Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908759
Atharva Pawar, Chirag Fatnani, Rajani Sonavane, Riya Waghmare, Sarang A. Saoji
Q-R codes are utilised for a variety of purposes, including accessing online web-pages and making a settlement. The Internet facilitates a wide range of illegal acts, including unsolicited e-marketing, financial embezzlement, and malicious distribution. Even though all the users identify the presence of Q-R codes visually, the information stored in those codes can only be accessed through an allocated Q-R code decoder. Q-R codes have also been shown to be used as an effective attack vector, For example techniques include social engineering, phishing, pharming, etc. Harmful codes are distributed under false pretences in congested areas, or malicious Q-R codes are pasted over current ones on billboards. Finally, consumers rely on decoder operating system to determine a random Q-R code is whether malicious or benign.For the purpose of this report, we consider the identification of malicious Q-R codes as a two-way classification problem in this research, and we test the effectiveness of many well-known M-L algorithms, including namely K-Nearest Neighbour, Random Forest, Binary LSTM and Support Vector Machine. This implies that the proposed method might be deemed an optimal and user-friendly QR code security solution. We created a prototype to test our recommendations and found it to be secure and usable in protecting users from harmful QR Codes.
{"title":"Secure QR Code Scanner to Detect Malicious URL using Machine Learning","authors":"Atharva Pawar, Chirag Fatnani, Rajani Sonavane, Riya Waghmare, Sarang A. Saoji","doi":"10.1109/ASIANCON55314.2022.9908759","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908759","url":null,"abstract":"Q-R codes are utilised for a variety of purposes, including accessing online web-pages and making a settlement. The Internet facilitates a wide range of illegal acts, including unsolicited e-marketing, financial embezzlement, and malicious distribution. Even though all the users identify the presence of Q-R codes visually, the information stored in those codes can only be accessed through an allocated Q-R code decoder. Q-R codes have also been shown to be used as an effective attack vector, For example techniques include social engineering, phishing, pharming, etc. Harmful codes are distributed under false pretences in congested areas, or malicious Q-R codes are pasted over current ones on billboards. Finally, consumers rely on decoder operating system to determine a random Q-R code is whether malicious or benign.For the purpose of this report, we consider the identification of malicious Q-R codes as a two-way classification problem in this research, and we test the effectiveness of many well-known M-L algorithms, including namely K-Nearest Neighbour, Random Forest, Binary LSTM and Support Vector Machine. This implies that the proposed method might be deemed an optimal and user-friendly QR code security solution. We created a prototype to test our recommendations and found it to be secure and usable in protecting users from harmful QR Codes.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106444","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909267
Dinisuru Nisal Gunaratna, Pumudu Fernando
Endoscopy is the most widely adhered medical procedure used to examine the gastrointestinal tract of a person. Accurate pathology detection during the endoscopic procedure is crucial as misidentifications or miss rates could reduce the chance of survival for the patient. After the successful collaboration of artificial intelligence with medicine, researchers around the world have tried different techniques in using this for gastroenterology. Our study demonstrates an extensive survey on existing pathology detection methodologies in endoscopic images using the publicly available datasets. The paper also discusses the content of the recently released datasets, preprocessing techniques tried on these datasets and how they affected the performance of the machine learning models. Furthermore, this study discusses how changing architectures of convolutional neural networks could affect the accuracy of models in relation to different datasets. Finally, the paper presents the results of each reviewed literature along with a brief discussion on the gaps that were identified.
{"title":"A Systematic Literature Review of Machine Learning based Approaches on Pathology Detection in Gastrointestinal Endoscopy","authors":"Dinisuru Nisal Gunaratna, Pumudu Fernando","doi":"10.1109/ASIANCON55314.2022.9909267","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909267","url":null,"abstract":"Endoscopy is the most widely adhered medical procedure used to examine the gastrointestinal tract of a person. Accurate pathology detection during the endoscopic procedure is crucial as misidentifications or miss rates could reduce the chance of survival for the patient. After the successful collaboration of artificial intelligence with medicine, researchers around the world have tried different techniques in using this for gastroenterology. Our study demonstrates an extensive survey on existing pathology detection methodologies in endoscopic images using the publicly available datasets. The paper also discusses the content of the recently released datasets, preprocessing techniques tried on these datasets and how they affected the performance of the machine learning models. Furthermore, this study discusses how changing architectures of convolutional neural networks could affect the accuracy of models in relation to different datasets. Finally, the paper presents the results of each reviewed literature along with a brief discussion on the gaps that were identified.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123868204","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}
Missing values in a dataset has always been a problem for data analysis and modelling. Building a model over a dataset where the missing values are not handled properly will definitely degrade the accuracy and performance of model. This problem particularly impacts deterministic models. Knowing that majority of the models that are used today are deterministic makes dealing with missing values crucial before applying the machine learning model. In this paper we have discussed various approaches such as statistical method (using mean), MICE and KNN for imputing missing values and tested their accuracy in combination with two prediction algorithms linear regression and random forest regression. We have used dataset of used cars containing missing values in few columns to predict the price of car given the details of car and thus comparing the accuracy of the estimated price with different approaches.
{"title":"Imputing missing values for Dataset of Used Cars","authors":"Samveg Shah, Mayur Telrandhe, Prathmesh Waghmode, Sunil Ghane","doi":"10.1109/ASIANCON55314.2022.9908600","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908600","url":null,"abstract":"Missing values in a dataset has always been a problem for data analysis and modelling. Building a model over a dataset where the missing values are not handled properly will definitely degrade the accuracy and performance of model. This problem particularly impacts deterministic models. Knowing that majority of the models that are used today are deterministic makes dealing with missing values crucial before applying the machine learning model. In this paper we have discussed various approaches such as statistical method (using mean), MICE and KNN for imputing missing values and tested their accuracy in combination with two prediction algorithms linear regression and random forest regression. We have used dataset of used cars containing missing values in few columns to predict the price of car given the details of car and thus comparing the accuracy of the estimated price with different approaches.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131160341","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909360
Mithra Venkatesan, A. Kulkarni, Radhika Menon, Shashikant Prasad
There is a drastic increase in the number of users who subscribe to the mobile broadband every year. On the other hand, 4G networks have reached the theoretical limits on the data rate and therefore it is not sufficient to accommodate the above increasing traffic. To overcome this problem, new Generation of mobile communication known as fifth generation (5G) comes into the picture. Large network capacity, ultra-low latency and heterogeneous device support are the important features in 5G Technology. Massive MIMO in 5G Technology is built on multi-tier architecture using several low power Base Stations (BSs) inside small cell. Simultaneous usage of the same spectrum causes interference which further reduces the system throughput and network capacity. Thus resource management is an integral part of 5G Heterogeneous Networks (HetNets) so that interference between several base stations and different devices can be minimized. Proposed scheme introduces feedback on the existing cell association and antenna allocation algorithms and also introduces the evolutionary game theory for interference mitigation in HetNets as Game theory can be efficiently modelled for a competitive and compatible environment. Impact of feedback and game theory into RATs on data rate experienced by users and revenue generated by base station from users respectively are observed. Feedback mechanism along with Game theory approach enables to make efficient and effective resource allocation decisions. This facilitates the existing Cell Association algorithms to maximize the data rate of users in different classes and the antenna allocation algorithm to maximize the total profit of the Base station. Both users and base stations are self-interested to maximize their own benefits in terms of data rate and revenue.
{"title":"Interference Mitigation Approach using Massive MIMO towards 5G networks","authors":"Mithra Venkatesan, A. Kulkarni, Radhika Menon, Shashikant Prasad","doi":"10.1109/ASIANCON55314.2022.9909360","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909360","url":null,"abstract":"There is a drastic increase in the number of users who subscribe to the mobile broadband every year. On the other hand, 4G networks have reached the theoretical limits on the data rate and therefore it is not sufficient to accommodate the above increasing traffic. To overcome this problem, new Generation of mobile communication known as fifth generation (5G) comes into the picture. Large network capacity, ultra-low latency and heterogeneous device support are the important features in 5G Technology. Massive MIMO in 5G Technology is built on multi-tier architecture using several low power Base Stations (BSs) inside small cell. Simultaneous usage of the same spectrum causes interference which further reduces the system throughput and network capacity. Thus resource management is an integral part of 5G Heterogeneous Networks (HetNets) so that interference between several base stations and different devices can be minimized. Proposed scheme introduces feedback on the existing cell association and antenna allocation algorithms and also introduces the evolutionary game theory for interference mitigation in HetNets as Game theory can be efficiently modelled for a competitive and compatible environment. Impact of feedback and game theory into RATs on data rate experienced by users and revenue generated by base station from users respectively are observed. Feedback mechanism along with Game theory approach enables to make efficient and effective resource allocation decisions. This facilitates the existing Cell Association algorithms to maximize the data rate of users in different classes and the antenna allocation algorithm to maximize the total profit of the Base station. Both users and base stations are self-interested to maximize their own benefits in terms of data rate and revenue.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129599432","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908688
S. N., Shruti Wagle, Priyanka Ghosh, Karishma Kishore
Finding music based on one’s mood is difficult unless it is manually classified and separated into distinct playlists. This is especially tough when the song is not in English due to varying lexical and syntactic styles. Our project employs textual sentiment analysis by testing various binary classifier algorithms - Random Forest, Naive Bayes, Support Vector Machine (SVM), and AdaBoost - to gauge which method is best for classifying English and Hindi language music lyrics into positive (happy) and negative (sad) sentiment.
{"title":"Sentiment Classification of English and Hindi Music Lyrics Using Supervised Machine Learning Algorithms","authors":"S. N., Shruti Wagle, Priyanka Ghosh, Karishma Kishore","doi":"10.1109/ASIANCON55314.2022.9908688","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908688","url":null,"abstract":"Finding music based on one’s mood is difficult unless it is manually classified and separated into distinct playlists. This is especially tough when the song is not in English due to varying lexical and syntactic styles. Our project employs textual sentiment analysis by testing various binary classifier algorithms - Random Forest, Naive Bayes, Support Vector Machine (SVM), and AdaBoost - to gauge which method is best for classifying English and Hindi language music lyrics into positive (happy) and negative (sad) sentiment.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131178107","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908712
Keerthana P.B., Joseph K.D.
Recently, wind energy generation grows quickly because of its economical features and it has less effect on mother earth. The long-distance between generation and customer reduces the maximum transmittable power. For addressing this issue series compensation is broadly used to raise the capacity of transmission. But the insertion of capacitors has the hazardous issue of Sub Synchronous Resonance (SSR). An Enhanced Detection Technique(EDT) is used in Double Fed Induction Generator (DFIG) based wind power system connected to the series compensated line(SCL) for fast detection of SSR. Comparison of enhanced detection technique with the traditional technique validate the superiority of EDT. SSR Damping Controller (SSRDC) in the static synchronous compensator (STATCOM) is applied for mitigation of hazardous effect of SSR. The voltage signal is the input for the detection circuit and line current signal is the input for SSRDC.
{"title":"Enhanced Detection and Mitigation on Sub Synchronous Resonance in Wind Farm with Series Compensated Line","authors":"Keerthana P.B., Joseph K.D.","doi":"10.1109/ASIANCON55314.2022.9908712","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908712","url":null,"abstract":"Recently, wind energy generation grows quickly because of its economical features and it has less effect on mother earth. The long-distance between generation and customer reduces the maximum transmittable power. For addressing this issue series compensation is broadly used to raise the capacity of transmission. But the insertion of capacitors has the hazardous issue of Sub Synchronous Resonance (SSR). An Enhanced Detection Technique(EDT) is used in Double Fed Induction Generator (DFIG) based wind power system connected to the series compensated line(SCL) for fast detection of SSR. Comparison of enhanced detection technique with the traditional technique validate the superiority of EDT. SSR Damping Controller (SSRDC) in the static synchronous compensator (STATCOM) is applied for mitigation of hazardous effect of SSR. The voltage signal is the input for the detection circuit and line current signal is the input for SSRDC.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128735589","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909083
Avani Sharma, Sumit Dhariwal
With the advent of time series prediction in multidisciplinary domains, Multivariate Chaotic Time Series (MCTS) prediction has become a popular topic of re-search. Manifold applications like weather forecasting, stocks prediction, medical support, etc., deploy such kind prediction approach to predict the future of the time series based on past observations. In literature, various solutions have been explored and proposed to forecast future values in time series data. Significant efforts have been made to utilize various Neural Networks for time series prediction considering their applicability for future data prediction. However, a comprehensive evaluation of such existing methods is missing which demands attention for accurate and efficient prediction of time series data. In this paper, we have applied and evaluated various deep learning techniques on different dynamically generated data sets. Further, a comprehensive comparison of different techniques have been presented referencing loss observed with performance matrix Mean Absolute Error.
{"title":"Analysis of Multivariate Chaotic Time Series using Neural Networks","authors":"Avani Sharma, Sumit Dhariwal","doi":"10.1109/ASIANCON55314.2022.9909083","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909083","url":null,"abstract":"With the advent of time series prediction in multidisciplinary domains, Multivariate Chaotic Time Series (MCTS) prediction has become a popular topic of re-search. Manifold applications like weather forecasting, stocks prediction, medical support, etc., deploy such kind prediction approach to predict the future of the time series based on past observations. In literature, various solutions have been explored and proposed to forecast future values in time series data. Significant efforts have been made to utilize various Neural Networks for time series prediction considering their applicability for future data prediction. However, a comprehensive evaluation of such existing methods is missing which demands attention for accurate and efficient prediction of time series data. In this paper, we have applied and evaluated various deep learning techniques on different dynamically generated data sets. Further, a comprehensive comparison of different techniques have been presented referencing loss observed with performance matrix Mean Absolute Error.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774102","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908931
G. P, Sanjay Kumar, Jambi Ratna Raja Kumar, Saju Raj T
Wireless Sensor Networks (WSNs) is main IoT module that gathers data from the environment and sends it to the destinations. The IOT may contain a broad variety of devices. Interconnecting several singly operating IoT devices via internet presents various issues, security that is remains a major concern given the large and frequently unknown audience. For resource-constrained nodes, most known techniques are very recursive. The sensor node’s resource will be severely shortened, compromising communication and security. However, the opponents' behaviour in WSNs has never been studied. The IoT network and its applications need a sophisticated security architecture to protect both gateway and sensor nodes from attacks. A secure communication system is the major goal of this work.
{"title":"Design of Secure Communication Methodologies for WSN Assisted IoT Applications","authors":"G. P, Sanjay Kumar, Jambi Ratna Raja Kumar, Saju Raj T","doi":"10.1109/ASIANCON55314.2022.9908931","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908931","url":null,"abstract":"Wireless Sensor Networks (WSNs) is main IoT module that gathers data from the environment and sends it to the destinations. The IOT may contain a broad variety of devices. Interconnecting several singly operating IoT devices via internet presents various issues, security that is remains a major concern given the large and frequently unknown audience. For resource-constrained nodes, most known techniques are very recursive. The sensor node’s resource will be severely shortened, compromising communication and security. However, the opponents' behaviour in WSNs has never been studied. The IoT network and its applications need a sophisticated security architecture to protect both gateway and sensor nodes from attacks. A secure communication system is the major goal of this work.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128504618","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909153
Shivam Wadhwa, Shailesh Mishra
In this paper, various real time indoor scenarios have been analyzed to provide the on-site practical solutions for efficient communication link establishment for futuristic 5G indoor application. The presented real time indoor scenario contains a transmitting and a receiving antenna with resonant frequency of 60GHz with 4.39 GHz bandwidth. The user equipment (UE) is considered as a receiving antenna which is placed at various coordinates in the room and a 5G transmitting antenna is placed at different positions in the room. The result analysis is carried out to find the best orientation of transmitting antenna such that it gives maximum power at the receiver antenna placed at different positions in the room. The orientation of the transmitting antenna can be implemented electronically using beamforming technique to established the efficient link.
{"title":"Investigation of On-site Channel Model for 5G Indoor Applications","authors":"Shivam Wadhwa, Shailesh Mishra","doi":"10.1109/ASIANCON55314.2022.9909153","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909153","url":null,"abstract":"In this paper, various real time indoor scenarios have been analyzed to provide the on-site practical solutions for efficient communication link establishment for futuristic 5G indoor application. The presented real time indoor scenario contains a transmitting and a receiving antenna with resonant frequency of 60GHz with 4.39 GHz bandwidth. The user equipment (UE) is considered as a receiving antenna which is placed at various coordinates in the room and a 5G transmitting antenna is placed at different positions in the room. The result analysis is carried out to find the best orientation of transmitting antenna such that it gives maximum power at the receiver antenna placed at different positions in the room. The orientation of the transmitting antenna can be implemented electronically using beamforming technique to established the efficient link.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125568849","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908676
Jayama Pinnamaneni, N. S, Prasad B. Honnavalli
A Docker container image can be defined as a lightweight, unattached, executable package of software that includes everything like code, runtime, system tools, system libraries and settings, needed to run an application, because of these features the container images are preferred over virtual machines. With this enormous usage, there is a lot of scope for the security issues arising in the container images. There are many open-source projects like Anchore, Clair that statically scan the container image’s docker file to find the vulnerabilities using databases like CVE, RedHat etc. Static analysis of container image main code is equally necessary to identify any vulnerabilities in the code and not only focus on the vulnerabilities based on OS level, as many malicious activities might take place if code is not scanned for any vulnerabilities. The main aim of the project is to create a static code analysing machine learning model to identify the vulnerable python libraries in container images.
{"title":"Identifying Vulnerabilities in Docker Image Code using ML Techniques","authors":"Jayama Pinnamaneni, N. S, Prasad B. Honnavalli","doi":"10.1109/ASIANCON55314.2022.9908676","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908676","url":null,"abstract":"A Docker container image can be defined as a lightweight, unattached, executable package of software that includes everything like code, runtime, system tools, system libraries and settings, needed to run an application, because of these features the container images are preferred over virtual machines. With this enormous usage, there is a lot of scope for the security issues arising in the container images. There are many open-source projects like Anchore, Clair that statically scan the container image’s docker file to find the vulnerabilities using databases like CVE, RedHat etc. Static analysis of container image main code is equally necessary to identify any vulnerabilities in the code and not only focus on the vulnerabilities based on OS level, as many malicious activities might take place if code is not scanned for any vulnerabilities. The main aim of the project is to create a static code analysing machine learning model to identify the vulnerable python libraries in container images.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127010420","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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