Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716598
N. Telagam, S. Lakshmi, K. Nehru
In this paper, we propose a peak to average power ratio (PAPR) reduction scheme for generalised frequency-division multiplexing (GFDM) systems based on parallel concatenation of low-density parity-check codes (PC-LDPC) codes. The proposed scheme maps the PC-LDPC codewords onto subcarriers to construct a symbol of channel coded GFDM. Then, these sub symbols are combined with subcarriers to form symbols, and these symbols are applied to the PAPR expression of the GFDM system for calculation. The BER value is higher at 10dB for the RRC filter-based GFDM system than the RC filter. The RC filter configuration has less BER at 10dB in 0.2 roll-off factor value with 100 iterations and 200 iterations in soft decision algorithm. When the value of Complementary Cumulative Distribution Function (CCDF) =0.001, the PC-LDPC GFDM system reduces the PAPR by 4 to 4.5 dB compared to the uncoded GFDM signal. The coding gain of 0.5dB is observed in Raised cosine pulse shaping filter with PC-LDPC codes.
{"title":"PAPR reduction of GFDM system using Parallel concatenation of LDPC codes","authors":"N. Telagam, S. Lakshmi, K. Nehru","doi":"10.1109/ICAECC54045.2022.9716598","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716598","url":null,"abstract":"In this paper, we propose a peak to average power ratio (PAPR) reduction scheme for generalised frequency-division multiplexing (GFDM) systems based on parallel concatenation of low-density parity-check codes (PC-LDPC) codes. The proposed scheme maps the PC-LDPC codewords onto subcarriers to construct a symbol of channel coded GFDM. Then, these sub symbols are combined with subcarriers to form symbols, and these symbols are applied to the PAPR expression of the GFDM system for calculation. The BER value is higher at 10dB for the RRC filter-based GFDM system than the RC filter. The RC filter configuration has less BER at 10dB in 0.2 roll-off factor value with 100 iterations and 200 iterations in soft decision algorithm. When the value of Complementary Cumulative Distribution Function (CCDF) =0.001, the PC-LDPC GFDM system reduces the PAPR by 4 to 4.5 dB compared to the uncoded GFDM signal. The coding gain of 0.5dB is observed in Raised cosine pulse shaping filter with PC-LDPC codes.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716625
K. Krupa, Sukumar Patil, Bhoopendra Singh
The objective of Design and Manufacturing companies is to increase customer satisfaction, productivity with good quality of products. At present many of the design and manufacturing companies are facing quality rejection, lead time issue and inability to meet the customer expectations. By implementing the lean manufacturing system, many problems can be solved by involving employees on the shop floor in Kaizen activities. One of the basic rules of Kaizen is “The continuous incremental improvement of an activity to create more value with less waste giving quantifiable and sustainable benefit”. The main objective of this paper is to provide background of Kaizen implementation in design and manufacturing areas.
{"title":"Importance of Kaizen and Its Implementation in Design and Manufacturing System","authors":"K. Krupa, Sukumar Patil, Bhoopendra Singh","doi":"10.1109/ICAECC54045.2022.9716625","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716625","url":null,"abstract":"The objective of Design and Manufacturing companies is to increase customer satisfaction, productivity with good quality of products. At present many of the design and manufacturing companies are facing quality rejection, lead time issue and inability to meet the customer expectations. By implementing the lean manufacturing system, many problems can be solved by involving employees on the shop floor in Kaizen activities. One of the basic rules of Kaizen is “The continuous incremental improvement of an activity to create more value with less waste giving quantifiable and sustainable benefit”. The main objective of this paper is to provide background of Kaizen implementation in design and manufacturing areas.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716643
Deekshith Krishnegowda
The first neural network model which was developed for image recognition application consisted of simple perceptrons. It had input, processing unit, and a single output. Neural networks which are used in today’s world consist of many complex MAC (Multiply and Accumulate) units. Be it the simple pattern recognition neural network model or complex models used for autonomous driving applications; adders are used for computing the activation point of neurons. Some adders offer better performance at the cost of area and power while some offer better power at the cost of performance. So, choosing the right type of adder architecture based upon the application becomes a very important criterion when we are trying to develop an inference engine for the neural network in hardware. To determine weight or activation point of a neuron, typically, float32 or float64 number representation is used. Float64 offers better accuracy than float32 but the drawback of using float64 is that it requires huge computation power. So, in this manuscript we compare different high-speed adder topologies, then discuss the implementation of an optimized 64-bit conditional sum and carry select adder that can be used to implement Deep Neural Network with float64 number representation. Analysis between different adder architecture is performed using Synopsys Design Compiler with 45nm Toshiba library for three different metrics: Timing, Area, and Power.
{"title":"Analyzing different high speed adder architecture for Neural Networks","authors":"Deekshith Krishnegowda","doi":"10.1109/ICAECC54045.2022.9716643","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716643","url":null,"abstract":"The first neural network model which was developed for image recognition application consisted of simple perceptrons. It had input, processing unit, and a single output. Neural networks which are used in today’s world consist of many complex MAC (Multiply and Accumulate) units. Be it the simple pattern recognition neural network model or complex models used for autonomous driving applications; adders are used for computing the activation point of neurons. Some adders offer better performance at the cost of area and power while some offer better power at the cost of performance. So, choosing the right type of adder architecture based upon the application becomes a very important criterion when we are trying to develop an inference engine for the neural network in hardware. To determine weight or activation point of a neuron, typically, float32 or float64 number representation is used. Float64 offers better accuracy than float32 but the drawback of using float64 is that it requires huge computation power. So, in this manuscript we compare different high-speed adder topologies, then discuss the implementation of an optimized 64-bit conditional sum and carry select adder that can be used to implement Deep Neural Network with float64 number representation. Analysis between different adder architecture is performed using Synopsys Design Compiler with 45nm Toshiba library for three different metrics: Timing, Area, and Power.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127425203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Often times there is scarcity when it comes to model training of a quality dataset. Sometimes the data that is available is unlabelled, sometimes very few samples are available for some classes. In these cases, few shot learning comes in handy. There are two approaches to few shot learning Data Level approach and Parameter Level approach. The paper consists of analysis of the number of training samples using parameter level approach. Two classes have been used to perform few shot learning. Meta transfer learning is being used, by initialising the parameters of convolutional neutral networks (CNN) learner model from a model trained on ImageNet. It has been performed incrementally on datasets of various sizes. The results and performance of all the models are compared to the results when the entire dataset is used. As well as the advantages of using few shot learning. It has found its applications in a wide range of fields mainly computer vision, natural language processing etc.
{"title":"A Proposed Algorithm to Perform Few Shot Learning with different sampling sizes","authors":"Kashvi Dedhia, Mallika Konkar, Dhruvil Shah, Prachi Tawde","doi":"10.1109/ICAECC54045.2022.9716609","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716609","url":null,"abstract":"Often times there is scarcity when it comes to model training of a quality dataset. Sometimes the data that is available is unlabelled, sometimes very few samples are available for some classes. In these cases, few shot learning comes in handy. There are two approaches to few shot learning Data Level approach and Parameter Level approach. The paper consists of analysis of the number of training samples using parameter level approach. Two classes have been used to perform few shot learning. Meta transfer learning is being used, by initialising the parameters of convolutional neutral networks (CNN) learner model from a model trained on ImageNet. It has been performed incrementally on datasets of various sizes. The results and performance of all the models are compared to the results when the entire dataset is used. As well as the advantages of using few shot learning. It has found its applications in a wide range of fields mainly computer vision, natural language processing etc.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115045243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716676
Ba Ajeethra, Sv Gautham Prasath, R. Arun Balaji, K. A. Kumar
Digital communication and networking had become an integral part of our everyday life. Technological advancements in Digital networking must also include security and confidentiality paradigms. Several previous works on communication systems comprised the problem of storage, sharing, and complexity of keys. On considering the mentioned problems of existing works, this paper proposes secure communication using cryptography and face recognition techniques with cloud computing. The proposed system pertains to a protected communication process, where messages are entitled only after the verification of the authorized sender and receiver using Linear Binary Pattern Histogram (LBPH) face recognition, and Rivest, Shamir, Adleman (RSA) cryptographic technique with the cloud management system. The system generates RSA key pair, which is exported as a Privacy- Enhanced Mail (PEM) file and stored in a remote server through a Secure Shell (SSH) tunnel.The proposed system has found that using 50 samples for face authentication is most efficient and accurate with limited time. Existing works have focused to increase security by adding layers of encryption which in turn increased the complexity to handle keys and decryption processes. This proposed methodology on following a biometric authentication system, stretches itself with an extra efficient layer of security without increasing the complexity of the system.
{"title":"A Cryptography based Face Authentication System for Secured Communication","authors":"Ba Ajeethra, Sv Gautham Prasath, R. Arun Balaji, K. A. Kumar","doi":"10.1109/ICAECC54045.2022.9716676","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716676","url":null,"abstract":"Digital communication and networking had become an integral part of our everyday life. Technological advancements in Digital networking must also include security and confidentiality paradigms. Several previous works on communication systems comprised the problem of storage, sharing, and complexity of keys. On considering the mentioned problems of existing works, this paper proposes secure communication using cryptography and face recognition techniques with cloud computing. The proposed system pertains to a protected communication process, where messages are entitled only after the verification of the authorized sender and receiver using Linear Binary Pattern Histogram (LBPH) face recognition, and Rivest, Shamir, Adleman (RSA) cryptographic technique with the cloud management system. The system generates RSA key pair, which is exported as a Privacy- Enhanced Mail (PEM) file and stored in a remote server through a Secure Shell (SSH) tunnel.The proposed system has found that using 50 samples for face authentication is most efficient and accurate with limited time. Existing works have focused to increase security by adding layers of encryption which in turn increased the complexity to handle keys and decryption processes. This proposed methodology on following a biometric authentication system, stretches itself with an extra efficient layer of security without increasing the complexity of the system.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716664
B. Bairwa, K. Pareek, Mrinal Sarvagya, U. Yaragatti
In this paper, we evaluated the leakage current of super capacitor during self-discharge. A three branch electrical equivalent circuit model (ECM) is constructed to estimate voltage response and leakage current of commercially available 2.7 V 350F (BCAP350) double-layer capacitor (DLC). Experimental work carried out with two constant current charging segments 0.25 ampere, and 0.5 amperes. Simulated data shows good agreement with experimental results obtained at electro-chemical workstation CH760e with RMSE and MAE error up to 0.0633, 0.05715, and 0.0759, 0.04173 for 0.25 ampere and 0.5 amperes charging current, respectively. The results confirm that the ECM model is capable to simulate the complex terminal behavior of the super capacitor and provides the means to study its application as an energy storage device.
本文对超级电容器自放电时的漏电流进行了计算。建立了三支路等效电路模型(ECM)，对市售2.7 V 350F (BCAP350)双层电容器(DLC)的电压响应和漏电流进行了估计。实验工作采用0.25安培和0.5安培两个恒流充电段进行。仿真数据与电化学工作站CH760e的实验结果吻合较好，在0.25安培和0.5安培充电电流下，RMSE和MAE误差分别为0.0633、0.05715和0.0759、0.04173。结果证实了ECM模型能够模拟超级电容器的复杂终端行为，为研究其作为储能器件的应用提供了手段。
{"title":"Analysis of Leakage Current Mechanism in Supercapacitor with Experimental Approach","authors":"B. Bairwa, K. Pareek, Mrinal Sarvagya, U. Yaragatti","doi":"10.1109/ICAECC54045.2022.9716664","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716664","url":null,"abstract":"In this paper, we evaluated the leakage current of super capacitor during self-discharge. A three branch electrical equivalent circuit model (ECM) is constructed to estimate voltage response and leakage current of commercially available 2.7 V 350F (BCAP350) double-layer capacitor (DLC). Experimental work carried out with two constant current charging segments 0.25 ampere, and 0.5 amperes. Simulated data shows good agreement with experimental results obtained at electro-chemical workstation CH760e with RMSE and MAE error up to 0.0633, 0.05715, and 0.0759, 0.04173 for 0.25 ampere and 0.5 amperes charging current, respectively. The results confirm that the ECM model is capable to simulate the complex terminal behavior of the super capacitor and provides the means to study its application as an energy storage device.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716611
G. R. K. Dora, R. Biradar, M. Prakruthi
New age wireless applications demand sharp rejection of unwanted signals at MHz frequencies. This necessitates the design of low loss, high attenuation bandpass filters. This paper proposes a design of compact 0.5 dB Chebyshev interdigital bandpass filter (IDBPF) which operates at a frequency of F0=875MHz used for space applications. The IDBPF is designed for an order n=9. Advanced Design System (ADS) software is used to design the filter. The designed filter is fabricated on a RO4350B substrate which has a relative dielectric constant $varepsilon_{r}$ of 3.66. Filter has pass bandwidth of $F0 pm 125MHz$, and insertion loss of $5 pm 3dB$ at 875MHz with steep rejection of 40dB min at 1050MHz and 25dB min at 650MHz. In this design, rejection is considered at prime importance than the insertion loss.
{"title":"Design and development of Interdigital Band pass filter for L-Band Wireless Communication Applications","authors":"G. R. K. Dora, R. Biradar, M. Prakruthi","doi":"10.1109/ICAECC54045.2022.9716611","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716611","url":null,"abstract":"New age wireless applications demand sharp rejection of unwanted signals at MHz frequencies. This necessitates the design of low loss, high attenuation bandpass filters. This paper proposes a design of compact 0.5 dB Chebyshev interdigital bandpass filter (IDBPF) which operates at a frequency of F0=875MHz used for space applications. The IDBPF is designed for an order n=9. Advanced Design System (ADS) software is used to design the filter. The designed filter is fabricated on a RO4350B substrate which has a relative dielectric constant $varepsilon_{r}$ of 3.66. Filter has pass bandwidth of $F0 pm 125MHz$, and insertion loss of $5 pm 3dB$ at 875MHz with steep rejection of 40dB min at 1050MHz and 25dB min at 650MHz. In this design, rejection is considered at prime importance than the insertion loss.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716585
S. Thavamani, U. Sinthuja
Internet of Things networks are becoming more popular for monitoring critical environments of various types, resulting in a large increase in the amount of data transmitted. Because of the large number of linked IoT devices, network and security protocols is a major concern. In the sphere of security, detection systems play a critical role: they are based on cutting-edge algorithms. They can recognize or forecast security attacks using techniques such as machine learning, allowing them to secure the underpinning system. We have depicted some of the Deep Learning based techniques and figured out the best technique called Long Short Term Memory (LSTM) with 87% of accuracy to build the Artificial Intelligence based Interpolation Technique for IoT Environment.
{"title":"LSTM based Deep Learning Technique to Forecast Internet of Things Attacks in MQTT Protocol","authors":"S. Thavamani, U. Sinthuja","doi":"10.1109/ICAECC54045.2022.9716585","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716585","url":null,"abstract":"Internet of Things networks are becoming more popular for monitoring critical environments of various types, resulting in a large increase in the amount of data transmitted. Because of the large number of linked IoT devices, network and security protocols is a major concern. In the sphere of security, detection systems play a critical role: they are based on cutting-edge algorithms. They can recognize or forecast security attacks using techniques such as machine learning, allowing them to secure the underpinning system. We have depicted some of the Deep Learning based techniques and figured out the best technique called Long Short Term Memory (LSTM) with 87% of accuracy to build the Artificial Intelligence based Interpolation Technique for IoT Environment.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716703
M. K. Khaishagi, Praful Kumar, D. Naik
RAFT is a deep network architecture for the detection of optical flow in the images. The RAFT model relates the per pixel motion between images even for minor changes in the position of the objects. It also updates the flow of field through recurrent units that perform lookups on the performance of the model. RAFT also works well with different datatypes and also it has better efficiency, training speed and count of parameters. Experiments were performed by using different parameters and also by changing certain values in the model itself. One cycle learning was also used to find the best parameters for the model. We also found that the RAFT model performs better than most of the other existing models for optical flow calculation in to images.
{"title":"Dense Optical Flow using RAFT","authors":"M. K. Khaishagi, Praful Kumar, D. Naik","doi":"10.1109/ICAECC54045.2022.9716703","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716703","url":null,"abstract":"RAFT is a deep network architecture for the detection of optical flow in the images. The RAFT model relates the per pixel motion between images even for minor changes in the position of the objects. It also updates the flow of field through recurrent units that perform lookups on the performance of the model. RAFT also works well with different datatypes and also it has better efficiency, training speed and count of parameters. Experiments were performed by using different parameters and also by changing certain values in the model itself. One cycle learning was also used to find the best parameters for the model. We also found that the RAFT model performs better than most of the other existing models for optical flow calculation in to images.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716622
A. Patil, Harivind Premkumar, Kiran M H M, Pranav Hegde
With the current advances in networking and the usage of computer networks in different sectors of technology, network security plays a prime role in enabling the proper functioning of networks by detecting and preventing attacks. In this paper, we propose an architecture using the Snort IDS/IPS and machine learning to build an Intelligent Network Intrusion Detection and Prevention System with dynamic rule updation creating robust and secure system with reduced resource consumption which can be used in Domestic Networks. The objective of JARVIS, the proposed system, is to detect malicious patterns in real-time traffic data and take action by dynamically updating Snort rules. By deploying a machine learning model (Random Forest) in parallel and dynamically enabling rules, resource consumption of Snort can be reduced and optimized. The model detects any attacks and suggests rules that can be deployed on Snort to prevent the attack. The false-positive rate of the model was reduced by looking at DNS queries to analyze the intent behind the traffic data. JARVIS also provides a web interface where the User can view Network Traffic Data, Detected Attacks as well as take the necessary actions. The machine learning model successfully detected incoming attacks with considerable accuracy and suggested rules in the web interface which allowed the user to deploy them and prevent the attack from causing further damage.
{"title":"JARVIS: An Intelligent Network Intrusion Detection and Prevention System","authors":"A. Patil, Harivind Premkumar, Kiran M H M, Pranav Hegde","doi":"10.1109/ICAECC54045.2022.9716622","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716622","url":null,"abstract":"With the current advances in networking and the usage of computer networks in different sectors of technology, network security plays a prime role in enabling the proper functioning of networks by detecting and preventing attacks. In this paper, we propose an architecture using the Snort IDS/IPS and machine learning to build an Intelligent Network Intrusion Detection and Prevention System with dynamic rule updation creating robust and secure system with reduced resource consumption which can be used in Domestic Networks. The objective of JARVIS, the proposed system, is to detect malicious patterns in real-time traffic data and take action by dynamically updating Snort rules. By deploying a machine learning model (Random Forest) in parallel and dynamically enabling rules, resource consumption of Snort can be reduced and optimized. The model detects any attacks and suggests rules that can be deployed on Snort to prevent the attack. The false-positive rate of the model was reduced by looking at DNS queries to analyze the intent behind the traffic data. JARVIS also provides a web interface where the User can view Network Traffic Data, Detected Attacks as well as take the necessary actions. The machine learning model successfully detected incoming attacks with considerable accuracy and suggested rules in the web interface which allowed the user to deploy them and prevent the attack from causing further damage.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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