97 research outputs found
IOT Security Against Network Anomalies through Ensemble of Classifiers Approach
The use of IoT networks to monitor critical environments of all types where the volume of data transferred has greatly expanded in recent years due to a large rise in all forms of data. Since so many devices are connected to the Internet of Things (IoT), network and device security is of paramount importance. Network dynamics and complexity are still the biggest challenges to detecting IOT attacks. The dynamic nature of the network makes it challenging to categorise them using a single classifier. To identify the abnormalities, we therefore suggested an ensemble classifier in this study. The proposed ensemble classifier combines the independent classifiers ELM, Nave Byes (NB), and the k-nearest neighbour (KNN) in bagging and boosting configurations. The proposed technique is evaluated and compared using the MQTTset, a dataset focused on the MQTT protocol, which is frequently utilised in IoT networks. The analysis demonstrates that the proposed classifier outperforms the baseline classifiers in terms of classification accuracy, precision, recall, and F-score
Distributed Apportioning in a Power Network for providing Demand Response Services
Greater penetration of Distributed Energy Resources (DERs) in power networks
requires coordination strategies that allow for self-adjustment of
contributions in a network of DERs, owing to variability in generation and
demand. In this article, a distributed scheme is proposed that enables a DER in
a network to arrive at viable power reference commands that satisfies the DERs
local constraints on its generation and loads it has to service, while, the
aggregated behavior of multiple DERs in the network and their respective loads
meet the ancillary services demanded by the grid. The Net-load Management
system for a single unit is referred to as the Local Inverter System (LIS) in
this article . A distinguishing feature of the proposed consensus based
solution is the distributed finite time termination of the algorithm that
allows each LIS unit in the network to determine power reference commands in
the presence of communication delays in a distributed manner. The proposed
scheme allows prioritization of Renewable Energy Sources (RES) in the network
and also enables auto-adjustment of contributions from LIS units with lower
priority resources (non-RES). The methods are validated using
hardware-in-the-loop simulations with Raspberry PI devices as distributed
control units, implementing the proposed distributed algorithm and responsible
for determining and dispatching realtime power reference commands to simulated
power electronics interface emulating LIS units for demand response.Comment: 7 pages, 11 Figures, IEEE International Conference on Smart Grid
Communication
WETTABILITY OF OXIDE THIN FILMS PREPARED BY PULSED LASER DEPOSITION : NEW INSIGHTS.
Master'sMASTER OF ENGINEERIN
Minimal Intubating Dose of Succinylcholine: A Comparative Study of 0.4, 0.5 and 0.6 mg/kg Dose
Muscle relaxants are integral part of modern balanced anesthesia and succinylcholine, a depolarizing drug, is in use despite its adverse effects. The excellent intubating condition, fastest onset and shortest duration of action make it an excellent choice for anesthesiologists. The conventional dose of 1.5-2 mg/kg is commonly used for obtaining relaxation for intubation. This study was conducted with much smaller dose of succinylcholine as 0.4, 0.5 and 0.6 mg/kg to evaluate the acceptable intubating dose at 60 seconds, which was unlikely to have any untoward/side effects
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Hierarchical and tree-like data sets arise in many applications, including
language processing, graph data mining, phylogeny and genomics. It is known
that tree-like data cannot be embedded into Euclidean spaces of finite
dimension with small distortion. This problem can be mitigated through the use
of hyperbolic spaces. When such data also has to be processed in a distributed
and privatized setting, it becomes necessary to work with new federated
learning methods tailored to hyperbolic spaces. As an initial step towards the
development of the field of federated learning in hyperbolic spaces, we propose
the first known approach to federated classification in hyperbolic spaces. Our
contributions are as follows. First, we develop distributed versions of convex
SVM classifiers for Poincar\'e discs. In this setting, the information conveyed
from clients to the global classifier are convex hulls of clusters present in
individual client data. Second, to avoid label switching issues, we introduce a
number-theoretic approach for label recovery based on the so-called integer
sequences. Third, we compute the complexity of the convex hulls in
hyperbolic spaces to assess the extent of data leakage; at the same time, in
order to limit the communication cost for the hulls, we propose a new
quantization method for the Poincar\'e disc coupled with Reed-Solomon-like
encoding. Fourth, at server level, we introduce a new approach for aggregating
convex hulls of the clients based on balanced graph partitioning. We test our
method on a collection of diverse data sets, including hierarchical single-cell
RNA-seq data from different patients distributed across different repositories
that have stringent privacy constraints. The classification accuracy of our
method is up to better than its Euclidean counterpart,
demonstrating the importance of privacy-preserving learning in hyperbolic
spaces
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Edge devices can benefit remarkably from federated learning due to their
distributed nature; however, their limited resource and computing power poses
limitations in deployment. A possible solution to this problem is to utilize
off-the-shelf sparse learning algorithms at the clients to meet their resource
budget. However, such naive deployment in the clients causes significant
accuracy degradation, especially for highly resource-constrained clients. In
particular, our investigations reveal that the lack of consensus in the
sparsity masks among the clients may potentially slow down the convergence of
the global model and cause a substantial accuracy drop. With these
observations, we present \textit{federated lottery aware sparsity hunting}
(FLASH), a unified sparse learning framework for training a sparse sub-model
that maintains the performance under ultra-low parameter density while yielding
proportional communication benefits. Moreover, given that different clients may
have different resource budgets, we present \textit{hetero-FLASH} where clients
can take different density budgets based on their device resource limitations
instead of supporting only one target parameter density. Experimental analysis
on diverse models and datasets shows the superiority of FLASH in closing the
gap with an unpruned baseline while yielding up to
improved accuracy with fewer communication,
compared to existing alternatives, at similar hyperparameter settings. Code is
available at \url{https://github.com/SaraBabakN/flash_fl}.Comment: Accepted in TMLR, https://openreview.net/forum?id=iHyhdpsny
Byzantine-Resilient Federated Learning with Heterogeneous Data Distribution
For mitigating Byzantine behaviors in federated learning (FL), most
state-of-the-art approaches, such as Bulyan, tend to leverage the similarity of
updates from the benign clients. However, in many practical FL scenarios, data
is non-IID across clients, thus the updates received from even the benign
clients are quite dissimilar. Hence, using similarity based methods result in
wasted opportunities to train a model from interesting non-IID data, and also
slower model convergence. We propose DiverseFL to overcome this challenge in
heterogeneous data distribution settings. Rather than comparing each client's
update with other client updates to detect Byzantine clients, DiverseFL
compares each client's update with a guiding update of that client. Any client
whose update diverges from its associated guiding update is then tagged as a
Byzantine node. The FL server in DiverseFL computes the guiding update in every
round for each client over a small sample of the client's local data that is
received only once before start of the training. However, sharing even a small
sample of client's data with the FL server can compromise client's data privacy
needs. To tackle this challenge, DiverseFL creates a Trusted Execution
Environment (TEE)-based enclave to receive each client's sample and to compute
its guiding updates. TEE provides a hardware assisted verification and
attestation to each client that its data is not leaked outside of TEE. Through
experiments involving neural networks, benchmark datasets and popular Byzantine
attacks, we demonstrate that DiverseFL not only performs Byzantine mitigation
quite effectively, it also almost matches the performance of OracleSGD, where
the server only aggregates the updates from the benign clients
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