoT which includes situation awareness, transportation, and smart grids.
KEYWORDS: Computer security, Information security, Network security, Defense and security, Web services, Data fusion, Defense systems, Sensors, Data modeling, Systems modeling
With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.
Driver distraction could result in safety compromises attributable to distractions from in-vehicle equipment usage [1]. The effective design of driver-vehicle interfaces (DVIs) and other human-machine interfaces (HMIs) together with their usability, and accessibility while driving become important [2]. Driving distractions can be classified as: visual distractions (any activity that takes your eyes away from the road), cognitive distraction (any activity that takes your mind away from the course of driving), and manual distractions (any activity that takes your hands away from the steering wheel [2]). Besides, multitasking during driving is a distractive activity that can increase the risks of vehicular accidents. To study the driver’s behaviors on the safety of transportation system, using an in-vehicle driver notification application, we examined the effects of increasing driver distraction levels on the evaluation metrics of traffic efficiency and safety by using two types of driver models: young drivers (ages 16-25 years) and middle-age drivers (ages 30-45 years). Our evaluation data demonstrates that as a drivers distraction level is increased, less heed is given to change route directives from the in-vehicle on-board unit (OBU) using textual, visual, audio, and haptic notifications. Interestingly, middle-age drivers proved more effective/resilient in mitigating the negative effects of driver distraction over young drivers [2].
KEYWORDS: Sensors, Defense systems, Sensor networks, Network security, Databases, Information security, Discrete wavelet transforms, Active sensors, Digital signal processing, Defense and security
In this paper, an implemented defense system is demonstrated to carry out cyber security situation awareness. The developed system consists of distributed passive and active network sensors designed to effectively capture suspicious information associated with cyber threats, effective detection schemes to accurately distinguish attacks, and network actors to rapidly mitigate attacks. Based on the collected data from network sensors, image-based and signals-based detection schemes are implemented to detect attacks. To further mitigate attacks, deployed dynamic firewalls on hosts dynamically update detection information reported from the detection schemes and block attacks. The experimental results show the effectiveness of the proposed system. A future plan to design an effective defense system is also discussed based on system theory.
KEYWORDS: Sensors, Defense and security, Sensor networks, Defense systems, Data centers, Signal processing, Analytical research, Network security, Signal detection, Detection and tracking algorithms
Network sensor-based defense (NSD) systems have been widely used to defend against cyber threats. Nonetheless, if the adversary finds ways to identify the location of monitor sensors, the effectiveness of NSD systems can be reduced. In this paper, we propose both temporal and spatial perturbation based defense mechanisms to secure NSD systems and make the monitor sensor invisible to the adversary. The temporal-perturbation based defense manipulates the timing information of published data so that the probability of successfully recognizing monitor sensors can be reduced. The spatial-perturbation based defense dynamically redeploys monitor sensors in the network so that the adversary cannot obtain the complete information to recognize all of the monitor sensors. We carried out experiments using real-world traffic traces to evaluate the effectiveness of our proposed defense mechanisms. Our data shows that our proposed defense mechanisms can reduce the attack accuracy of recognizing detection sensors.
An intelligent transportation system (ITS) is one typical cyber-physical system (CPS) that aims to provide efficient,
effective, reliable, and safe driving experiences with minimal congestion and effective traffic flow management. In order
to achieve these goals, various ITS technologies need to work synergistically. Nonetheless, ITS’s reliance on wireless
connectivity makes it vulnerable to cyber threats. Thus, it is critical to understand the impact of cyber threats on ITS. In
this paper, using real-world transportation dataset, we evaluated the consequences of cyber threats – attacks against service
availability by jamming the communication channel of ITS. In this way, we can have a better understanding of the
importance of ensuring adequate security respecting safety and life-critical ITS applications before full and expensive real-world
deployments. Our experimental data shows that cyber threats against service availability could adversely affect
traffic efficiency and safety performances evidenced by exacerbated travel time, fuel consumed, and other evaluated
performance metrics as the communication network is compromised. Finally, we discuss a framework to make ITS secure
and more resilient against cyber threats.
Accurate and timely knowledge is critical in intelligent transportation system (ITS) as it leads to improved traffic flow
management. The knowledge of the past can be useful for the future as traffic patterns normally follow a predictable pattern
with respect to time of day, and day of week. In this paper, we systematically evaluated the prediction accuracy and speed
of several supervised machine learning algorithms towards congestion identification based on six weeks real-world traffic
data from August 1st, 2012 to September 12th, 2012 in the Maryland (MD)/Washington DC, and Virginia (VA) area. Our
dataset consists of six months traffic data pattern from July 1, 2012 to December 31, 2012, of which 6 weeks was used as
a representative sample for the purposes of this study on our reference roadway – I-270. Our experimental data shows
that with respect to classification, classification tree (Ctree) could provide the best prediction accuracy with an accuracy
rate of 100% and prediction speed of 0.34 seconds. It is pertinent to note that variations exist respecting prediction accuracy
and prediction speed; hence, a tradeoff is often necessary respecting the priority of the applications in question. It is also
imperative to note from the outset that, algorithm design and calibration are important factors in determining their
effectiveness.
KEYWORDS: Device simulation, Network security, Defense and security, Data communications, Simulink, Computer simulations, MATLAB, Telecommunications, Communication and information technologies, Renewable energy
Demand response is one of key smart grid applications that aims to reduce power generation at peak hours and maintain
a balance between supply and demand. With the support of communication networks, energy consumers can become
active actors in the energy management process by adjusting or rescheduling their electricity usage during peak hours
based on utilities pricing incentives. Nonetheless, the integration of communication networks expose the smart grid to
cyber-attacks. In this paper, we developed a smart grid simulation test-bed and designed evaluation scenarios. By
leveraging the capabilities of Matlab and ns-3 simulation tools, we conducted a simulation study to evaluate the impact
of cyber-attacks on demand response application. Our data shows that cyber-attacks could seriously disrupt smart grid
operations, thus confirming the need of secure and resilient communication networks for supporting smart grid
operations.
The smart grid is the integration of computing and communication technologies into a power grid with a goal of enabling real time control, and a reliable, secure, and efficient energy system [1]. With the increased interest of the research community and stakeholders towards the smart grid, a number of solutions and algorithms have been developed and proposed to address issues related to smart grid operations and functions. Those technologies and solutions need to be tested and validated before implementation using software simulators. In this paper, we developed a general smart grid simulation model in the MATLAB/Simulink environment, which integrates renewable energy resources, energy storage technology, load monitoring and control capability. To demonstrate and validate the effectiveness of our simulation model, we created simulation scenarios and performed simulations using a real-world data set provided by the Pecan Street Research Institute.
Most enterprise networks are built to operate in a static configuration (e.g., static software stacks, network configurations, and application deployments). Nonetheless, static systems make it easy for a cyber adversary to plan and launch successful attacks. To address static vulnerability, moving target defense (MTD) has been proposed to increase the difficulty for the adversary to launch successful attacks. In this paper, we first present a literature review of existing MTD techniques. We then propose a generic defense framework, which can provision an incentive-compatible MTD mechanism through dynamically migrating server locations. We also present a user-server mapping mechanism, which not only improves system resiliency, but also ensures network performance. We demonstrate a MTD with a multi-user network communication and our data shows that the proposed framework can effectively improve the resiliency and agility of the system while achieving good network timeliness and throughput performance.
To date, Unmanned Aerial Vehicles (UAVs) have been widely used for numerous applications. UAVs can directly connect to ground stations or satellites to transfer data. Multiple UAVs can communicate and cooperate with each other and then construct an ad-hoc network. Multi-UAV systems have the potential to provide reliable and timely services for end users in addition to satellite networks. In this paper, we conduct a simulation study for evaluating the network performance of multi-UAV systems and satellite networks using the ns-2 networking simulation tool. Our simulation results show that UAV communication networks can achieve better network performance than satellite networks and with a lower cost and increased timeliness. We also investigate security resiliency of UAV networks. As a case study, we simulate false data injection attacks against UAV communication networks in ns-2 and demonstrate the impact of false data injection attacks on network performance.
Intelligent transportation system (ITS) applications are expected to provide a more efficient, effective, reliable, and
safe driving experience, which can minimize road traffic congestion resulting in a better traffic flow management. To
efficiently manage traffic flows, in this paper, we compare the effectiveness of two well-known vehicle routing
algorithms: the Dijkstra's shortest path algorithm and the A* (Astar) algorithm in terms of the total travel time and the
travel distance. To this end, we built a generic ITS test-bed and created several real-world driving scenarios using field
and simulation data to evaluate the performance of these two routing algorithms. The dataset used in our simulation is six
weeks traffic volume data from 08/01/2012 to 09/27/2012 in the Maryland (MD)/Washington DC and Virginia (VA)
area. Our simulation data shows that an increase in network size results in scalability problems as the efficiency and
effectiveness of these algorithms diminishes in larger road networks with greater traffic volume densities, flow rates, and
congested conditions. In addition, the imprecision of the road network increases as the network size and the traffic
volume density increases. Our study shows that the ability of these vehicular routing algorithms to adaptively route
traffic depends on the size and type of road networks, and the current roadway conditions.
KEYWORDS: Clouds, Computer security, Operating systems, Signal detection, Signal generators, Data storage, Network security, Data centers, Error analysis, Computing systems
One of the key characteristics of cloud computing is the device and location independence that enables the user to access
systems regardless of their location. Because cloud computing is heavily based on sharing resource, it is vulnerable to cyber
attacks. In this paper, we investigate a localization attack that enables the adversary to leverage central processing unit
(CPU) resources to localize the physical location of server used by victims. By increasing and reducing CPU usage through
the malicious virtual machine (VM), the response time from the victim VM will increase and decrease correspondingly. In
this way, by embedding the probing signal into the CPU usage and correlating the same pattern in the response time from
the victim VM, the adversary can find the location of victim VM. To determine attack accuracy, we investigate features in
both the time and frequency domains. We conduct both theoretical and experimental study to demonstrate the effectiveness
of such an attack.
The growing in use of smart mobile devices for everyday applications has stimulated the spread of mobile malware,
especially on popular mobile platforms. As a consequence, malware detection becomes ever more critical in sustaining the
mobile market and providing a better user experience. In this paper, we review the existing malware and detection schemes.
Using real-world malware samples with known signatures, we evaluate four popular commercial anti-virus tools and our
data shows that these tools can achieve high detection accuracy. To deal with the new malware with unknown signatures,
we study the anomaly based detection using decision tree algorithm. We evaluate the effectiveness of our detection scheme
using malware and legitimate software samples. Our data shows that the detection scheme using decision tree can achieve
a detection rate up to 90% and a false positive rate as low as 10%.
Networking technologies are exponentially increasing to meet worldwide communication requirements. The rapid
growth of network technologies and perversity of communications pose serious security issues. In this paper, we aim to
developing an integrated network defense system with situation awareness capabilities to present the useful information
for human analysts. In particular, we implement a prototypical system that includes both the distributed passive and active
network sensors and traffic visualization features, such as 1D, 2D and 3D based network traffic displays. To effectively
detect attacks, we also implement algorithms to transform real-world data of IP addresses into images and study the pattern
of attacks and use both the discrete wavelet transform (DWT) based scheme and the statistical based scheme to detect
attacks. Through an extensive simulation study, our data validate the effectiveness of our implemented defense system.
KEYWORDS: Satellites, Satellite communications, Data transmission, Data communications, Bismuth, Silicon, Space operations, Analytical research, Video, Algorithm development
For worldwide, a satellite communication network is an integral component of the global networking infrastructure. In
this paper, we focus on developing effective routing techniques that consider both user preferences and network dynamic
conditions. In particular, we develop a weighted-based route selection scheme for the core satellite communication network.
Unlike the shortest path routing scheme, our scheme chooses the route from multiple matched entries based on the
assigned weights that reflect the dynamic condition of networks. We also discuss how to derive the optimal weights for
route assignment. To further meet user’s preference, we implement the multiple path routing scheme to achieve the high
rate of data transmission and the preemption based routing scheme to guarantee the data transmission for high priority
users. Through extensive simulation studies, our data validates the effectiveness of our proposed routing schemes.
Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities
with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring,
detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling
network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness
on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework,
we also implement the statistical-based detection and sequential testing-based detection techniques and compare their
respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed
framework for combating cyber attacks.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.