Digital Signal Processing Leveraged for Intrusion Detection

This thesis describes the development and evaluation of a novel system called the Network Attack Characterization Tool (NACT). The NACT employs digital signal processing to detect network intrusions, by exploiting the Lomb-Scargle periodogram method to obtain a spectrum for sampled network traffic. The Lomb-Scargle method for generating a periodogram allows for the processing of unevenly sampled network data. This method for determining a periodogram has not yet been used for intrusion detection. The spectrum is examined to determine if features exist above a significance level chosen by the user. These features are considered an attack, triggering an alarm. Two traffic statistics are used to construct the time series over which the periodogram analysis is accomplished. These two statistics are packet inter-arrival time and payload size. The traffic source for this research is the 1999 DARPA intrusion detection data set developed by MIT Lincoln Laboratories. Three specific attacks from this data set are examined; the Processtable attack, the Dictionary attack and the Teardrop attack. Of the three attacks the NACT was able to detect the Processtable attack with an accuracy of 100%. The Dictionary and Teardrop attacks were also detected with 100% and 85% accuracies respectively. This success in detecting these attacks establishes that digital signal processing methods can be a successful technique for network intrusion detection

Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks

There has been a paradigm shift in the industrial wireless sensor domain caused by the Internet of Things (IoT). IoT is a thriving technology leading the way in short range and fixed wireless sensing. One of the issues in Industrial Wireless Sensor Network-IWSN is finding the optimal solution for minimizing the defect time in superframe scheduling. This paper proposes a method using the evolutionary algorithms approach namely particle swarm optimization (PSO), Orthogonal Learning PSO, genetic algorithms (GA) and modified GA for optimizing the scheduling of superframe. We have also evaluated a contemporary method, deadline monotonic scheduling on the ISA 100.11a. By using this standard as a case study, the presented simulations are object-oriented based, with numerous variations in the number of timeslots and wireless sensor nodes. The simulation results show that the use of GA and modified GA can provide better performance for idle and missed deadlines. A comprehensive and detailed performance evaluation is given in the paper

Non-personal services to provide metering effort at NAB, Little Creek, VA

Issued as Progress reports no. 1-4, and Final report, Project no. A-289

Annual report town of Milan, New Hampshire for the year ending December 31, 2008.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire