IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction

An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS

Semi-supervised multi-layered clustering model for intrusion detection

A Machine Learning (ML) -based Intrusion Detection and Prevention System (IDPS) requires a large amount of labeled up-to-date training data, to effectively detect intrusions and generalize well to novel attacks. However, labeling of data is costly and becomes infeasible when dealing with big data, such as those generated by IoT (Internet of Things) -based applications. To this effect, building a ML model that learns from non- or partially-labeled data is of critical importance. This paper proposes a novel Semi-supervised Multi-Layered Clustering Model (SMLC) for network intrusion detection and prevention tasks. The SMLC has the capability to learn from partially labeled data while achieving a comparable detection performance to supervised ML-based IDPS. The performance of the SMLC is compared with well-known supervised ensemble ML models, namely, RandomForest, Bagging, and AdaboostM1 and a semi-supervised model (i.e., tri-training) on a benchmark network intrusion dataset, the Kyoto 2006+. Experimental results show that the SMLC outperforms all other models and can achieve better detection accuracy using only 20% labeled instances of the training data

Learning a deep-feature clustering model for gait-based individual identification

Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals’ identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling

FE65 as a link between VLDLR and APP to regulate their trafficking and processing

<p>Abstract</p> <p>Background</p> <p>Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2. In the present study, we tested whether FE65 can interact with another ApoE receptor, VLDLR, thereby altering its trafficking and processing, and whether FE65 can serve as a linker between APP and VLDLR.</p> <p>Results</p> <p>We found that FE65 interacted with VLDLR using GST pull-down and co-immunoprecipitation assays in COS7 cells and in brain lysates. This interaction occurs via the PTB1 domain of FE65. Co-transfection with FE65 and full length VLDLR increased secreted VLDLR (sVLDLR); however, the levels of VLDLR C-terminal fragment (CTF) were undetectable as a result of proteasomal degradation. Additionally, FE65 increased cell surface levels of VLDLR. Moreover, we identified a novel complex between VLDLR and APP, which altered trafficking and processing of both proteins. Furthermore, immunoprecipitation results demonstrated that the presence of FE65 increased the interaction between APP and VLDLR <it>in vitro </it>and <it>in vivo</it>.</p> <p>Conclusions</p> <p>These data suggest that FE65 can regulate VLDLR trafficking and processing. Additionally, the interaction between VLDLR and APP altered both protein's trafficking and processing. Finally, our data suggest that FE65 serves as a link between VLDLR and APP. This novel interaction adds to a growing body of literature indicating trimeric complexes with various ApoE Receptors and APP.</p

DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection

Recent studies have proposed that traditional security technology – involving pattern-matching algorithms that check predefined pattern sets of intrusion signatures – should be replaced with sophisticated adaptive approaches that combine machine learning and behavioural analytics. However, machine learning is performance driven, and the high computational cost is incompatible with the limited computing power, memory capacity and energy resources of portable IoT-enabled devices. The convoluted nature of deep-structured machine learning means that such models also lack transparency and interpretability. The knowledge obtained by interpretable learners is critical in security software design. We therefore propose two novel models featuring a common Deep Extraction and Mutual Information Selection (DEMISe) element which extracts features using a deep-structured stacked autoencoder, prior to feature selection based on the amount of mutual information (MI) shared between each feature and the class label. An entropy-based tree wrapper is used to optimise the feature subsets identified by the DEMISe element, yielding the DEMISe with Tree Evaluation and Regression Detection (DETEReD) model. This affords ‘white box’ insight, and achieves a time to build of 603 seconds, a 99.07% detection rate, and 98.04% model accuracy. When tested against AWID, the best-referenced intrusion detection dataset, the new models achieved a test error comparable to or better than state-of-the-art machine-learning models, with a lower computational cost and higher levels of transparency and interpretability

Disruption of cholinergic neurotransmission, within a cognitive challenge paradigm, is indicative of Aβ-related cognitive impairment in preclinical Alzheimer’s disease after a 27-month delay interval

Background Abnormal beta-amyloid (Aβ) is associated with deleterious changes in central cholinergic tone in the very early stages of Alzheimer’s disease (AD), which may be unmasked by a cholinergic antagonist (J Prev Alzheimers Dis 1:1–4, 2017). Previously, we established the scopolamine challenge test (SCT) as a “cognitive stress test” screening measure to identify individuals at risk for AD (Alzheimer’s & Dementia 10(2):262–7, 2014) (Neurobiol. Aging 36(10):2709-15, 2015). Here we aim to demonstrate the potential of the SCT as an indicator of cognitive change and neocortical amyloid aggregation after a 27-month follow-up interval. Methods Older adults (N = 63, aged 55–75 years) with self-reported memory difficulties and first-degree family history of AD completed the SCT and PET amyloid imaging at baseline and were then seen for cognitive testing at 9, 18, and 27 months post-baseline. Repeat PET amyloid imaging was completed at the time of the 27-month exam. Results Significant differences in both cognitive performance and in Aβ neocortical burden were observed between participants who either failed vs. passed the SCT at baseline, after a 27-month follow-up period. Conclusions Cognitive response to the SCT (Alzheimer’s & Dementia 10(2):262–7, 2014) at baseline is related to cognitive change and PET amyloid imaging results, over the course of 27 months, in preclinical AD. The SCT may be a clinically useful screening tool to identify individuals who are more likely to both have positive evidence of amyloidosis on PET imaging and to show measurable cognitive decline over several years

Development of a PbWO4 Detector for Single-Shot Positron Annihilation Lifetime Spectroscopy at the GBAR Experiment

We have developed a PbWO4 (PWO) detector with a large dynamic range to measure the intensity of a positron beam and the absolute density of the ortho-positronium (o-Ps) cloud it creates. A simulation study shows that a setup based on such detectors may be used to determine the angular distribution of the emission and reflection of o-Ps to reduce part of the uncertainties of the measurement. These will allow to improve the precision in the measurement of the cross-section for the (anti)hydrogen formation by (anti)proton-positronium charge exchange and to optimize the yield of antihydrogen ion which is an essential parameter in the GBAR experiment

Measurement of Production Properties of Positively Charged Kaons in Proton-Carbon Interactions at 31 GeV/c

Spectra of positively charged kaons in p+C interactions at 31 GeV/c were measured with the NA61/SHINE spectrometer at the CERN SPS. The analysis is based on the full set of data collected in 2007 with a graphite target with a thickness of 4% of a nuclear interaction length. Interaction cross sections and charged pion spectra were already measured using the same set of data. These new measurements in combination with the published ones are required to improve predictions of the neutrino flux for the T2K long baseline neutrino oscillation experiment in Japan. In particular, the knowledge of kaon production is crucial for precisely predicting the intrinsic electron neutrino component and the high energy tail of the T2K beam. The results are presented as a function of laboratory momentum in 2 intervals of the laboratory polar angle covering the range from 20 up to 240 mrad. The kaon spectra are compared with predictions of several hadron production models. Using the published pion results and the new kaon data, the K+/\pi+ ratios are computed.Comment: 10 pages, 11 figure

Multiplicity dependence of jet-like two-particle correlations in p-Pb collisions at sNN\sqrt{s_{NN}} = 5.02 TeV

Two-particle angular correlations between unidentified charged trigger and associated particles are measured by the ALICE detector in p-Pb collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV. The transverse-momentum range 0.7 $< p_{\rm{T}, assoc} < p_{\rm{T}, trig} <$ 5.0 GeV/$c$ is examined, to include correlations induced by jets originating from low momen\-tum-transfer scatterings (minijets). The correlations expressed as associated yield per trigger particle are obtained in the pseudorapidity range $|\eta|<0.9$. The near-side long-range pseudorapidity correlations observed in high-multiplicity p-Pb collisions are subtracted from both near-side short-range and away-side correlations in order to remove the non-jet-like components. The yields in the jet-like peaks are found to be invariant with event multiplicity with the exception of events with low multiplicity. This invariance is consistent with the particles being produced via the incoherent fragmentation of multiple parton--parton scatterings, while the yield related to the previously observed ridge structures is not jet-related. The number of uncorrelated sources of particle production is found to increase linearly with multiplicity, suggesting no saturation of the number of multi-parton interactions even in the highest multiplicity p-Pb collisions. Further, the number scales in the intermediate multiplicity region with the number of binary nucleon-nucleon collisions estimated with a Glauber Monte-Carlo simulation.Comment: 23 pages, 6 captioned figures, 1 table, authors from page 17, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/161