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. In this paper, 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.
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.
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.
Anomaly detection is the process of finding data points that deviate from a baseline. In a real-life setting, anomalies are usually unknown or extremely rare. Moreover, the detection must be accomplished in a timely manner or the risk of corrupting the system might grow exponentially. In this work, we propose a two level framework for detecting anomalies in sequences of discrete elements. First, we assess whether we can obtain enough information from the statistics collected from the discriminator's layers to discriminate between out of distribution and in distribution samples. We then build an unsupervised anomaly detection module based on these statistics. As to augment the data and keep track of classes of known data, we lean toward a semi-supervised adversarial learning applied to discrete elements.
Engineering Education (ASEE) Mid-Atlantic Region Distinguished Teacher Award. He teaches courses in both analog and digital electronic circuit design and instrumentation, with a focus on wireless communication. He has more than 15 years experience in the development and delivery of synchronous and asynchronous web-based course supplements for electrical engineering courses. Dr. Astatke played a leading role in the development and implementation of the first completely online undergraduate ECE program in the State of Maryland. He has published over 40 papers and presented his research work at regional, national and international conferences. He also runs several exciting summer camps geared towards middle school, high school, and community college students to expose and increase their interest in pursuing Science Technology Engineering and Mathematics (STEM) fields. Dr. Astatke travels to Ethiopia every summer to provide training and guest lectures related to the use of the mobile laboratory technology and pedagogy to enhance the ECE curriculum at five different universities.
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