Pub Date : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055957
Alamgir Naushad, G. Abbas, Shehzad Ali Shah, Z. Abbas
Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with minimum hop count. Load-balancing of data traffic over multiple paths is achieved for better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure-recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio, when compared with eminent WSN routing protocols.
{"title":"Energy Efficient Clustering with Reliable and Load-Balanced Multipath Routing for WSNs","authors":"Alamgir Naushad, G. Abbas, Shehzad Ali Shah, Z. Abbas","doi":"10.1109/ICACS47775.2020.9055957","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055957","url":null,"abstract":"Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with minimum hop count. Load-balancing of data traffic over multiple paths is achieved for better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure-recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio, when compared with eminent WSN routing protocols.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128637086","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055944
Sheikh Badar ud din Tahir, A. Jalal, Mouazma Batool
Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.
{"title":"Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets","authors":"Sheikh Badar ud din Tahir, A. Jalal, Mouazma Batool","doi":"10.1109/ICACS47775.2020.9055944","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055944","url":null,"abstract":"Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131150507","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055946
A. Rashid, M. Siddique, S. Ahmed
Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.
{"title":"Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System","authors":"A. Rashid, M. Siddique, S. Ahmed","doi":"10.1109/ICACS47775.2020.9055946","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055946","url":null,"abstract":"Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957741","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055947
Hifsa Tanveer, Mian Muhammad Mubasher, S. W. Jaffry
This study aims to explore the effects of human panic factor on drivers' driving behavior. Most of the car following models focus on idealistic situations aiming for perfection, traffic psychology, however, suggests that emotions do play a significant role in drivers' behavior which in result effect their driving and decision making. Therefore, it is necessary to incorporate human factors in car following models for better realistic results in driving situations where external task demand increases (for example, poor weather conditions like fog, or making up to a meeting in time). Despite the fact that car following models have sublime appreciation in literature, none of them has focused on incorporating human panic factor in these models. Although some work is being done on understanding panic factor in drivers which helps us to understand their driving behaviors and effect on acceleration under panic situations, but this work is limited to statistical approach. This study is intended to fill this void by reviewing literature and making latest advancements by integrating human panic factor in Intelligent Driver Model (IDM). We attempted to integrate human panic factor in IDM, and simulation-based results verified our assumptions for the enhanced version of IDM. The enhanced version of model namely P-IDM models the acceleration behavior of drivers under panic condition, and reproduces acceleration as intended.
{"title":"Integrating Human Panic Factor in Intelligent Driver Model","authors":"Hifsa Tanveer, Mian Muhammad Mubasher, S. W. Jaffry","doi":"10.1109/ICACS47775.2020.9055947","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055947","url":null,"abstract":"This study aims to explore the effects of human panic factor on drivers' driving behavior. Most of the car following models focus on idealistic situations aiming for perfection, traffic psychology, however, suggests that emotions do play a significant role in drivers' behavior which in result effect their driving and decision making. Therefore, it is necessary to incorporate human factors in car following models for better realistic results in driving situations where external task demand increases (for example, poor weather conditions like fog, or making up to a meeting in time). Despite the fact that car following models have sublime appreciation in literature, none of them has focused on incorporating human panic factor in these models. Although some work is being done on understanding panic factor in drivers which helps us to understand their driving behaviors and effect on acceleration under panic situations, but this work is limited to statistical approach. This study is intended to fill this void by reviewing literature and making latest advancements by integrating human panic factor in Intelligent Driver Model (IDM). We attempted to integrate human panic factor in IDM, and simulation-based results verified our assumptions for the enhanced version of IDM. The enhanced version of model namely P-IDM models the acceleration behavior of drivers under panic condition, and reproduces acceleration as intended.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128216468","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055939
A. Tariq, R. A. Rehman
The future Internet paradigm Named Data Networking (NDN) and networking architecture like Software Defined Networking (SDN) and Internet of Things (IoT) has caught the attention of research community. NDN is a future internet standard that builds on the practices of current Internet standard TCP/IP. NDN replaces the nodes address with named data that enhance the availability of data. SDN is a good solution for the emerging technologies, efficient resource utilization and communication. Flexible and high-level programmable nature of the SDN caught the attention of the world towards it. IoT enable everything interconnected with other devices and the internet. In dense IoT scenarios, Broadcast storm problem occurs because of the broadcast nature of NDN. Same type of packets cause this problem, that increase the total number of requests and retransmissions. In-time and fast transmission of highly important packets is an issue in IoT. In this paper, we proposed a Controller based Broadcast Storm Avoidance Mechanism (CBAM)in NDN enabled IoT that reduces the broadcast problem with the help of controller. Our scheme used the efficient flow management of SDN controller to control the broadcast storm and efficient transmissions of packets. We also categorized the packet according to the importance and priority. A naming criteria decides the priority of the packet. CBAM outperforms GIF and traditional NDN Flooding in total number of Interests and content retrieval time.
{"title":"CBAM: A Controller based Broadcast Storm Avoidance Mechanism in SDN based NDN-IoTs","authors":"A. Tariq, R. A. Rehman","doi":"10.1109/ICACS47775.2020.9055939","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055939","url":null,"abstract":"The future Internet paradigm Named Data Networking (NDN) and networking architecture like Software Defined Networking (SDN) and Internet of Things (IoT) has caught the attention of research community. NDN is a future internet standard that builds on the practices of current Internet standard TCP/IP. NDN replaces the nodes address with named data that enhance the availability of data. SDN is a good solution for the emerging technologies, efficient resource utilization and communication. Flexible and high-level programmable nature of the SDN caught the attention of the world towards it. IoT enable everything interconnected with other devices and the internet. In dense IoT scenarios, Broadcast storm problem occurs because of the broadcast nature of NDN. Same type of packets cause this problem, that increase the total number of requests and retransmissions. In-time and fast transmission of highly important packets is an issue in IoT. In this paper, we proposed a Controller based Broadcast Storm Avoidance Mechanism (CBAM)in NDN enabled IoT that reduces the broadcast problem with the help of controller. Our scheme used the efficient flow management of SDN controller to control the broadcast storm and efficient transmissions of packets. We also categorized the packet according to the importance and priority. A naming criteria decides the priority of the packet. CBAM outperforms GIF and traditional NDN Flooding in total number of Interests and content retrieval time.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116720879","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055942
Humaira Aslam Chughtai, Z. Rana
Numerous models have been studied and presented in literature for classification of defect-prone source code files. Usually these models use static code metrics, process metrics, and change metrics as input and predict defect proneness of code. However, there has been limited use of people related metrics as input to the prediction models. Impact of using people related information should be studied for better classification of defect prone files in future releases of software projects. This study proposes the use of People Profile Metrics (PPM) of software development team members to improve the prediction of defect prone source code files. The experiment is performed on an open source project and the defect prone source code files have been classified. In addition, severity of defects has also been predicted. The PPM have been evaluated for three classifiers Decision Tree, Random Forest, and K-Nearest Neighbors using Weka. Significant improvement in classification of defect prone source code files, in terms of Precision, Recall and F-Measure has been achieved. The combination of existing static code metrics and the PPM will be tested for more projects and for unsupervised models.
{"title":"People Profile Metrics for Improved Classification of Defect Prone Files in Open Source Projects","authors":"Humaira Aslam Chughtai, Z. Rana","doi":"10.1109/ICACS47775.2020.9055942","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055942","url":null,"abstract":"Numerous models have been studied and presented in literature for classification of defect-prone source code files. Usually these models use static code metrics, process metrics, and change metrics as input and predict defect proneness of code. However, there has been limited use of people related metrics as input to the prediction models. Impact of using people related information should be studied for better classification of defect prone files in future releases of software projects. This study proposes the use of People Profile Metrics (PPM) of software development team members to improve the prediction of defect prone source code files. The experiment is performed on an open source project and the defect prone source code files have been classified. In addition, severity of defects has also been predicted. The PPM have been evaluated for three classifiers Decision Tree, Random Forest, and K-Nearest Neighbors using Weka. Significant improvement in classification of defect prone source code files, in terms of Precision, Recall and F-Measure has been achieved. The combination of existing static code metrics and the PPM will be tested for more projects and for unsupervised models.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131163378","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055949
Faizan Khursheeed, M. Sami-Ud-Din, Dr.Irshad Ahmed Sumra, M. Safder
The web of the IoT has been started but also had some serious issues related to its security. It doesn't matter to what level its vulnerability affects. It can be domestic or any enterprise level. Billions of devices are linked with the internet. It is the network of objects and sensors by which these devices exchange data with each other without human involvement. On a large scale, there is a struggle to protect data from leaking. The main purpose of this is to review the survey of the work done in security in the specific area. As a whole, this study is measuring the flaws in the security related to the Internet of Things. Design and Protocols are studied in this survey for securing the communications between so-called ‘things'. Open inspection problems and security application challenges in IoT security are still present. This study is aimed to be a helpful guidebook to learn the vulnerabilities and threats related to security in IoT and increases the security design.
{"title":"A Review of Security Machanism in internet of Things(IoT)","authors":"Faizan Khursheeed, M. Sami-Ud-Din, Dr.Irshad Ahmed Sumra, M. Safder","doi":"10.1109/ICACS47775.2020.9055949","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055949","url":null,"abstract":"The web of the IoT has been started but also had some serious issues related to its security. It doesn't matter to what level its vulnerability affects. It can be domestic or any enterprise level. Billions of devices are linked with the internet. It is the network of objects and sensors by which these devices exchange data with each other without human involvement. On a large scale, there is a struggle to protect data from leaking. The main purpose of this is to review the survey of the work done in security in the specific area. As a whole, this study is measuring the flaws in the security related to the Internet of Things. Design and Protocols are studied in this survey for securing the communications between so-called ‘things'. Open inspection problems and security application challenges in IoT security are still present. This study is aimed to be a helpful guidebook to learn the vulnerabilities and threats related to security in IoT and increases the security design.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629092","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055953
Muhammad Burhan, R. A. Rehman
In recent years, Named-Data Network (NDN) has been introduced and considered as an emerging movement for the architecture of future Internet. It depends on the data-centric communication approach to retrieve content objects from the network. In such a way, NDN is expected to assist several applications such as VANET, known as Vehicular Named-Data Network (VNDN). In VNDN, a vehicle broadcasts an Interest packet to retrieve required content object regardless end-to-end connection with other vehicles. This situation leads to the broadcast storm problem of Interest packets, where each vehicle have to broadcast same Interest packet within their transmission range in the network. Further, this situation may lead to the additional delay and wastage of network resources. Along, it degrades the performance of VANET applications. In this paper, a new strategy, named as Broadcast Storm Mitigation Strategy (BSMS) is proposed in order to mitigate the broadcast problem of the Interest packets in the network. Furthermore, vehicles travel at high and different speeds. Therefore, Data packets do not reach to the consumer vehicle by following the same path as Interest packet. However, the proposed scheme also provides a solution to tackle the problem of disconnect link. The proposed scheme is evaluated by use of simulations which demonstrates that BSMS provides better results as compared to traditional native VNDN implementation in terms of average number of Interest packets broadcast in the network and average time delay per Interest satisfied.
{"title":"BSMS: A Reliable Interest Forwarding Protocol for NDN based VANETs","authors":"Muhammad Burhan, R. A. Rehman","doi":"10.1109/ICACS47775.2020.9055953","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055953","url":null,"abstract":"In recent years, Named-Data Network (NDN) has been introduced and considered as an emerging movement for the architecture of future Internet. It depends on the data-centric communication approach to retrieve content objects from the network. In such a way, NDN is expected to assist several applications such as VANET, known as Vehicular Named-Data Network (VNDN). In VNDN, a vehicle broadcasts an Interest packet to retrieve required content object regardless end-to-end connection with other vehicles. This situation leads to the broadcast storm problem of Interest packets, where each vehicle have to broadcast same Interest packet within their transmission range in the network. Further, this situation may lead to the additional delay and wastage of network resources. Along, it degrades the performance of VANET applications. In this paper, a new strategy, named as Broadcast Storm Mitigation Strategy (BSMS) is proposed in order to mitigate the broadcast problem of the Interest packets in the network. Furthermore, vehicles travel at high and different speeds. Therefore, Data packets do not reach to the consumer vehicle by following the same path as Interest packet. However, the proposed scheme also provides a solution to tackle the problem of disconnect link. The proposed scheme is evaluated by use of simulations which demonstrates that BSMS provides better results as compared to traditional native VNDN implementation in terms of average number of Interest packets broadcast in the network and average time delay per Interest satisfied.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124658845","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055943
M. Adil, Rahim Khan, M. Ghani
Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.
{"title":"Preventive Techniques of Phishing Attacks in Networks","authors":"M. Adil, Rahim Khan, M. Ghani","doi":"10.1109/ICACS47775.2020.9055943","DOIUrl":"https://doi.org/10.1109/ICACS47775.2020.9055943","url":null,"abstract":"Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.","PeriodicalId":268675,"journal":{"name":"2020 3rd International Conference on Advancements in Computational Sciences (ICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998998","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 : 2020-02-01DOI: 10.1109/ICACS47775.2020.9055955
Shahzad Nazir, M. Asif, Shahbaz Ahmad
In this era, to find out relevant research articles is considered an important task to track the state-of-the-art-work, and it is termed as research paper recommender system. Considering the massive increase in research corpora, the research community has turned its focus towards finding the most relevant research papers. Researchers have adopted different techniques that are bibliographic information based, content-based, and collaborative filtering based. The most common approach for the research paper recommender system is content-based. According to a survey, 55% of research paper recommender systems use a content-based approach. On the other hand, due to the unavailability of the full text of research papers, researchers started utilizing the Meta-data. But it is still unclear that what proportion of full content can be represented by the Meta-data. This research explored the significant portion of the full content contained by the Metadata of research articles. We applied two different techniques; in the first technique, we implemented the TF-IDF over Metadata and full content and considered the intersection of key terms. Secondly, similarity scores of Meta-data and full content were calculated by applying cosine similarity. This approach was assessed on a dataset of 271 research articles that were automatically downloaded from CiteseerX. The results revealed that the Meta-data of research articles could effectively represent the 47% proportion.
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