Comprehensive test ban treaty international monitoring system security threats and proposed security attributes

To monitor compliance with a Comprehensive Test Ban Treaty (CTBT), a sensing network, referred to as the International Monitoring System (IMS), is being deployed. Success of the IMS depends on both its ability to preform its function and the international community`s confidence in the system. To ensure these goals, steps must be taken to secure the system against attacks that would undermine it; however, it is not clear that consensus exists with respect to the security requirements that should be levied on the IMS design. In addition, CTBT has not clearly articulated what threats it wishes to address. This paper proposes four system-level threats that should drive IMS design considerations, identifies potential threat agents, and collects into one place the security requirements that have been suggested by various elements of the IMS community. For each such requirement, issues associated with the requirement are identified and rationale for the requirement is discussed

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Approximate Public Key Authentication with Information Hiding

This paper describes a solution for the problem of authenticating the shapes of statistically variant gamma spectra while simultaneously concealing the shapes and magnitudes of the sensitive spectra. The shape of a spectrum is given by the relative magnitudes and positions of the individual spectral elements. Class-specific linear orthonormal transformations of the measured spectra are used to produce output that meet both the authentication and concealment requirements. For purposes of concealment, the n-dimensional gamma spectra are transformed into n-dimensional output spectra that are effectively indistinguishable from Gaussian white noise (independent of the class). In addition, the proposed transformations are such that statistical authentication metrics computed on the transformed spectra are identical to those computed on the original spectra

A Low-Power VHDL Design for an Elliptic Curve Digital Signature Chip

The authors present a VHDL design that incorporates optimizations intended to provide digital signature generation with as little power, space, and time as possible. These three primary objectives of power, size, and speed must be balanced along with other important goals, including flexibility of the hardware and ease of use. The highest-level function doffered by their hardware design is Elliptic Curve Optimal El Gamal digital signature generation. The parameters are defined over the finite field GF(2{sup 178}), which gives security that is roughly equivalent to that provided by 1500-bit RSA signatures. The optimizations include using the point-halving algorithm for elliptic curves, field towers to speed up the finite field arithmetic in general, and further enhancements of basic finite field arithmetic operations. The result is a synthesized VHDL digital signature design (using a CMOS 0.5{micro}m, 5V, 25 C library) of 191,000 gates that generates a signature in 4.4 ms at 20 MHz

Low-Power Public Key Cryptography

This report presents research on public key, digital signature algorithms for cryptographic authentication in low-powered, low-computation environments. We assessed algorithms for suitability based on their signature size, and computation and storage requirements. We evaluated a variety of general purpose and special purpose computing platforms to address issues such as memory, voltage requirements, and special functionality for low-powered applications. In addition, we examined custom design platforms. We found that a custom design offers the most flexibility and can be optimized for specific algorithms. Furthermore, the entire platform can exist on a single Application Specific Integrated Circuit (ASIC) or can be integrated with commercially available components to produce the desired computing platform

Distributed Denial-of-Service Characterization

Distributed denial of service (DoS) attacks on cyber-resources are complex problems that are difficult to completely define, characterize, and mitigate. We recognize the process-nature of DoS attacks and view them from multiple perspectives. Identification of opportunities for mitigation and further research may result from this attempt to characterize the DoS problem space. We examine DoS attacks from the point of view of (1) a high-level that establishes common terminology and a framework for discussing the DoS process, (2) layers of the communication stack, from attack origination to the victim of the attack, (3) specific network and computer elements, and (4) attack manifestations. We also examine DoS issues associated with wireless communications. Using this collection of views, one begins to see the DoS problem in a holistic way that may lead to improved understanding, new mitigation strategies, and fruitful research