Kofi Nyarko

Network intrusion visualization with NIVA, an intrusion detection visual analyzer with haptic integration

The explosive growth of malicious activities on worldwide communication networks, such as the Internet, has highlighted the need for efficient intrusion detection systems. The efficiency of traditional intrusion detection systems is limited by their inability to effectively relay relevant information due to their lack of interactive/immersive technologies. We explore several network visualization techniques geared towards intrusion detection on small and large-scale networks. We also examine the use of haptics in network intrusion visualization. By incorporating concepts from electromagnetics, fluid dynamics, and gravitational theory, we show that haptic technologies can provide another dimension of information critical to the efficient visualization of network intrusion data. Furthermore, we explore the applicability of these visualization techniques in conjunction with commercial network intrusion detectors. Finally, we present a network intrusion visualization application with haptic integration, NIVA which allows the analyst to interactively investigate as well as efficiently detect structured attacks across time and space using advanced interactive three-dimensional displays.

Cloud based passive building occupancy characterization for attack and disaster response

In the moments following natural disasters and terrorist attacks, rescue personnel are often deployed to evacuate all occupants from commercial buildings with hundreds, if not, thousands of tenants. Without a comprehensive estimate of the building occupancy, the rescue personnel are subjected to great risk with little assurance that a given search area is populated. As cloud based computing becomes more ubiquitous, commercial building occupants are utilizing cloud based solutions for managing work schedules. Consequently, the opportunity exists to exploit these whole building occupant schedules to infer occupancy levels throughout the building. In addition, when combined with data from directional passive infrared sensors, and a knowledge of the number of mobile users on Wi-Fi access points, the occupancy profiles can be adjusted based on actively sensed occupancy. This paper discusses an Inferred Occupancy Characterization (IOC) Architecture, which aims to effectively determine occupancy levels within various zones of a multi-zone structure.

Network intrusion visualization with NIVA, an intrusion detection visual and haptic analyzer

The rapid growth of malicious activities on worldwide communication networks, such as the Internet, has highlighted the need for efficient intrusion detection systems. The efficiency of traditional intrusion detection systems is limited, in part, by their inability to relay effectively relevant information due to their lack of interactive/immersive technologies. In this paper, we explore several network visualization techniques geared toward intrusion detection on small- and large-scale networks. We also examine the use of haptics in network intrusion visualization. By incorporating concepts from electromagnetics, fluid dynamics, and gravitational theory, we show that haptic technologies can provide another dimension of information critical to the efficient visualization of network intrusion data. Furthermore, we explore the applicability of these visualization techniques in conjunction with commercial network intrusion detectors. Finally, we present a network intrusion visualization application with haptic integration, NIVA, which allows the analyst to interactively investigate as well as efficiently detect structured attacks across time and space using advanced interactive three-dimensional displays.

Integrity Monitoring of Digital Elevation Models using Corner Edge Detection of Accumulated X-Band Radials

Synthetic vision systems (SVS) provides pilots with displays of stored geo-spatial data representing terrain, obstacles and cultural features. This system has the potential to improve flight safety by providing situational awareness and reducing the likelihood of controlled flight into terrain (CFIT). In order to enable the safe use of SVS at low altitudes, real-time range-to-terrain measurements may be necessary to ensure integrity of terrain data for civil aviation applications. This paper describes an integrity monitor which uses a novel approach to check the consistency between a terrain elevation profile synthesized from an X-band weather radar (WxR) sensor and the profile given in a digital elevation model (DEM). Features, in the form of edge locations and associated curvature strengths, are extracted and placed in a graph representation. A comparison is performed on the relationships that are drawn from the node attributes using the approximate graph matching technique in order to confirm the integrity of the terrain dataset. Terrain scans from NASAs integrated intelligent flight deck (IIFD) program are used to validate the proposed integrity monitoring approach

Building occupant and asset localization and tracking using visible light communication

This research demonstrates how inexpensive commercial off-the-shelf lighting components and microcontrollers can be used to construct a solution for occupant and asset localization and tracking through visible light communication (VLC). Through the modulation of the emitted light from networked LED luminaires, the location of a receiver can be determined. This paper describes the implementation of the VLC enabled LED luminaires, in addition to the infrared synchronization protocol, which enabled inexpensive white LEDs to be time division multiplexed to avoid packet collisions. Luminaires use token message passing to regulate packet transmission. Physical construction of these luminaires is discussed in addition to the simulated performance of this system.

A robust close-range photogrammetric target extraction algorithm for size and type variant targets

The Photo-G program conducted by Naval Air Systems Command at the Atlantic Test Range in Patuxent River, Maryland, uses photogrammetric analysis of large amounts of real-world imagery to characterize the motion of objects in a 3-D scene. Current approaches involve several independent processes including target acquisition, target identification, 2-D tracking of image features, and 3-D kinematic state estimation. Each process has its own inherent complications and corresponding degrees of both human intervention and computational complexity. One approach being explored for automated target acquisition relies on exploiting the pixel intensity distributions of photogrammetric targets, which tend to be patterns with bimodal intensity distributions. The bimodal distribution partitioning algorithm utilizes this distribution to automatically deconstruct a video frame into regions of interest (ROI) that are merged and expanded to target boundaries, from which ROI centroids are extracted to mark target acquisition points. This process has proved to be scale, position and orientation invariant, as well as fairly insensitive to global uniform intensity disparities.

Cases on Research and Knowledge Discovery: Homeland Security Centers of Excellence

To ensure its protection from enemies both foreign and domestic, a government must invest resources and personnel toward the goal of homeland security. It is through these endeavors that citizens are able to live out their lives in peace. Cases on Research and Knowledge Discovery: Homeland Security Centers of Excellence presents a series of studies and descriptive examples on the US Department of Homeland Security and related research. Through its investigation of interesting challenges and thought-provoking ideas, this volume offers professionals, researchers, and academics in the fields of security science, engineering, technology, and mathematics an in-depth discussion of some of the issues that directly affect the safety, security, and prosperity of the nation. Market: This premier publication is essential for all academic and research library reference collections. It is a crucial tool for academicians, researchers, and practitioners. Ideal for classroom use.

A depopulation embedded system model in urban environments

The threat of terrorism is a constant reminder of the significance of developing effective strategies to safeguard the population. This is particularly important in large, densely populated areas, which can often be the primary targets of a terrorist attack. In such a situation, the implementation of a depopulation plan becomes very challenging to manage. This paper explores one possible method of addressing rapid evacuation of densely populated urban environments in response to a terrorist threat through an in-vehicle embedded system model. The objective of the model is to determine feasible technologies that enable mobile ad-hoc mesh communication to safely depopulate an urban area.

Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

The integrity monitor for synthetic vision systems provides pilots with a consistency check between stored Digital Elevation Models (DEM) and real-time sensor data. This paper discusses the implementation of the Shadow Detection and Extraction (SHADE) algorithm in reconfigurable hardware to increase the efficiency of the design. The SHADE algorithm correlates data from a weather radar and DEM to determine occluded regions of the flight path terrain. This process of correlating the weather radar and DEM data occurs in two parallel threads which are then fed into a disparity checker. The DEM thread is broken up into four main sub-functions: 1) synchronization and translation of GPS coordinates of aircraft to the weather radar, 2) mapping range bins to coordinates and computing depression angles, 3) mapping state assignments to range bins, and 4) shadow region edge detection. This correlation must be done in realtime; therefore, a hardware implementation is ideal due to the amount of data that is to be processed. The hardware of choice is the field programmable gate array because of programmability, reusability, and computational ability. Assigning states to each range bin is the most computationally intensive process and it is implemented as a finite state machine (FSM). Results of this work are focused on the implementation of the FSM.