A distributed deep learning approach with mobile edge computing for next generation IoT networks security

Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation

Network motif comparison rationalizes Sec1/Munc18-SNARE regulation mechanism in exocytosis

BackgroundNetwork motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes. MethodsWe present a comparative network motif experimental approach, which helps to explain complex biological phenomena and increases the understanding of biological functions at the molecular level by exploring evolutionary design principles of network motifs. ResultsUsing this framework to analyze the SM (Sec1/Munc18)-SNARE (N-ethylmaleimide-sensitive factor activating protein receptor) system in exocytic membrane fusion in yeast and neurons, we find that the SM-SNARE network motifs of yeast and neurons show distinct dynamical behaviors. We identify the closed binding mode of neuronal SM (Munc18-1) and SNARE (syntaxin-1) as the key factor leading to mechanistic divergence of membrane fusion systems in yeast and neurons. We also predict that it underlies the conflicting observations in SM overexpression experiments. Furthermore, hypothesis-driven lipid mixing assays validated the prediction. ConclusionTherefore this study provides a new method to solve the discrepancies and to generalize the functional role of SM proteins

ConvXSS:a deep learning-based smart ICT framework against code injection attacks for HTML5 web applications in sustainable smart city infrastructure

In this paper we propose ConvXSS, a novel deep learning approach for the detection of XSS and code injection attacks, followed by context-based sanitization of the malicious code if the model detects any malicious code in the application. Firstly, we briefly discuss XSS and code injection attacks that might pose threat to sustainable smart cities. Along with this, we discuss various approaches proposed previously for the detection and alleviation of these attacks followed by their respective limitations. Then we propose our deep learning model adopting whose novelty is based on the approach followed for Data Pre-Processing. Then we finally propose Context-based Sanitization to replace the malicious part of the code with sanitized code. Numerical experiments conducted on various datasets have shown various results out of which the best model has an accuracy of 99.42%, a precision of 99.81% and a recall of 99.35%. When compared with other state of the art techniques in this domain, our approach shows at par or in the best case, better results in terms of detection speed and accuracy of CSS attacks

A NEWER PHOTOCHEMICAL METHOD FOR ESTIMATION OF p-PHENYLENE DIAMINE USING SODIUM NITROPRUSSIDE

ABSTRACT A newer, faster, inexpensive and convenient quantitative method for the determination of p-phenylene diamine using photochemical exchange reaction of sodium nitroprusside has been investigated. Sodium nitroprusside is a photolabile complex and it undergoes photochemical ligand exchange reactions rapidly. Some recent efforts have been made to utilise such reactions for the estimation of some nitrogen containing anions and electron rich organic molecules. The progress of the reaction is observed spectrophotometrically. The effects of different parameters like pH, change of concentration of sodium nitroprusside, concentration of ligands, light intensity etc. on percentage error was investigated. The efforts were made to minimise the percentage error and some optimum conditions were obtained. Such reaction can be used for the determination of p-phenylene diamine in the range of millimoles to micromoles, hence it is important to know whether such estimations can be done successfully and that to with the desired accuracy

Mechanistic Insights into a Novel Exporter-Importer System of Mycobacterium tuberculosis Unravel Its Role in Trafficking of Iron

Elucidation of the basic mechanistic and biochemical principles underlying siderophore mediated iron uptake in mycobacteria is crucial for targeting this principal survival strategy vis-à-vis virulence determinants of the pathogen. Although, an understanding of siderophore biosynthesis is known, the mechanism of their secretion and uptake still remains elusive.Here, we demonstrate an interplay among three iron regulated Mycobacterium tuberculosis (M.tb) proteins, namely, Rv1348 (IrtA), Rv1349 (IrtB) and Rv2895c in export and import of M.tb siderophores across the membrane and the consequent iron uptake. IrtA, interestingly, has a fused N-terminal substrate binding domain (SBD), representing an atypical subset of ABC transporters, unlike IrtB that harbors only the permease and ATPase domain. SBD selectively binds to non-ferrated siderophores whereas Rv2895c exhibits relatively higher affinity towards ferrated siderophores. An interaction between the permease domain of IrtB and Rv2895c is evident from GST pull-down assay. In vitro liposome reconstitution experiments further demonstrate that IrtA is indeed a siderophore exporter and the two-component IrtB-Rv2895c system is an importer of ferrated siderophores. Knockout of msmeg_6554, the irtA homologue in Mycobacterium smegmatis, resulted in an impaired M.tb siderophore export that is restored upon complementation with M.tb irtA.Our data suggest the interplay of three proteins, namely IrtA, IrtB and Rv2895c in synergizing the balance of siderophores and thus iron inside the mycobacterial cell

The role of REM theta activity in emotional memory

While NREM sleep has been strongly implicated in the reactivation and consolidation of memory traces, the role of REM sleep remains unclear. A growing body of research on humans and animals provide behavioral evidence for a role of REM sleep in the strengthening and modulation of emotional memories. Theta activity – which describes low frequency oscillations in the local field potential within the hippocampus, amygdala and neocortex – is a prominent feature of both wake and REM sleep in humans and rodents. Theta coherence between the hippocampus and amygdala drives large-scale PGO waves, the density of which predicts increases in plasticity-related gene expression. This could potentially facilitate the processing of emotional memory traces within the hippocampus during REM sleep. Further, the timing of hippocampal activity in relation to theta phase is vital in determining subsequent potentiation of neuronal activity. This could allow the emotionally modulated strengthening of novel and the gradual weakening of consolidated hippocampal memory traces observed in both wake and REM sleep. Hippocampal theta activity is also correlated with REM sleep acetylcholine levels – which are thought to reduce hippocampal afferent inputs in the neocortex. The additional low levels of noradrenaline during REM sleep, which facilitate recurrent activation within the neocortex, could allow the integration of novel memory traces previously consolidated during NREM sleep. We therefore propose that REM sleep mediates the prioritized processing of emotional memories within the hippocampus, the integration of previously consolidated memory traces within the neocortex, as well as the disengagement of consolidated neocortical memory traces from the hippocampus

Journal of Civil Engineering Construction Technology Integrated hydraulic design approach for cost effective aqueduct trough

An aqueduct structure is a complex structure as compared to bridge, as it takes canal water across stream and canal traffic over the trough. The water-tightness and free expansions -contractions of trough, canal water load as well as traffic load on the trough involves complex load combinations, for which the superstructure and substructure of it is required to be planned and designed. The object of this research paper is to develop an optimized hydraulic design, by integration of various theories applicable, to provide cost effective aqueduct structure. This integrated hydraulic design for an aqueduct trough aims at minimization of water-way area of moving water, thus, minimizing mass of moving water per unit length of an aqueduct trough, which will result into lesser water load on aqueduct trough which ensures less quantity of construction materials and thus the aqueduct substructure and superstructure is economical. A case study of the executed project is also depicted which shows around 29% saving in concrete quantity by this method

Security and privacy in V2X communications:how can collaborative learning improve cybersecurity?

Advances in cellular technology are a key driver of the growing automotive Vehicle to Everything (V2X) market. In V2X communications, information from sensors and other sources travels via high-bandwidth, low-latency, high-reliability links, paving the way to fully autonomous driving and intelligent mobility. With the future adoption of 5G and beyond (5G&B) networks, V2X is likely to generate a huge volume of data, which encourages the use of edge computing and pushes the system to learn the model locally to support real-time applications. However, the edge computing paradigm raises concerns about the security and privacy of local nodes (e.g., vehicles) and the increased risk of cyberattacks. In this article, we identify open research questions, key requirements, and potential solutions to provide cyber resilience in V2X communications.

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation