Experiments on Adaptive Techniques for Host-Based Intrusion Detection

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerable preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment

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

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

Coordinated Machine Learning and Decision Support for Situation Awareness

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator\u27s input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario

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

ManTiCore: Encryption with Joint Cipher-State Authentication

We describe a new method for authenticated encryption, which uses information from the internal state of the cipher to provide the authentication. This methodology has a number of benefits. The encryption has properties similar to CBC mode, yet the encipherment and authentication mechanisms can be parallelized and/or pipelined. The authentication overhead is minimal, so the computational cost of the authenticated encryption is very nearly that of the encryption process. Also, the authentication process remains resistant against some IV reuse. We present a class of encryption algorithms that are based on cryptographic hash functions. Because of the hash function construction, the MTC4 class of methods supports variable encryption block sizes up to twice the hash output block length and trivially supports variable key lengths. We also provide a more general construction for using the internal state of any round-based block cipher as an authenticator. We give a concrete example of the general construction that uses AES as the encryption primitive. We provide performance measurements for all of our constructions

Adaptive Critic Designs for Host-Based Intrusion Detection

We explore adaptive critic designs for host-based intrusion detection because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Results on Solaris Basic Security Module audit data demonstrate an ability to learn to distinguish between clean and exploit data