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)

Detecting crypto-ransomware in IoT networks based on energy consumption footprint

An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach out-performs K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure

MDA-driven development of standard-compliant OSS components: the OSS/J inventory case-study.

The telecommunications-oriented Operational Support Systems (OSS) industry have recognised the value of technology independent modelling of OSS solutions as a way to reduce cost, add agility, validate and verify solution designs against architectural guidelines of an enterprise and most importantly provide traceability in the design methodology process. The challenges faced by the OSS community is how MDA tools can deliver the promise of advanced meta-modelling, model definition and validation and model transformation for both OSS software components and integration logic in the larger OSS landscape. This paper describes how an advanced extensible meta-modelling tool is used to build an OSS component following best practice industry guidelines. Extended MOF, extended executable OCL and a powerful transformation language are used to capture the constraints in the meta-models as well as models followed by complete, 100% code generation from models. Furthermore, meta-models are also developed to capture graphical user interface elements in conjunction with the inventory data models, which are then automatically translated into code. This work is the precursor for defining extensive meta-models for a component-based OSS infrastructure based on industry best practice, for adding high degree of formality to model specifications and for enabling the verification of domain requirements by executing the models through model snapshot creation, way before system implementation takes place

The effects of feedback format, and egocentric & allocentric relative phase on coordination stability.

The stability of coordinated rhythmic movement is primarily affected by the required mean relative phase. In general, symmetrical coordination is more stable than asymmetrical coordination; however, there are two ways to define relative phase and the associated symmetries. The first is in an egocentric frame of reference, with symmetry defined relative to the sagittal plane down the midline of the body. The second is in an allocentric frame of reference, with symmetry defined in terms of the relative direction of motion. Experiments designed to separate these constraints have shown that both egocentric and allocentric constraints contribute to overall coordination stability, with the former typically showing larger effects. However, separating these constraints has meant comparing movements made either in different planes of motion, or by limbs in different postures. In addition, allocentric information about the coordination is either in the form of the actual limb motion, or a transformed, Lissajous feedback display. These factors limit both the comparisons that can be made and the interpretations of these comparisons. The current study examined the effects of egocentric relative phase, allocentric relative phase, and allocentric feedback format on coordination stability in a single task. We found that while all three independently contributed to stability, the egocentric constraint dominated. This supports previous work. We examine the evidence underpinning theoretical explanations for the egocentric constraint, and describe how it may reflect the haptic perception of relative phase

Internet of Things for Sustainable Community Development: Introduction and Overview

The two-third of the city-dwelling world population by 2050 poses numerous global challenges in the infrastructure and natural resource management domains (e.g., water and food scarcity, increasing global temperatures, and energy issues). The IoT with integrated sensing and communication capabilities has the strong potential for the robust, sustainable, and informed resource management in the urban and rural communities. In this chapter, the vital concepts of sustainable community development are discussed. The IoT and sustainability interactions are explained with emphasis on Sustainable Development Goals (SDGs) and communication technologies. Moreover, IoT opportunities and challenges are discussed in the context of sustainable community development

Optical and Electronic Properties of Al-Doped Mg12O12 Nanocluster: A Theoretical Study

Effects of A doping on the structural, optical, and electronic properties of Mg12O12 nanocluster have been investigated using density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. It is found that for all stable structures, the doped nanocluster with five Al atoms has a larger binding energy of �5.22 and �5.06 eV evaluated by M06-2X and B97D functional, respectively. Both M06-2X and B97D functional exhibited that the Al substituted at the Mg-site can alter the energy gap of the nanocluster in comparison with unstable O sites. With substituting four Al atoms at the Mg sites of the nanocluster, the changes in the energy gap is significantly large than other states. More details on the dopant effects, charge population and electronic structure evolution with the variation of the Al concentration of doping are discussed in the context. © 2019, Pleiades Publishing, Ltd