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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Prasad, Sanjana Sharma, Ishu Arun, S. Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

ISBN: (纸本)9798350370249

DNS attacks can be caused by various methods such as Rebinding attacks, Cryptojacking and Domain Generation Algorithm. Classifying DNS attacks in a timely manner and taking necessary action to mitigate the issue is important. This research study describes the hybrid model of Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) that is used to improve the detection or categorization of DNS attacks. A dataset called CIRA-CIC-DoHBrw-2020 is used to train the suggested hybrid mode. According to experimental data, the LSTM prototype outperformed CNN with respect to identifying or categorizing DNS attacks. The CNN-LSTM and CNN-GRU hybrid models have the ability to improve the model's accuracy. CNN was used for feature extraction and LSTM for temporal modeling in the CNN-LSTM hybrid model. The suggested CNN-GRU hybrid model, on the other hand, obtains the greatest accuracy of 95.16%, demonstrating the efficacy of integrating GRU with CNN to effectively recognize or categorize both temporal and geographical characteristics of DNS network data. The CNN-GRU model's enhanced performance demonstrates how well merging different neural network architectures—like CNNs and GRUs—may enhance convergence and lower error rates. © 2024 IEEE.

关键词： Convolutional Neural Networks DNS Security Gated Recurrent Units Infrastructure Security Network Security Network Traffic Classification

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A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

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Intelligent Automation & Soft Computing 2023年第3期35卷 3501-3515页

作者： M.Daniel Nareshkumar B.Jaison Department of Electronics and Communication Engineering R.M.K.Engineering CollegeKavaraipettai601206India Department of Computer Science and Engineering R.M.K.Engineering CollegeKavaraipettai601206India

With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various *** fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much *** than ever before,there is a plethora of info about sign language usage in the real *** languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign *** main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the *** continuous sign language,entire words’hand signs are *** paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classiﬁcation accuracy on the data,thereby showing that interpreters can be implemented in the real *** newly proposed Mobile-NetV2 architecture serves as the backbone of this *** is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of *** the proposed architecture in this paper,the classiﬁcation accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.

关键词： Deep learning machine learning classiﬁcation ﬁlters american sign language

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Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave communication Networks

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China communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

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Transfer learning based multi-class malware classification using VGG16 feature extractor and SVM classifier 10

Transfer learning based multi-class malware classification u...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Saggu, Harman Singh, Karandeep Suresh Babu, T.N. Punjabi University Department of Computer Science and Engineering Patiala India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350353068

Malicious programs are becoming a severe problem in today's internet-connected world. Several works have explored image analysis-based malware detection in recent years. The image analysis method converts the malicious code into gray-scale images, which enables the use of deep learning models in the domain of malware detection. This paper proposes a new architecture using a fine-tuned Visual Geometry Group(VGG16) model with a support vector machine (SVM) classifier to achieve high-performance accuracy in detecting and classifying evolving malware. At first, the malicious code is represented in gray-scale images, which are further reshaped and resized to be fed into a fine-tuned VGG16 model as end-to-end feature extraction with the least dependence on human-crafted feature engineering. The extracted textural feature maps are further correlated to remove the highly correlated features before using them to train a support vector machine (SVM) classifier. The experiments are performed on public bench-marked MalImg datasets and actual malware samples. The VGG 16+ SVM model obtained 99.25% classification accuracy for the MalImg and 80.29% for real-world malware, which signifies that the model can detect real-world instances of malware. The proposed method outperforms similar existing works and is suitable for deployment in an IoT environment due to the least cost involved in feature engineering. © 2024 IEEE.

关键词： Deep learning

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Deep Learning Analysis for Skin Cancer Detection

Deep Learning Analysis for Skin Cancer Detection

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International Conference on VLSI, Signal Processing, Power electronics, IoT, communication and Embedded Systems, VSPICE-2022

作者： Singh, Chandra Nischitha Shetty, Shailesh S. Bekal, Anush Bhat, Sandeep Badiger, Manjunatha Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Mangaluru India Department of Electronics & Communication Engineering Shree Devi Institute of Technology Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Mangaluru India

ISBN: (纸本)9789819944439

In the present world, skin cancer is the most widely recognized reason for death among people. Skin malignancy is an unusual development of skin cells. Frequently created on the body part exposed to the sunlight;however, it can happen on any place on the body. The majority of the skin malignancy is treatable at the beginning phase. So an early and quick identification of skin disease can spare the patient's life. With the new innovation, early identification of skin malignancy is conceivable at the introductory stage utilizing picture handling. The skin cancer detection using image processing is based on the detection of skin cancer types at its earliest stage. There are many types of skin cancers found. It is difficult to identify the type of skin cancer at the earlier stage, manual identification can often be time consuming and inaccurate. Doctors are able to identify the symptoms of skin cancer but are unable to identify the type of skin cancer in the initial stage. So the doctors will wait until it gets blotted but by that time the disease will become out of control. So a software is developed to help the Skin Cancer Detection at its earliest stage by passing valid input images. So this chapter explains about a method to identify and classify the skin cancer using images using Convolutional Neural Network (CNN) algorithm. The accuracy obtained by using the proposed method is 89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Disease control

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Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

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computer Systems science & engineering 2023年第6期45卷 2635-2645页

作者： J.R.Arunkumar S.Velmurugan Balarengadurai Chinnaiah G.Charulatha M.Ramkumar Prabhu A.Prabhu Chakkaravarthy Department of Information Technology Sree Vidyanikethan Engineering College(Autonomous)TirupatiAndhra PradeshIndia Department of Electronics and Communication Engineering TJS Engineering CollegeKavaraipettaiChennaiTamil NaduIndia Department of Computer Science and Engineering Vidyavardhaka College of EngineeringMysuruKarnatakaIndia Department of Electronics and Communication Engineering Peri Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennaiTamil NaduIndia

Nowadays,Wireless Sensor Network(WSN)is a modern technology with a wide range of applications and greatly attractive benefits,for example,self-governing,low expenditure on execution and data communication,long-term function,and unsupervised access to the *** Internet of Things(IoT)is an attractive,exciting *** applying communication technologies in sensors and supervising features,WSNs have initiated communication between the IoT *** IoT offers access to the highest amount of information collected through WSNs,it leads to privacy management ***,this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique(LRECC)to establish a secure IoT structure for preventing,detecting,and mitigating *** approach uses the Elliptical Curve Cryptography(ECC)algorithm to generate and distribute security *** algorithm is a light weight key;thus,it minimizes the routing ***,the Logistic Regression machine learning technique selects the transmitter based on intelligent *** main application of this approach is smart *** approach provides continuing reliable routing paths with small *** addition,route nodes cooperate with IoT,and it handles the resources proficiently and minimizes the 29.95%delay.

关键词： Wireless sensor network internet of things security elliptical curve cryptography machine learning regression analysis

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Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

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Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

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Enhancing Security: A Hybrid Encryption Algorithm for Effective Key Generation 2

Enhancing Security: A Hybrid Encryption Algorithm for Effect...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Jacob, Jeevan John Kiran, P.S. Shenoy, Jaigovind B Basavaraju, Sagar Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350366570

The abstract Data security is of most importance, in the present day- the introduction of high-level wireless communication channels has led to generation and transfer of massive amounts of data. Data security must keep up with it. Data breach may cause disastrous outcomes. Previously, hardware security modules (HSM) were used to perform cryptographic actions- storing keys and for authentication. This is no longer feasible as HSMs are physical devices and become inconvenient to carry around all the time. The unavailability of HSM poses a serious security threat (for cryptographic keys). This paper aims at a novel software solution that compensates for hardware. As the algorithm focuses on securing keys used for encryption. It may be referred to as key security scheme (KSS). The paper proposes a way to generate keys instead of storing it and assumes performing of cryptographic actions on the available hardware in the system. The proposed method can secure data, both in transit and while at rest. © 2024 IEEE.

关键词： Encryption algorithms

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Leveraging U-Net Variants for High-Precision Flood Mapping: A Performance Evaluation on the ETCI Dataset 2

Leveraging U-Net Variants for High-Precision Flood Mapping: ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

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