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AS-CL IDS: anomaly and signature-based CNN-LSTM intrusion detection system for Internet of Things
In recent years, the internet of things (IoT) has had a significant impact on our daily lives, offering various advantages for improving our quality of life. However, it is crucial to prioritize the security of IoT devices and the protection of user's personal data. Intrusion detection systems (IDS) play a critical role in maintaining data privacy and security. An IoT IDS continuously monitors network activity and identifies potential security risks or attacks targeting IoT devices. While traditional IDS solutions exist, intrusion detection heavily relies on artificial intelligence (AI). AI can greatly enhance the capabilities of IoT IDS through real-time monitoring, precise threat identification, and automatic response capabilities. It is essential to develop and utilize these technologies securely and responsibly to mitigate potential risks and safeguard user privacy. A hybrid IDS was proposed for anomaly-based and signature-based intrusions, leveraging convolutional neural network with long short-term memory (CNN-LSTM). The name of the proposed hybrid model is anomaly and signaturebased CNN-LSTM intrusion detection system (AS-CL IDS). The AS-CL IDS concentrated on two different IoT IDS detection strategies employing a combination of deep learning techniques. The model includes model training and testing as well as data preprocessing. The CIC-IDS 2018, IoT network intrusion dataset, MQTT-IoT-IDS2020, and BoTNeTIoTL01 datasets were used to train and test the AS-CL IDS. The overall performance of the proposed model was assessed using accepted assessment metrics. Despite reducing the number of characteristics, the model achieved 99.81% accuracy. Furthermore, a comparison was made between the proposed model and existing alternative models to demonstrate its productivity. As a result, the proposed model proves valuable for predicting IoT attacks. Looking ahead, the deployment strategy of the IoT IDS can anticipate the utilization of real-time datasets for future implementations. 2023 Jinsi Jose and Deepa V. Jose. -
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Performance Analysis of Deep Learning Algorithms for Intrusion Detection in IoT
Due to the wide availability of IoT devices at affordable cost and the ease of use has increased IoT devices increased usage. Due to the enormous usage of the Internet of Things (IoT) devices, the security aspects related to the data are also a significant concern in this data-driven world. Negligence of security measures from users can result in severe data falsification or data thefts. In this scenario, the Intrusion Detection System has a pivotal role in IoT security. Incorporating the deep learning techniques is an effective way to predict various attacks, either known or unknown. This paper highlights the various security threats associated with IoT, the importance of deep learning in IoT intrusion detection, and various IoT intrusion detection systems using deep learning. Comparative analysis of the different deep learning techniques was performed. The results have shown Convolution Neural Networks gave high accuracy in prediction based on various evaluation metrics. 2021 IEEE. -
Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things
The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed. 2021 IEEE. -
Benzoyl hydrazine-anchored graphene oxide as supercapacitor electrodes
In this study, benzoyl-hydrazine anchored graphene oxide (BHGO) is synthesised using graphene oxide (GO) and benzoyl hydrazine (BH) via a simple, cost effective ultrasonic assisted chemical route. BH acted as a nitrogen source, reducing agent, and morphology modifier resulting in good electrochemical performance of BHGO. The supercapacitor behaviour of BHGO is investigated in different aqueous electrolytes and it exhibits a specific capacitance of 170 F g?1 at a current density of 1 A g?1 in 1 M H2SO4 and capacitive retention of 85% over 5000 cycles at 5 A g?1. This high performance is attributed to the enrichment of electroactive sites of GO through nitrogen moieties enhancing faradaic redox reactions and thereby the polarization at the electrode surface. 2020 Elsevier B.V. -
Emerging ternary nanocomposite of rGO draped palladium oxide/polypyrrole for high performance supercapacitors
In this work, novel electrodeposited palladium oxide-polypyrrole (PdP) and its ternary composite with reduced graphene oxide (PdPGO) draped over the surface of PdP were synthesised to achieve the excellent electrochemical properties and high stability. An exhaustive study has been carried out to correlate the crystalline structure, chemical bonding, morphological behaviour, redox reactions at the electroactive species, and its promising influences on the electrochemical performance. The electrodeposited PdPGO composite on stainless steel bestows superior electrochemical properties and a specific capacitance of 595 F g?1 at 1 A g?1 in 1 M H2SO4. The incorporation of rGO with the PdP matrix prevents the aggregation of rGO layers and is responsible for the enhanced electrostatic interactions at the electrode-electrolyte interface in PdPGO. Outstanding supercapacitance retention of 88% even after 5000 cycles at 5 A g?1 was accomplished for the ternary composite of Pd. These profound electrochemical characteristics are due to the synergistic effect of the individual components involved, manifest a great potential for Pd based composites toward novel electrode materials for supercapacitors of high efficiency. This method facilitates blueprints for synthesizing a series of advanced electrode materials for enhancing high storage capability. The high electrochemical performance of the PdPGO reveals how synergy plays a very important role to work on the blueprint to create active electrode materials for energy storage solutions. 2020 Elsevier B.V. -
Price Discovery of Currency Futures at NSE
The current study aimed to examine the causal relationship between the NSE currency future rates and currency spot rates in order to identify the price discovery mechanism at NSE market and its integration with foreign exchange market (spot market). To study the causal relationship between the said markets, we have considered daily closing rates for NSE currency futures and currency spot rates for selected pairs of currencies, i.e. USD/INR, GBP/INR, JPY/INR and EURO/INR. The data was obtained from www.nseindia.com and www.investing.com for the period from Jan-2010 to Sep-2017, which makes approximately 1750 observations for each currency pair in each market. It is found that the spot rate for JPY/INR leads the future rate. It is also identified that the spot rate for USD/INR does not cause the changes in futures. It indicates that the market integration between spot and futures at NSE for currency pair USD/INR is strong compared to other selected currency pairs. From the variance decomposition test we found that there is almost no impact of variance in USD/INR spot rate on future rate variance forecast errors. It implies that the causal relationship between for USD/INR spot and future rates is strong and mature compared to the measured causal relationships for the remaining currency pairs. This study concludes that the price discovery process for currency pair USD/INR is better at NSE currency futures among the selected currency pairs. Copyright 2022 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License -
Impact of demand response contracts on short-term load forecasting in smart grid using SVR optimized by GA
In a Smart Grid environment the performance measure of the grid is calculated by considering the fact that how accurately and precisely a load forecasting (LF) is done. A true Load Forecasting is vital to make a current grid smarter and more reliable when it comes to its performance. Demand Response (DR) contracts is a type of program in smart grid where the customer is free to select a type of contract which is given by the utility and is one of the growing factor which affects the load forecasting results in the Smart Grid, therefore in order to do a complete evaluation of smart grid performance and to accomplish an accurate load forecasting results the different types of contracts should also be studied. The purpose of this study is to accomplish two goals. The first one is to develop a suitable model which can incorporate various factors that can affect the load forecasting results. The subsequent goal is to identify the impact of the demand response contracts on the load forecasting results. In the proposed study, Support Vector Machine-Regression (SVR) is selected as the base methodology to perform a Short - Term Load Forecasting (STLF) under smart grid environment. 2017 IEEE. -
An Automated Deep Learning Model for Detecting Sarcastic Comments
The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models. 2021 IEEE. -
Synthesis, structural characterization, electrochemical and photocatalytic properties of vanadium complex anchored on reduced graphene oxide
In this work, vanadium complex anchored reduced graphene oxide (rGO-VO) was successfully synthesized by coordination interaction with phenyl azo salicylaldehyde (PAS) coupled trimethoxy silyl propanamine (TMSPA). The physicochemical and microscopic properties of rGO-VO were studied with different analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, Scanning electron microscopy (SEM), and Transmission electron microscopy (TEM) which confirmed the synthesis of rGO-VO. The electrochemical studies of rGO-VO in glassy carbon electrode demonstrated high current density because of the amazing electrochemical properties of rGO. The photocatalytic studies of anchored rGO-VO and VO(acac)2 toward MB dye indicated that anchored rGO-VO with visible light irradiated MB was degraded fast as compared to VO(acac)2. 2021 Taylor & Francis Group, LLC. -
Machine Learning Algorithms for Prediction of Mobile Phone Prices
The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Photophysical and Electrochemical Studies of Anchored Chromium (III) Complex on Reduced Graphene Oxide via Diazonium Chemistry
Covalently anchored chromium complex on reduced graphene oxide (rGO-Cr) is successfully synthesised through trimethoxy silyl propanamine (TMSPA) and phenyl azo salicylaldehyde (PAS) coupling. The rGO-Cr is characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron dispersive analysis of X-rays (EDAX), Raman spectroscopy, scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM). Absorption and emission properties of rGO-TMSPA-PAS are studied by excitation dependent photoluminescence emissions at room temperature. Electrochemical sensing activity of rGO-Cr is monitored for paracetamol using modified glassy carbon electrode. Cyclic voltammetry measurements indicated that rGO-Cr substantially enhance the eletrochemical response of paracetamol. The experimental factors are investigated and optimized. 2019 John Wiley & Sons, Ltd. -
GWebPositionRank: Unsupervised Graph and Web-based Keyphrase Extraction form BERT Embeddings
Automatic keyphrase extraction is considered a preliminary task in many Natural Language Processing (NLP) applications that attempt to extract the descriptive phrases representing the main content of a document. Owing to the need for a large amount of labelled training data, an unsupervised approach is highly appropriate for keyphrase extraction and ranking. Keyphrase Extraction with BERT Transformers (KeyBERT) leverages the BERT embeddings that utilize the cosine similarity to rank the candidate keyphrases. However, extracting keyphrases based on the fundamental cosine similarity measure does not consider the spatial dimension locally and globally. Hence, this work focuses on enhancing the KeyBERT-based method with a Graph-based WebPositionRank (GWebPositionRank) design. The proposed unsupervised GWebPositionRank is the composition of graph-based ranking, referring to local analysis and web-based ranking, referring to the global analysis. To spatially examine the keyphrases, the proposed approach conducts the keyphrase position analysis at the document level through graph-based ranking and the web level using the WebPositionRank algorithm. Initially, the proposed approach extracts the coarse-grained keyphrases from the KeyBERT model and ranks the extracted keyphrases, the modelling of quality and fine-tuned keyphrases. In the GWebPositionRank method, the quality keyphrase ranking involves the document-level position analysis and four different graph centrality measures in a constructed textual graph for each text document, whereas the fine-tuned keyphrase ranking involves the web-level position analysis and diversity computation for the quality keyphrases extracted from the graph-based ranking method. Thus, the proposed approach extracts a set of potential keyphrases for each document through the advantage of the GWebPositionRank algorithm. The experimental results illustrate that the proposed unsupervised algorithm yielded superior results than the comparative baseline models while testing on the SemEval2017 dataset. 2024 IEEE. -
MADeGen: Multi-Agent based Deep Reinforcement Learning for Sequential Keyphrase Generation
Keyphrase generation is an essential tool in the field of natural language processing for information retrieval, document summarization, and text recommendation applications, extracting succinct and representative phrases from the text document. Traditional keyphrase extraction methods applied the supervised or unsupervised learning fail to capture the sequential keyphrase generation in a dynamic environment. The keyphrase generation approaches lack focus on explicitly discriminating the present and absent keyphrases, leading to the inadequate generation of semantically rich absent keyphrases. Hence, this work utilizes the potential benefits of reinforcement learning with the design of a distinguished reward function for present and absent keyphrases for sequential decision-making in the keyphrase generation. Thus, this work presents a novel keyphrase generation system, MADeGen, utilizing Multi- Agent Deep Reinforcement Learning (MADRL). In particular, a multi-agent reinforcement system collaboratively enables the generation of representative and coherent keyphrases by the evaluation metric-aware cooperative reward function analysis and adaptively training the agents. The proposed MADeGen incorporates two major phases, such as multi-agent modelling and actor critic-based policy optimization towards accurate keyphrase generation. In the first phase, the proposed approach designs two learning agents, including the extraction agent and generation agent, with the incorporation of a pre-trained language model. In the multi-agent system, the generation agent is the finetuned version of the extraction agent with the integration of the Wikipedia source. Secondly, the evaluation-aware adaptive reward function is designed to evaluate each agent's generated keyphrases with reference to ground-truth keyphrases. In subsequence, the cooperative reward analysis triggers the actor critic-based policy optimization for the generation agent in the multi-agent system to precisely generate the semantically relevant keyphrases with the assistance of an external web source. Experimental results on several benchmark datasets, such as Inspec, PubMed, and wiki20, illustrate the effectiveness of the proposed MADeGen compared to the existing keyphrase extraction models, yielding state-of-the-art performance in keyphrase extraction tasks. The proposed MADeGen proves its higher performance in the present as well as absent keyphrase extraction as 0.367 and 0.438 F1-score, respectively, while testing on the Inspec dataset. (2024), (Intelligent Network and Systems Society). All Rights Reserved. -
Spectrochemical and theoretical approaches for acylhydrazone-based fluoride sensors
Abstract: Acylhydrazone derivatives N?-[1-(2-fluorophenyl)ethylidene]pyridine-3-carbohydrazide (R1) and N?-[2-fluorobenzylidene]benzohydrazide (R2) were synthesized from their corresponding hydrazides and characterized by spectroscopic methods. The response of these acylhydrazones towards different anions was studied by colorimetric and spectrofluorometric methods in acetonitrile. The receptors exhibited a specific response towards fluoride ion. The binding affinity of the receptors with fluoride anion was studied by fluorescence spectroscopic techniques and abinitio density functional theory calculations with Beckers three-parameter LeeYangPar (B3LYP) exchange functional with 6-311G basis set. Graphical abstract: [Figure not available: see fulltext.]. 2018, Springer Nature B.V. -
An electrochemical sensor for nanomolar detection of caffeine based on nicotinic acid hydrazide anchored on graphene oxide (NAHGO)
A simple modified sensor was developed with nicotinic acid hydrazide anchored on graphene oxide (NAHGO), by ultrasonic-assisted chemical route, using hydroxy benzotriazole as a mediator. Structural and morphologies of NAHGO samples were investigated in detail by Fourier-Transform Infrared spectroscopy (FT-IR), Powder X-ray diffraction (P-XRD), Raman spectroscopy, Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Thermogravimetric analysis (TGA). The detailed morphological examination and electrochemical studies revealed the delaminated sheet with the tube-like structure of NAHGO provided the route for more electroactive surface which influenced the electrooxidation of caffeine with increased current. The electrochemical behaviour of NAHGO on a glassy carbon electrode (GCE) for caffeine detection was demonstrated by employing voltammetric techniques. The influence of scan rate, pH, and concentration on caffeine's peak current was also studied. The NAHGO sensor was employed for the determination of caffeine in imol plus and energy drinks. The detection limit determined was 8.7 109M, and the best value was reported so far. The results show that NAHGO modified electrodes are one of the best preferences to establish new, efficient, and reliable analytical tools for the detection of caffeine. 2021, The Author(s). -
Dimensionally engineered ternary nanocomposite of reduced graphene oxide/multiwalled carbon nanotubes/zirconium oxide for supercapacitors
Three dimensional (3D) hybrid nanoarchitecture of two-dimensional (2D) reduced graphene oxide/one dimensional (1D) multiwalled carbon nanotube and zero-dimensional (0D) zirconium oxide (ZrO2) nanoparticles (rGO/MWCNT/ZrO2) was synthesised by a simple hydrolysis method for high performance supercapacitors. To unlock the properties of individual materials to the maximum, binaries of ZrO2 with GO and MWCNT were also synthesised. The increased wettability, integrated structure, and the synergistic effect of rGO, MWCNT, and ZrO2 in rGO/MWCNT/ZrO2 (GMZ) offer a capacitance of 357 F g?1 at 1 A g?1 with excellent capacitance retention of 98% across 5000 cycles. 1D structure of MWCNT creates an exceptional conductive network with rGO due to the confinement of electrons and ions without disturbing its electronic structure. The intriguing supercapacitor performance of differently dimensioned framework with ZrO2 emphasises the engineered orientation and tuning of a designed environment for its appropriateness, uniqueness, and sensitivity to push up enhanced performance. 2021 Elsevier B.V. -
The study of algal diversity from fresh water bodies of Chimmony Wildlife Sanctuary, Kerala, India
The algal diversity of the freshwater ecosystem is very significant because they are the primary energy producers in the food web. The study for the algal diversity was conducted at Chimmony Wildlife Sanctuary, Thrissur, Kerala, India, from selected sampling sites (Pookoyil thodu, Kidakkapara thodu, Viraku thodu, Nellipara thodu, Anaporu thodu, Kodakallu thodu, Odan thodu, Mullapara thodu, Payampara thodu, Chimmony dam). The identified algal species belong to four different classes: Chlorophyceae, Euglenineae, Rhodophyceae, and Cyanophyceae. Sixty-one algal species were identified, represented by 37 genera, 22 families, and 14 orders. Among the four, Chlorophyceae was the dominant class. Jose & Xavier 2022. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and distribution of this article in any medium by providing adequate credit to the author(s) and the source of publication. -
Seasonal Variation of Physicochemical Parameters and Their Impact on the Algal Flora of Chimmony Wildlife Sanctuary
Background and Objective: The lack of biodiversity knowledge and biodiversity loss are the two inevitable truths around us. Algae are the most crucial organism in our entire biodiversity. The seasonal variation of algal diversity can monitor the environmental changes of the freshwater ecosystem. The present study was conducted because the seasonal changes of algal diversity in Chimmony Wildlife Sanctuary were utterly unknown. Materials and Methods: The algal samples were collected and preserved from ten stations for three seasons (pre-monsoon, monsoon, post-monsoon). The physicochemical parameters of water like temperature, pH, total dissolved solids, total dissolved oxygen, total alkalinity and light intensity of the sampling stations were recorded. Results: The study revealed that the seasonal variation of physicochemical parameters provoked a change in the diversity of Algae. The Chimmony Wildlife Sanctuary has its highest algal diversity during pre-monsoon season. The Chlorophyceae Algae were dominant during the pre-monsoon season, while the Cyanophycean Algae were dominant during monsoon season. The ANOVA (two-way) analysis showed no significant difference between stations and there is a considerable difference between seasons for dissolved oxygen, alkalinity, temperature and total dissolved solids. While for pH, it showed no significant difference between seasons and stations but for light intensity, it showed a substantial difference between stations and seasons. A negative correlation was observed between algal species and seasons. The temperature and dissolved oxygen showed a negative correlation. Conclusion: The physicochemical parameters were changed according to the seasonal variation. Since Algae act as a biological pollution indicator for all the water resources, the study of algal flora according to the seasonal variation is crucial. 2022 Joel Jose and Jobi Xavier. -
Study of mineral and nutritional composition of some seaweeds found along the coast of Gulf of Mannar, India
The presence of Algae on the Earth is ubiquitous. The industry that widely uses algae is food industry, where the algae are used as a food supplement and also as an addition to the nutrient rich food. This study emphasizes on the mineral and nutritional composition of the selected fourteen algal species which are abundantly found along the coast of the Gulf of Mannar. The selected species of algae belong to different algal families such as Chlorophyta, Phaeophyta and Rhodophyta. The amount of minerals such as Ca, Zn, Fe, K, Mg, Mn, and Cu were estimated by employing the method of acid digestion followed by atomic absorption spectroscopy. We estimated the nutritional content based on the assessment of total protein, carbohydrate, phenol, ash and moisture contents of the algal species. The results based on the analysis of the mineral content in the algal seaweeds depicted that the seaweeds comprised of high amount of the macro minerals and trace minerals. Estimation of nutritional composition revealed that these algal species are rich in protein and carbohydrate. The ash contents were found to be very high in Jania rubens (86.66%), Padina boergesenii (85%) and Valoniopsis pachynema (84%). Based on the present study we infer that the algal seaweeds contained high amount of the nutritional compounds, which might pave the way for a higher standard of nutritional supply to the humans in the future. Jose & Xavier (2020). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/).