With increased global interconnectivity, reliance on e-commerce, network services, and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer-virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current antivirus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral stains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java, and tested. The results validate the use of a distributed-agent biological-system approach toward the computer-security problems of virus elimination and ID.
Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorized WAP access and improve network security. This is done using Differential Evolution (DE) to optimize the performance of a "Learning from Signals" (LFS) classifier implemented with RF "Distinct Native Attribute"(RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is superior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters. Track: Real world applications.
Wireless communication networks remain underattack with ill-intentioned “hackers” routinely gaining unauthorized access through Wireless Access Points(WAPs)–one of the most vulnerable points in an informationtechnology system. The goal here is to demonstrate thefeasibility of using Radio Frequency (RF) air monitoring to augment conventional bit-level security at WAPs. The specific networks of interest are those based on Orthogonal Frequency Division Multiplexing (OFDM), to include 802.11a/g WiFi and 4G 802.16 WiMAX. Proof-of-concept results are presented to demonstrate the effectiveness of a “Learningfrom Signals” (LFS) classifier with Gaussian kernel bandwidth parameters optimally determined through DifferentialEvolution (DE). The resultant DE-optimized LFS classifier is implemented within an RF “Distinct Native Attribute” (RFDNA) fingerprinting process using both Time Domain (TD) and Spectral Domain (SD) input features. The RF-DNA isused for intra-manufacturer (like-model devices from a given manufacturer) discrimination of IEEE compliant 802.11a WiFi devices and 802.16e WiMAX devices. A comparative performance assessment is provided using results from the proposed DE-optimized LFS classifier and a Bayesian-based Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classifier as used in previous demonstrations. The assessment is performed using identical TD and SD fingerprint features for both classifiers. Finally, the impact of Gaussian, triangular, and uniform kernel functions on classifier performance is demonstrated. Preliminary resultsof the DE-optimized classifier are very promising, with correct classification improvement of 15% to 40% realized over the range of signal to noise ratios considered
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