Pub Date : 2019-03-01DOI: 10.1109/ICACCS.2019.8728471
A. S, S. K, P. Poornachandran, V. Menon, S. P.
In this paper we propose a deep learning framework for classification of malware. There has been an enormous increase in the volume of malware generated lately which represents a genuine security danger to organizations and people. So as to battle the expansion of malwares, new strategies are needed to quickly identify and classify malware. Malimg dataset, a publicly available benchmark data set was used for the experimentation. The architecture used in this work is a hybrid cost-sensitive network of one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network which obtained an accuracy of 94.4%, an increase in performance compared to work done by [1] which got 84.9%. Hyper parameter tuning is done on deep learning architecture to set the parameters. A learning rate of 0.01 was taken for all experiments. Train-test split of 70-30% was done during experimentation. This facilitates to find how well the models perform on imbalanced data sets. Usual methods like disassembly, decompiling, de-obfuscation or execution of the binary need not be done in this proposed method. The source code and the trained models are made publicly available for further research.
{"title":"Deep Learning Framework and Visualization for Malware Classification","authors":"A. S, S. K, P. Poornachandran, V. Menon, S. P.","doi":"10.1109/ICACCS.2019.8728471","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728471","url":null,"abstract":"In this paper we propose a deep learning framework for classification of malware. There has been an enormous increase in the volume of malware generated lately which represents a genuine security danger to organizations and people. So as to battle the expansion of malwares, new strategies are needed to quickly identify and classify malware. Malimg dataset, a publicly available benchmark data set was used for the experimentation. The architecture used in this work is a hybrid cost-sensitive network of one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network which obtained an accuracy of 94.4%, an increase in performance compared to work done by [1] which got 84.9%. Hyper parameter tuning is done on deep learning architecture to set the parameters. A learning rate of 0.01 was taken for all experiments. Train-test split of 70-30% was done during experimentation. This facilitates to find how well the models perform on imbalanced data sets. Usual methods like disassembly, decompiling, de-obfuscation or execution of the binary need not be done in this proposed method. The source code and the trained models are made publicly available for further research.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127109817","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728470
R. Sangeetha, A. Vidhyashri, M. Reena, R. Sudharshan, Sangeetha Govindan, J. Ajayan
The circuit performance of dynamic CMOS comparators has been reviewed in this work. CMOS dynamic comparators contributes a major role on the implementation of mixed signal successive approximation register (SAR) type analog to digital converters (ADC). High precision, dynamic range, low voltage operation, high speed, low power consumption, reliability and offset voltage are the critical factors to be considered while designing CMOS dynamic comparators. This paper reviewed the performance of some popular dynamic CMOS comparators such as strong arm latch comparator, dynamic latched comparator, resistive diode comparator, double tail comparators, differential pair comparator and Lewis-Grey comparator.
{"title":"An Overview Of Dynamic CMOS Comparators","authors":"R. Sangeetha, A. Vidhyashri, M. Reena, R. Sudharshan, Sangeetha Govindan, J. Ajayan","doi":"10.1109/ICACCS.2019.8728470","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728470","url":null,"abstract":"The circuit performance of dynamic CMOS comparators has been reviewed in this work. CMOS dynamic comparators contributes a major role on the implementation of mixed signal successive approximation register (SAR) type analog to digital converters (ADC). High precision, dynamic range, low voltage operation, high speed, low power consumption, reliability and offset voltage are the critical factors to be considered while designing CMOS dynamic comparators. This paper reviewed the performance of some popular dynamic CMOS comparators such as strong arm latch comparator, dynamic latched comparator, resistive diode comparator, double tail comparators, differential pair comparator and Lewis-Grey comparator.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127564744","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728411
Surejya Suresh, M. T. P., Supriya M.H
In the current fast-moving world, human-computer- interactions (HCI) is one of the main contributors towards the progress of the country. Since the conventional input devices limit the naturalness and speed of human-computer- interactions, Sign Language recognition system has gained a lot of importance. Different sign languages can be used to express intentions and intonations or for controlling devices such as home robots. The main focus of this work is to create a vision based system, a Convolutional Neural Network (CNN) model, to identify six different sign languages from the images captured. The two CNN models developed have different type of optimizers, the Stochastic Gradient Descent (SGD) and Adam.
{"title":"Sign Language Recognition System Using Deep Neural Network","authors":"Surejya Suresh, M. T. P., Supriya M.H","doi":"10.1109/ICACCS.2019.8728411","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728411","url":null,"abstract":"In the current fast-moving world, human-computer- interactions (HCI) is one of the main contributors towards the progress of the country. Since the conventional input devices limit the naturalness and speed of human-computer- interactions, Sign Language recognition system has gained a lot of importance. Different sign languages can be used to express intentions and intonations or for controlling devices such as home robots. The main focus of this work is to create a vision based system, a Convolutional Neural Network (CNN) model, to identify six different sign languages from the images captured. The two CNN models developed have different type of optimizers, the Stochastic Gradient Descent (SGD) and Adam.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"448 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132616755","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728463
K. Vinothini, S. Jayanthy
Road sign recognition is one of the important tasks of intelligent transportation systems (ITS). The project aims at implementation of road sign detection and control of an autonomous vehicle using Haar Cascade Classifier algorithm. In this proposed work, the system automatically detects the road signs, controls the vehicle and command certain actions. The system consists of Raspberry Pi 3 processor and web camera which automatically captures the video data and converts them into number of frames which are processed by the proposed algorithm in OpenCV to detect the road sign and control the vehicle. Based on the detected sign, the vehicle is controlled by two DC motors interfaced with Raspberry Pi. The experimental results for Peak Signal to Noise Ratio (PSNR) and Minimum Mean Square Error indicate the proposed system gives more accurate results with higher PSNR value compared to Hough Transformation. The performance metrics of the algorithm implemented in ARM processor is much better compared to the results obtained using MATLAB software.
{"title":"Road Sign Recognition System for Autonomous Vehicle using Raspberry Pi","authors":"K. Vinothini, S. Jayanthy","doi":"10.1109/ICACCS.2019.8728463","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728463","url":null,"abstract":"Road sign recognition is one of the important tasks of intelligent transportation systems (ITS). The project aims at implementation of road sign detection and control of an autonomous vehicle using Haar Cascade Classifier algorithm. In this proposed work, the system automatically detects the road signs, controls the vehicle and command certain actions. The system consists of Raspberry Pi 3 processor and web camera which automatically captures the video data and converts them into number of frames which are processed by the proposed algorithm in OpenCV to detect the road sign and control the vehicle. Based on the detected sign, the vehicle is controlled by two DC motors interfaced with Raspberry Pi. The experimental results for Peak Signal to Noise Ratio (PSNR) and Minimum Mean Square Error indicate the proposed system gives more accurate results with higher PSNR value compared to Hough Transformation. The performance metrics of the algorithm implemented in ARM processor is much better compared to the results obtained using MATLAB software.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130488344","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728326
R. Karthik, T. Divagar, M. Karthikeyan, D. Kumar
At present all the works of human beings are replaced by the robots. Generally robotics are classified into service robotics and industrial robotics. Nowadays all fields are occupied by robotics including, hospitals, agriculture, defense, hazardous environment and office. The Robots are used where ever human does not do their work efficiently and safely such as handling poisonous and explosive products in industries. The direction of the robotic vehicle and the spraying of water in the fire is controlled by the voice command. The communication between the vehicle and humans are established through the NODE MCU and ARDUINO. The vehicle consists of three major components such as the NODE MCU, ARDUINO, and water level indicator (on vehicle). This Robotic vehicle is involved to rescue the human beings and extinguishing the fire where fire fighters are not able to enter into the fire accidental area.
{"title":"Voice Operated Intelligent Fire Extinguishing Vehicle","authors":"R. Karthik, T. Divagar, M. Karthikeyan, D. Kumar","doi":"10.1109/ICACCS.2019.8728326","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728326","url":null,"abstract":"At present all the works of human beings are replaced by the robots. Generally robotics are classified into service robotics and industrial robotics. Nowadays all fields are occupied by robotics including, hospitals, agriculture, defense, hazardous environment and office. The Robots are used where ever human does not do their work efficiently and safely such as handling poisonous and explosive products in industries. The direction of the robotic vehicle and the spraying of water in the fire is controlled by the voice command. The communication between the vehicle and humans are established through the NODE MCU and ARDUINO. The vehicle consists of three major components such as the NODE MCU, ARDUINO, and water level indicator (on vehicle). This Robotic vehicle is involved to rescue the human beings and extinguishing the fire where fire fighters are not able to enter into the fire accidental area.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128136064","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728332
Jayshree Talukdar, P. Sarma
In this paper we are mainly classifying cancerous or non-cancerous cells of human lung which was further classified into which category it falls benign or malignant. Area feature from region of interest (ROI) and Support Vector Machine is applied for classification and CLAHE as an enhancement technique. This paper concentrated on classification of lung nodules by Support Vector Machine technique.
{"title":"An Approach to Classify Lung Nodules for Detection of Cancer Cells","authors":"Jayshree Talukdar, P. Sarma","doi":"10.1109/ICACCS.2019.8728332","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728332","url":null,"abstract":"In this paper we are mainly classifying cancerous or non-cancerous cells of human lung which was further classified into which category it falls benign or malignant. Area feature from region of interest (ROI) and Support Vector Machine is applied for classification and CLAHE as an enhancement technique. This paper concentrated on classification of lung nodules by Support Vector Machine technique.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128144063","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728513
S. Manikandan, M. Jeyakarthic
Evolutions and recent trends in communication technologies have the development of low charge, low power, tiny sized and multidimensional purpose sensor node for wireless sensor networks (WSNs). But energy constrained nature of WSNs demands that their planning and interactive protocols to be designed in an energy aware manner. This paper proposed to make a trend analysis reports on the major perspectives of fuzzy based distributed energy efficient clustering techniques. The most representative fuzzy based clustering techniques describes, deliberated and qualitatively analyzed. In particular the proceeds of different distributed clustering are analyzed with respect to their implication performance and application circumstances. This analysis aims to deliver suitable guidance for system architects to evaluate and select appropriate fuzzy based distributed clustering techniques for their specific application.
{"title":"A Trend Analysis of Diffusion Energy Effective Clustering Techniques in Wireless Sensor Networks","authors":"S. Manikandan, M. Jeyakarthic","doi":"10.1109/ICACCS.2019.8728513","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728513","url":null,"abstract":"Evolutions and recent trends in communication technologies have the development of low charge, low power, tiny sized and multidimensional purpose sensor node for wireless sensor networks (WSNs). But energy constrained nature of WSNs demands that their planning and interactive protocols to be designed in an energy aware manner. This paper proposed to make a trend analysis reports on the major perspectives of fuzzy based distributed energy efficient clustering techniques. The most representative fuzzy based clustering techniques describes, deliberated and qualitatively analyzed. In particular the proceeds of different distributed clustering are analyzed with respect to their implication performance and application circumstances. This analysis aims to deliver suitable guidance for system architects to evaluate and select appropriate fuzzy based distributed clustering techniques for their specific application.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132322195","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728481
S. Pattar
Lung nodules are a commonly occurring problem in the society. This problem is more prevalent in populations that expose themselves to risk factors such as smoking, pollution, etc. The current techniques used for segmentation of lung nodules from CT images have the following drawbacks: Most segmentation algorithms use Local Fitting Models that need re-initialization for the sign distance function. The inhomogeneity in the CT images make the algorithms to settle at local minima and lead to wrong segmentation. Re-initialization increases the time required and makes the algorithm slow. Region-based active contour models are powerful and flexible methods which can able to segment real and synthetic images. In the proposed method Global Convex Segmentation (GCS) and Split Bregman technique is incorporated into a region based active contour model such as Chan-Vese (CV) with Region-Scalable Fitting (RSF) scheme to segment the Lung nodules region. Local Binary Pattern descriptor (LBP) is used to extract the tumor features. The extracted features are used to classify the nodules as tumor or non-tumor with the help of Support Vector Machine (SVM). The classification accuracy obtained is enhanced compared to other existing methods. Experimental results are demonstrated by using Lung CT images.
{"title":"Segmentation and Classification of Lung Nodules using Split Bregman and SVM Classifier","authors":"S. Pattar","doi":"10.1109/ICACCS.2019.8728481","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728481","url":null,"abstract":"Lung nodules are a commonly occurring problem in the society. This problem is more prevalent in populations that expose themselves to risk factors such as smoking, pollution, etc. The current techniques used for segmentation of lung nodules from CT images have the following drawbacks: Most segmentation algorithms use Local Fitting Models that need re-initialization for the sign distance function. The inhomogeneity in the CT images make the algorithms to settle at local minima and lead to wrong segmentation. Re-initialization increases the time required and makes the algorithm slow. Region-based active contour models are powerful and flexible methods which can able to segment real and synthetic images. In the proposed method Global Convex Segmentation (GCS) and Split Bregman technique is incorporated into a region based active contour model such as Chan-Vese (CV) with Region-Scalable Fitting (RSF) scheme to segment the Lung nodules region. Local Binary Pattern descriptor (LBP) is used to extract the tumor features. The extracted features are used to classify the nodules as tumor or non-tumor with the help of Support Vector Machine (SVM). The classification accuracy obtained is enhanced compared to other existing methods. Experimental results are demonstrated by using Lung CT images.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473208","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728504
D. Naveen, K. Praveen
There are so many modern cryptographic protocols are available which can be used for authenticating wirelessly connected devices. Usually the keys are stored inside the memory of the integrated device which will prompt adversaries to extract secret keys from integrated device. We can persist these attacks by using Physically unclonable functions (PUFs). PUF protocol are designed in such a way that it needs to be light-weight and is resistance against physical attacks. In this paper, we are utilizing the secret sharing method with PUF for an efficient and secure method to authenticate the devices. This new approach is lightweight and suitable for energy constrained platforms such as IOT, smart cards. The proposed protocol does not follow the classic PUF protocol challenge and response pairs, instead of that here a set of shares generated using (2, n) ideal visual secret sharing method are used for authentication
{"title":"PUF Authentication using Visual Secret Sharing Scheme","authors":"D. Naveen, K. Praveen","doi":"10.1109/ICACCS.2019.8728504","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728504","url":null,"abstract":"There are so many modern cryptographic protocols are available which can be used for authenticating wirelessly connected devices. Usually the keys are stored inside the memory of the integrated device which will prompt adversaries to extract secret keys from integrated device. We can persist these attacks by using Physically unclonable functions (PUFs). PUF protocol are designed in such a way that it needs to be light-weight and is resistance against physical attacks. In this paper, we are utilizing the secret sharing method with PUF for an efficient and secure method to authenticate the devices. This new approach is lightweight and suitable for energy constrained platforms such as IOT, smart cards. The proposed protocol does not follow the classic PUF protocol challenge and response pairs, instead of that here a set of shares generated using (2, n) ideal visual secret sharing method are used for authentication","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116894073","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 : 2019-03-01DOI: 10.1109/ICACCS.2019.8728343
T. Kavitha, Rajadurai Kaliyaperumal
A wireless sensor network (WSN) is a group of resource-constrained, inexpensive, tiny, and homogeneous or heterogeneous sensor nodes. The inherent nature of WSNs such that it makes them deployable in a variety of circumstances, which increases the interest towards them but at the same time poses tremendous challenges such as resource-constrained nodes, unattended operations, unknown topology and wireless communication links. Security in WSNs can be achieved with the help of various cryptographic operations. The strength of cryptographic system depends on the secrecy of the key it uses. So, a solid strong key management frame work is the prerequisite for the cryptographic primitive upon which other security primitives are built.To improve the energy efficiency and increase the resilience more effectively, an Energy Efficient Hierarchical Key management Protocol (EEHKMP) for hierarchical homogeneous WSN is proposed. In this protocol, a Differentiated random KPD (DKPD) process is employed for randomly deployed distributed WSN. Its main objective is to distribute different number of keys which are chosen randomly to different sensors in order to enhance the resilience of certain links such that the nodes can route through those links with higher resilience. This DKPD process divides the sensor nodes into different classes and pre-distributes the keys according to each class. Nodes with maximum residual energy and minimum distance are elected as cluster heads (CHs). The CH sets up the intra-cluster and inter-cluster routes with nodes having more shared keys. CH generates multiple random key shares to generate pair-wise key and transmits each key share to source and destination on each hop route, which is selected based on the cost function. Key shares are hop-by-hop encrypted / decrypted by a combination of all shared pre-distributed keys on that hop. Finally, a key update mechanism is presented to update the keys.
无线传感器网络(WSN)是一组资源受限、价格低廉、体积小、同质或异构的传感器节点。无线传感器网络的固有特性使其能够在各种情况下进行部署，这增加了人们对其的兴趣，但同时也带来了巨大的挑战，如资源受限节点、无人值守操作、未知拓扑和无线通信链路。无线传感器网络的安全性可以通过各种加密操作来实现。密码系统的强度取决于它所使用的密钥的保密性。因此，坚实的密钥管理框架是构建其他安全原语的加密原语的先决条件。为了更有效地提高能源效率和增强弹性，提出了一种用于分层同构WSN的节能分层密钥管理协议(EEHKMP)。该协议对随机部署的分布式WSN采用差分随机KPD (Differentiated random KPD, DKPD)过程。它的主要目标是将随机选择的不同数量的密钥分配给不同的传感器，以增强某些链路的弹性，使节点能够以更高的弹性通过这些链路。该DKPD过程将传感器节点划分为不同的类别，并根据每个类别预分发密钥。选取剩余能量最大、距离最小的节点作为簇头。CH为拥有更多共享密钥的节点建立集群内和集群间路由。CH生成多个随机密钥共享以生成成对密钥，并将每个密钥共享发送到每一跳路由上的源和目的，根据代价函数选择。密钥共享是由该跳上所有共享的预分发密钥的组合逐跳加密/解密的。最后，提出了一种密钥更新机制来更新密钥。
{"title":"Energy Efficient Hierarchical Key Management Protocol","authors":"T. Kavitha, Rajadurai Kaliyaperumal","doi":"10.1109/ICACCS.2019.8728343","DOIUrl":"https://doi.org/10.1109/ICACCS.2019.8728343","url":null,"abstract":"A wireless sensor network (WSN) is a group of resource-constrained, inexpensive, tiny, and homogeneous or heterogeneous sensor nodes. The inherent nature of WSNs such that it makes them deployable in a variety of circumstances, which increases the interest towards them but at the same time poses tremendous challenges such as resource-constrained nodes, unattended operations, unknown topology and wireless communication links. Security in WSNs can be achieved with the help of various cryptographic operations. The strength of cryptographic system depends on the secrecy of the key it uses. So, a solid strong key management frame work is the prerequisite for the cryptographic primitive upon which other security primitives are built.To improve the energy efficiency and increase the resilience more effectively, an Energy Efficient Hierarchical Key management Protocol (EEHKMP) for hierarchical homogeneous WSN is proposed. In this protocol, a Differentiated random KPD (DKPD) process is employed for randomly deployed distributed WSN. Its main objective is to distribute different number of keys which are chosen randomly to different sensors in order to enhance the resilience of certain links such that the nodes can route through those links with higher resilience. This DKPD process divides the sensor nodes into different classes and pre-distributes the keys according to each class. Nodes with maximum residual energy and minimum distance are elected as cluster heads (CHs). The CH sets up the intra-cluster and inter-cluster routes with nodes having more shared keys. CH generates multiple random key shares to generate pair-wise key and transmits each key share to source and destination on each hop route, which is selected based on the cost function. Key shares are hop-by-hop encrypted / decrypted by a combination of all shared pre-distributed keys on that hop. Finally, a key update mechanism is presented to update the keys.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"572 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116292398","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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