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Anomaly Detection in IoT Data Streams Based on Long Short-Term Memory

Anomaly Detection in IoT Data Streams Based on Long Short-Te...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Hussein, Layth Mohan, M. Rajeswari, P. Gurupandi, D. Saranya, N. Naga The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept of Ise Bangalore India Karpagam Institute of Technology Department of Ece Coimbatore India Panimalar Engineering College Department of Ece Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350360660

Nowadays, the swift growth of Ambient Intelligence (AmI) in Internet of Things (IoT), sensing devices have been generating many data streams in intelligent scenarios. The IoT deployment issues, complexity of systems is leading to abnormal behavior in data streams. Moreover, the dynamic and consecutive data streams generated in IoT systems leads to difficulty in determining anomaly detection. Therefore, this paper presents a Long Short-Term Memory (LSTM) and Sparse Autoencoder method for classification of data to determine anomaly behavior in IoT data streams. Firstly, a min-max normalization technique is employed to scale the input data for better results and Sparse Autoencoder utilizes sparsity penalty to capture relevant features. The LSTM network is employed for prediction tasks which leverages the ability to handle sequential IoT data streams and dropout layers are utilized to prevent overfitting. Finally, K-means clustering mechanism is used to establish an error threshold for anomaly detection. The performance of proposed LSTM and Sparse Autoencoder is evaluated on SKAB dataset which illustrated higher precision (0.824), recall (0.982) and f1 scores (0.847). The proposed method outperformed existing anomaly detection methods such as Neural Network (NN), Adaptable and Interpretable Framework for Anomaly Detection (AID). © 2024 IEEE.

关键词： Long short-term memory

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An Innovative Method for Mobile Network Payment Security System Based on hybrid LSTM-Ensemble Learning-Based Model 3

An Innovative Method for Mobile Network Payment Security Sys...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Sujatha, N. Pramod, S. Aravindh, S. Babu, Balagopala Ramesh Devi, S. Rukmani Ananthi, M. B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Department of Mathematics Karnataka Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India SRKR Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamilnadu Chennai India KGiSL Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350354171

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. Mobile phones have practically become people's constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer's biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTM-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTM and Ensemble Learning. © 2024 IEEE.

关键词： Contrastive Learning

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Levy Flight Strategy with Chicken Swarm Optimization Algorithm for Real-Time Anomaly Detection in Cybersecurity

Levy Flight Strategy with Chicken Swarm Optimization Algorit...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Saisree, Mallubhotla Syamala Subramanya, S.G. Jagadish, Sripelli Sureshkumar, S. Al-Farouni, Mohammed Hyderabad India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education Department of Cse Krishnankoil India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350375442

The cybersecurity field has witnessed several Intrusion Detection System (IDS) and Anomaly Detection, which are essential for identifying malicious activities in network traffic. However, growing volume and diversity of modern network attacks pose challenges, impacting the accuracy of these detection systems. This paper, Levy Flight strategy with Chicken Swarm Optimization Algorithm (LCSO) and Gradient-Boosted Decision Tree (GBDT) techniques achieve better accuracy. GBDT builds decision trees iteratively and helps in reducing bias and variance, leading to high accuracy models, especially with feature selected by using CSO. The CSO algorithm applied to various types of data, making it a flexible feature selection method for different cybersecurity. Initially data obtained from KDD-99 and NSL-KDD dataset and normalization is essential to prevent feature with large range from dominating the learning process. The feature selection using LCSO algorithm focusing on the most relevant features, helps improved efficiency achieve classification accuracy. The GBDT build better classifiers that handle both normal and anomalous pattern efficiently, especially with optimized feature set. The proposed LCSO-GBDT algorithm achieves better accuracy of 99.15% on KDD and 99.12% on NSL-KDD dataset when compared to existing techniques Deep Neural Network (DNN) and Bat Algorithm approach. © 2024 IEEE.

关键词： Network intrusion

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Fostering Global Stability: Machine Learning for Anticipating Terrorism

Fostering Global Stability: Machine Learning for Anticipatin...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Archana Naik P Ushashree Basavaraj Muddapu Uttkarsh Dutt Guttedar Muddam Venkatesh Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Geethanjali College of Engineering & Technology Hyderabad India

The threat of terrorism remains a significant global concern, with governments and security agencies continually seeking advanced methods for mitigating and preventing attacks. Machine learning has emerged as a powerful tool for predictive analysis, offering the potential to anticipate and prepare for potential terrorism incidents. This paper aims to develop a machine learning model that can predict the likelihood of a terrorism attack based on historical data and relevant features. The results of the study will offer valuable insights into the potential application of machine learning to enhance counterterrorism efforts. It’s important to emphasize that terrorism is a multifaceted issue and should be seen as a supplementary tool rather than a comprehensive solution.

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Predictive Modeling of Epidemic Diseases based on Vector-Borne Diseases using Support Vector Machine with Extreme Gradient Boost 1

Predictive Modeling of Epidemic Diseases based on Vector-Bor...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Satish, E.G. Haq, Quazi Mohd. Imranul Mahaboob John, Y.M. Hassan, Muntather Muhsin Jothi, E. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India University of Nizwa College of Arts and Sciences Department of Biological Sciences and Chemistry Nizwa Oman Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

In recent years, the globalization and progressive climatic changes lead to increase in the occurrence of Vector Borne Diseases (VBD) from endemic regions to non-endemic regions. The common forms of VBD includes malaria, chikungunya, dengue fever, yellow fever and plague. The existing methods for predicting epidemic diseases did not handle non-linear relationships and complex interactions. Therefore, this paper proposes Support Vector Machine with Extreme Gradient Boost (SVM-XGBOOST) for predicting epidemic diseases based on vector borne diseases. Initially, vector borne diseases dataset is taken which contains epidemiological data and information related to climate. Then, this data is preprocessed by using cross validation to generalize the model well and it evaluates the quality of data. After that, Least Absolute Shrinkage and Selection Operator (LASSO) or (L1Regularization) is used for selecting features which selects the relevant features automatically and handles noisy data. finally, classification is done by using SVM-XGBOOST which handles non-linear relationships and handles complex interactions. The proposed SVM-XGBOOST achieved better results in terms of accuracy (98.45%), Precision (95.50%), Recall (97.98%), and F1 score (9 7. 2 5 %). © 2024 IEEE.

关键词： Vectors

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Resource Allocation and Task Scheduling in Cloud Computing Using Improved Bat and Modified Social Group Optimization 2

Resource Allocation and Task Scheduling in Cloud Computing U...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Ganesan, Thirusubramanian Almusawi, Muntather Sudhakar, K. Sathishkumar, B.R. Kumar, Komuravelly Sudheer Cognizant Technology Solutions TX United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept.of Ai & Ds Bengaluru India Sri Ramakrishna Engineering College Department of Ece Coimbatore India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350372892

Nowadays, Cloud computing is growing quickly with a maximum number of users and clients that are reaching from single users to huge business organizations. The cloud is facing significant challenges in managing large volumes of data and several cloud resources due to its rapid growth. Several optimization algorithms faced a challenge in scalability when working with more complicated scheduling scenarios. A combination of Resource Allocation and Task Scheduling using Improved Bat Optimization Algorithm (IBOA) by using dynamic weights with the speed update formula and increasing the random searchability and Modified Social Group Optimization (MSGO) by modifying the acquiring phase is introduced in this research. The developed IB and MSGO methods are simulated using a various simulation setup and the obtained results like response time, utilization of resources and energy consumption are compared against existing methods to measure the efficiency. For 100 tasks, the proposed IB and MSGO method achieved 32.5 watts of energy. The obtained results exposed that IB and MSGO method has better performance compared with the existing method namely Multi-Objective Task Scheduling Grey Wolf Optimization (MOTSGWO) in terms of energy consumption. © 2024 IEEE.

关键词： Cloud platforms

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Inertia Weight Based Carpet Weaver Optimization for Energy Efficient Routing in Mobile Ad-hoc Networks

Inertia Weight Based Carpet Weaver Optimization for Energy E...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Pramodhini, R. Revathi, V.M. Chanti, Yerrolla Hassan, Muntather Muhsin Kiran, J. Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350375442

The Mobile Ad-hoc Network (MANET) includes nodes connected by wireless links in which each node potentially performing as both host and router for the other nodes. The existing techniques reduces the PDR and enhance the delay which affect the network energy efficiency. Therefore, this research proposes an Inertia Weight based Carpet Weaver Optimization (IWCWO) algorithm for energy-efficient routing in MANET. The inertia weight is integrated into CWO which balance exploration and exploitation during the optimization process, making it suitable for a dynamic and distributed system such as MANET. By optimizing the routing path, IWCWO improves the robustness of network data transmission and reduces delay while significantly improving network throughput. The fitness functions for IWCWO for selecting optimal Cluster Head (CH) and routes are communication cost, energy ratio, network load and total number of gateway hops. The metrics such as Packet Delivery Ratio (PDR), delay and throughput are taken for estimating IWCWO performance. The proposed IWCWO achieves high PDR of 99.91%, throughput of 249.25Kbps with less delay of 1535ms which is better than Energy-Efficient Perspicacious Ant Colony Optimization (EEPACORP). © 2024 IEEE.

关键词： Mobile ad hoc networks

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Multi Resolution based Graph Convolutional Networks for Detecting Communities in Social Networks

Multi Resolution based Graph Convolutional Networks for Dete...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Harsha, P. Obaid, Saif Mazumder, Debarshi Nagendar, Y. Almoussawi, Zainab Abed Malla Reddy Engineering College for Women Cse Department Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Sr University School of Computer Science and Engineering Warangal India Ahl Al Bayt University College of Islamic Science Karbala Iraq

ISBN: (纸本)9798350360660

Detecting the fundamental community structure of a social network is crucial in social network analysis. All though, there are many recent approaches in detecting communities, analysing large scale social networks at all resolutions is a challenging task. To solve this problem, the proposed research focuses on developing an efficient Multi Resolution-Graph Convolutional Network (MR-GCN) to identify the communities in social networks. The proposed method with the graph mining technique improves the output graph quality in identifying the communities. The conventional approaches depend on the labelled data, which is limited data in the real time applications. Initially, the nodes of raw graph are converted into embeddings and are further processed by multiple layers of a GCN to incorporate both the local and global structural features. The MR feature fusion is used to combine node embeddings from different resolutions, which includes all necessary information. The final phase includes Adam optimizer to train the model, and the overall experimental results obtained are accuracy of 97.85%, precision of 96.57%, and f1-score of 95.77%. The proposed method results are compared with the existing methods such as Graph Neural Network encoding scheme for a Multi-Objective Evolutionary Algorithm (GNN-MOEA) and Markov Random Fields GCN (MRFasGCN). © 2024 IEEE.

关键词： Convolutional neural networks

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Cluster Head Selection using Dingo Optimization Algorithm in Mobile Ad Hoc Network

Cluster Head Selection using Dingo Optimization Algorithm in...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Kumar Pareek, Piyush Polimetla, Kiran Zanke, Pankaj Ali, Hassan Mohamed University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India Adobe Inc Department. of Information Technology AustinTX United States Sapiens International Corporation The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350360660

Efficient Energy-Efficient Cluster Head Selection (EECHS) remains a critical issue in Mobile Ad Hoc Networks (MANETs), where the selection of an appropriate cluster head (CH) can significantly enhance power efficiency. Clustering in routing plays a vital role in optimizing MANET performance by coordinating pathways through various analytics and criteria. Nodes in MANETs form clusters to improve communication, employing strategies such as distributed clustering and cluster-based routing. Despite their application, these methods often suffer from complexity and high overhead, which compromise their *** study proposes the Dingo Optimization Algorithm (DOA) as a viable solution to the EECHS problem. DOA inherits its exploration and exploitation processes from the natural behavior of dingoes, involving techniques such as pursuing, approaching, encircling, and pestering to identify the optimal CHs. Extensive experimental research conducted on the NS2 simulator demonstrates that the proposed method achieves a packet delivery ratio (PDR) of 96%, a throughput of 240 Kbps, and an energy consumption of 21 mJ for a network with 500 nodes. The simulation results and replication findings indicate that this strategy is particularly suitable for sensitive MANET applications, outperforming existing routing tactics in terms of efficiency and effectiveness. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Adaptive Energy Management Strategies for Hybrid Energy Systems Using Multi Object Grey Wolf Optimization

Adaptive Energy Management Strategies for Hybrid Energy Syst...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Karamsetty, Shouryadhar Husain, Saif O. Soujanya, Thota Trisheela, S. Abdul Hussein, Abbas Hameed Mallareddy Engineering College for Women Secunderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Ahl Al Bayt University College of Pharmacy Karbala Iraq

ISBN: (纸本)9798350360660

In the current scenario, sunshine and wind are renewable energy sources, which are available across the planet, and they are sustainable alternatives because of self-reproduction. Even though, all isolated resources are established manually and simultaneously in a globular way. If networks perform inconsistently and resources produce less power than required. To overcome this issue, the Multi Object Grey Wolf Optimization (MOGWO) is proposed to manage renewable resources for grid systems. The proposed MOGWO optimizes the parameters and manage the renewable energy sources including Photo-Voltaic (PV), wind and battery with a duty cycle. The peak demands of resources are effectively managed by using proposed MOGWO and over voltage is encountered by the duty cycle. The energy is utilized in a consistent way by switching DC-converters. In the first zero interval, the proposed MOGWO established powers of PV 80.12W, wind 67.45W and battery 29.45W and in final interval, the proposed MOGWO provided powers of PV 86.45W, wind 71.39W and battery 29.99W. © 2024 IEEE.

关键词： Hybrid power

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