Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

Optimization techniques applied to passive measures for in-orbit spacecraft survivability

Spacecraft designers have always been concerned about the effects of meteoroid impacts on mission safety. The engineering solution to this problem has generally been to erect a bumper or shield placed outboard from the spacecraft wall to disrupt/deflect the incoming projectiles. Spacecraft designers have a number of tools at their disposal to aid in the design process. These include hypervelocity impact testing, analytic impact predictors, and hydrodynamic codes. Analytic impact predictors generally provide the best quick-look estimate of design tradeoffs. The most complete way to determine the characteristics of an analytic impact predictor is through optimization of the protective structures design problem formulated with the predictor of interest. Space Station Freedom protective structures design insight is provided through the coupling of design/material requirements, hypervelocity impact phenomenology, meteoroid and space debris environment sensitivities, optimization techniques and operations research strategies, and mission scenarios. Major results are presented

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin

The AFIT ENgineer, Volume 2, Issue 4

In this issue: AFMC Spark Tank Semi-finalist New AFIT Patents 2020 Graduate School Award Winners Airmen and Artificial Intelligence Nuclear Treaty Monitorin

An Approximate Dynamic Programming Approach for Comparing Firing Solutions in a Networked Air Defense Environment

The United States Army currently employs a shoot-shoot-look firing policy for air defense. As the Army moves to a networked defense-in-depth strategy, this policy will not provide optimal results for managing interceptor inventories in a conflict to minimize the damage to defended assets. The objective for air and missile defense is to identify the firing policy for interceptor allocation that minimizes expected total cost of damage to defended assets. This dynamic weapon target assignment problem is formulated first as a Markov decision process (MDP) and then approximate dynamic programming (ADP) is used to solve problem instances based on a representative scenario. Least squares policy evaluation (LSPE) and least squares temporal difference (LSTD) algorithms are employed to determine the best approximate policies possible. An experimental design is conducted to investigate problem features such as conflict duration, attacker and defender weapon sophistication, and defended asset values. The LSPE and LSTD algorithm results are compared to two benchmark policies (e.g., firing one or two interceptors at each incoming tactical ballistic missile (TBM)). Results indicate that ADP policies outperform baseline polices when conflict duration is short and attacker weapons are sophisticated. Results also indicate that firing one interceptor at each TBM (regardless of inventory status) outperforms the tested ADP policies when conflict duration is long and attacker weapons are less sophisticated

Applying Cognitive Measures In Counterfactual Prediction

Counterfactual reasoning can be used in task-switching scenarios, such as design and planning tasks, to learn from past behavior, predict future performance, and customize interventions leading to enhanced performance. Previous research has focused on external factors and personality traits; there is a lack of research exploring how the decision-making process relates to both task-switching and counterfactual predictions. The purpose of this dissertation is to describe and explain individual differences in task-switching strategy and cognitive processes using machine learning techniques and linear ballistic accumulator (LBA) models, respectively, and apply those results in counterfactual models to predict behavior. Applying machine learning techniques to real-world task-switching data identifies a pattern of individual strategies that predicts out-of-sample clustering better than random assignment and identifies the most important factors contributing to the strategies. Comparing parameter estimates from several different LBA models, on both simulated and real data, indicates that a model based on information foraging theory that assumes all tasks are evaluated simultaneously and holistically best explains task-switching behavior. The resulting parameter values provide evidence that people have a switch-avoidance tendency, as reported in previous research, but also show how this tendency varies by participant. Including parameters that describe individual strategies and cognitive mechanisms in counterfactual prediction models provides little benefit over a baseline intercept-only model to predict a holdout dataset about real-world task switching behavior and performance, which may be due to the complexity and noise in the data. The methods developed in this research provide new opportunities to model and understand cognitive processes for decision-making strategies based on information foraging theory, which has not been considered previously. The results from this research can be applied to future task-switching scenarios as well as other decision-making tasks, both in a laboratory setting as well as the real-world, and have implications for understanding how these decisions are made

On the Design of Solar Gravity Driven Planetocentric Transfers Using Artificial Neural Networks

The sun's gravity can be used to efficiently transfer between different planetocentric orbits. Such transfers cannot be designed in a two-body dynamical system, nor do analytical methods exist to identify such transfers. This dissertation presents a method to efficiently identify transfers between a specified departure and target orbit. This method is applied to a well known problem: transfers from inclined low-earth orbits to the geostationary orbit. Motivated by the large observed control authority of the sun for geocentric transfers, a new mission architecture is defined. This architecture allows the injection of multiple spacecraft around Mars in different target orbits, enabled by solar gravity driven orbital transfers. The efficient design of applications for a wide variety of departure and target orbits, requires an understanding of a large area of the phase space. This dissertation showcases how an artificial neural network architecture can accurately predict the solar gravity driven transfers, for a significantly large section of the phase space. The developed architecture is then used to efficiently identify transfers for several different applications. Multiple revolution transfers with maneuvers at intermediate periareions are identified that arrive at Phobos or Deimos. Furthermore, transfers are designed that transfer to both Phobos and Deimos in a single trajectory. In addition to addressing solar perturbed planetocentric transfers, this dissertation shows how the developed artificial neural network framework can be applied to a different problem, with different dynamics. As an example, the dissertation develops an artificial neural network architecture that can predict heteroclinic connections in the Earth-Moon circular restricted three-body problem