Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

Internet of Things (IoT) in military setting generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices to wearable combat uniforms), which are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and bening application. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. for evaluation of proposed malware detection approaches)

A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

A Survey of Access Control Models in Wireless Sensor Networks

Copyright 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/)Wireless sensor networks (WSNs) have attracted considerable interest in the research community, because of their wide range of applications. However, due to the distributed nature of WSNs and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. Resource constraints in sensor nodes mean that security mechanisms with a large overhead of computation and communication are impractical to use in WSNs; security in sensor networks is, therefore, a challenge. Access control is a critical security service that offers the appropriate access privileges to legitimate users and prevents illegitimate users from unauthorized access. However, access control has not received much attention in the context of WSNs. This paper provides an overview of security threats and attacks, outlines the security requirements and presents a state-of-the-art survey on access control models, including a comparison and evaluation based on their characteristics in WSNs. Potential challenging issues for access control schemes in WSNs are also discussed.Peer reviewe

An improved method for text summarization using lexical chains

This work is directed toward the creation of a system for automatically sum-marizing documents by extracting selected sentences. Several heuristics including position, cue words, and title words are used in conjunction with lexical chain in-formation to create a salience function that is used to rank sentences for extraction. Compiler technology, including the Flex and Bison tools, is used to create the AutoExtract summarizer that extracts and combines this information from the raw text. The WordNet database is used for the creation of the lexical chains. The AutoExtract summarizer performed better than the Microsoft Word97 AutoSummarize tool and the Sinope commercial summarizer in tests against ideal extracts and in tests judged by humans

Addressing the Nation: The Use of Design Competitions in Interpreting Historic Sites

Design competitions are regularly used for the creation of monuments and structures in the United States. Pursuing this method to develop the interpretation of a historic site or monument, encompassing more than the design of the site and its structures, however, is a rarer and more recent phenomenon. This thesis evaluates the use of design competitions in the design and interpretation of historic sites that could be considered recent sites of conscience. This type of site is especially difficult to interpret, given its sometimes controversial status. The interpretation and design of a historic site significantly impacts a visitor’s perception of an event, a people, or the history of a location. It is responsible for creating what the visitor takes with them. A process this important must be carefully pursued and evaluated, especially when the content requires the designer to address the nation. The sites evaluated in this thesis (Women\u27s Rights National Historical Site, Little Bighorn Battlefield National Monument, and Flight 93 National Memorial) represent different stages of the process, ranging from a site that opened in 1980 (Women\u27s Rights) to a site currently undergoing the construction of its chosen design (Flight 93). These design competitions, in response to a call for interpretation of a historic site marred by national and regional trauma or upheaval, reveal the lessons learned from the event and stimulate the next steps to occur on the site. They additionally allow opportunities for a variety of viewpoints to be expressed and considered in a juried atmosphere

Utilization of GIS in Tracking Disinterment and Movement of Unknown US WWII War Dead: Foundations for a Geospatial Approach to Addressing Commingled Remains

In the aftermath of World War II, the US was faced with the monumental task of finding and identifying over 405,000 service members who did not survive the conflict (McDermott, 2005, p. 1). Of these 405,000, 81,000 remain missing and 2,498 remain unidentified in cemeteries across Europe alone (American Battle Monuments Commission, 2022). Often, these individuals were interred and disinterred multiple times, crossing the continent in the journey from loss incident or battlefield to their final resting place. Commingling, the accidental mixing of remains, is an ever-present concern in the forensic identification of individuals from mass casualty incidents (Belcher et al., 2021). Each instance of disinterment and movement is an opportunity for commingling to occur. DNA testing is an oft relied upon method for distinguishing between individuals in these cases but can be time consuming and expensive. Further, when tests are conducted and results do not indicate a match with a suspected individual, this can compound the difficulty in establishing the unknown individual\u27s identity. This project aims to establish a foundation for a spatially oriented approach to addressing commingling in these cases and aid in creating a shortlist of suspected individuals that may be a positive match. Through the creation of a geospatial tracking system, each unknown individual\u27s path is traced in relation to known locations of origin and interment and can be analyzed in tandem with all unknown individuals that they have crossed paths. Likelihood of identification is inversely proportional to the interval between death and forensic analysis (Steere & Boardman, 1957). This is to say that identification becomes increasingly difficult and less likely to succeed as time grows between the death of an individual and an attempt at identifying them. Ultimately, time, volume of effort, and funds are limited resources in the mission to identify the US\u27s remaining missing service members. The creation of tools that require fewer resources and can ease more intensive identification methods is a pursuit highly relevant to our efficiency in identification. Advisor: William R. Belche

IMMACCS: A Multi-Agent Decision-Support System

This report describes work performed by the Collaborative Agent Design Research Center for the US Marine Corps Warfighting Laboratory (MCWL), on the IMMACCS experimental decision-support system. IMMACCS (Integrated Marine Multi-Agent Command and Control System) incorporates three fundamental concepts that distinguish it from existing (i.e., legacy) command and control applications. First, it is a collaborative system in which computer-based agents assist human operators by monitoring, analyzing, and reasoning about events in near real-time. Second, IMMACCS includes an ontological model of the battlespace that represents the behavioral characteristics and relationships among real world entities such as friendly and enemy assets, infrastructure objects (e.g., buildings, roads, and rivers), and abstract notions. This object model provides the essential common language that binds all IMMACCS components into an integrated and adaptive decision-support system. Third, IMMACCS provides no ready made solutions that may not be applicable to the problems that will occur in the real world. Instead, the agents represent a powerful set of tools that together with the human operators can adjust themselves to the problem situations that cannot be predicted in advance. In this respect, IMMACCS is an adaptive command and control system that supports planning, execution and training functions concurrently. The report describes the nature and functional requirements of military command and control, the architectural features of IMMACCS that are designed to support these operational requirements, the capabilities of the tools (i.e., agents) that IMMACCS offers its users, and the manner in which these tools can be applied. Finally, the performance of IMMACCS during the Urban Warrior Advanced Warfighting Experiment held in California in March, 1999, is discussed from an operational viewpoint

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted