Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.
{"title":"Scalable Honeypot Architecture for Identifying Malicious Network Activities","authors":"Gokul Kannan Sadasivam, C. Hota","doi":"10.1109/EITES.2015.15","DOIUrl":"https://doi.org/10.1109/EITES.2015.15","url":null,"abstract":"Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132582633","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}
In this paper, we present a new matrix factorization method for recommender system problems, named bounded-SVD, which utilizes the constraint that all the ratings in the rating matrix are bounded within a pre-determined range. In our proposed method, the bound constraints are included in the objective function so that both the task of minimizing errors and the constraints are taken into account during the optimization process. For evaluation, we compare the performance of bounded-SVD with an existing method, called Bounded Matrix Factorization (BMF), which also uses the bound constraints on the ratings. The results on major real-world recommender system datasets show that our method outperforms BMF in almost cases and it is also faster and more simple to implement than BMF. Moreover, the way the bound constraints are integrated in bounded-SVD can also be applied to other optimization problems with bound constraints as well.
{"title":"Bounded-SVD: A Matrix Factorization Method with Bound Constraints for Recommender Systems","authors":"B. Le, Kazuki Mori, R. Thawonmas","doi":"10.2197/ipsjjip.24.314","DOIUrl":"https://doi.org/10.2197/ipsjjip.24.314","url":null,"abstract":"In this paper, we present a new matrix factorization method for recommender system problems, named bounded-SVD, which utilizes the constraint that all the ratings in the rating matrix are bounded within a pre-determined range. In our proposed method, the bound constraints are included in the objective function so that both the task of minimizing errors and the constraints are taken into account during the optimization process. For evaluation, we compare the performance of bounded-SVD with an existing method, called Bounded Matrix Factorization (BMF), which also uses the bound constraints on the ratings. The results on major real-world recommender system datasets show that our method outperforms BMF in almost cases and it is also faster and more simple to implement than BMF. Moreover, the way the bound constraints are integrated in bounded-SVD can also be applied to other optimization problems with bound constraints as well.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114612158","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}
For ELALRED algorithm the concept of a Learning Automata-Like (LAL) mechanism devised for congestion avoidance in wired networks. The algorithm, named as Efficient LAL Random Early Detection (ELALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with an LAL philosophy. The primary objective of ELALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In ELALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. ELALRED approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of ELALRED over the LALRED and traditional RED methods which were chosen as the benchmarks for performance comparison purposes.
{"title":"Efficient LALRED for Congestion Avoidance Using Automata-Like Solution","authors":"S. Mahajan","doi":"10.1109/EITES.2015.11","DOIUrl":"https://doi.org/10.1109/EITES.2015.11","url":null,"abstract":"For ELALRED algorithm the concept of a Learning Automata-Like (LAL) mechanism devised for congestion avoidance in wired networks. The algorithm, named as Efficient LAL Random Early Detection (ELALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with an LAL philosophy. The primary objective of ELALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In ELALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. ELALRED approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of ELALRED over the LALRED and traditional RED methods which were chosen as the benchmarks for performance comparison purposes.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164675","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}
Recent advances in low power radios and sensor technology have enabled the pervasive deployment of sensor networks consisting of sensor nodes that are very small in size and relatively inexpensive. Wireless Sensor Networks (WSNs) have been seen more as a solution to large scale tracking and monitoring applications, because their low data rate, low energy consumption and short range communication presents the great opportunity to instrument and monitor the physical world at unprecedented scale. However realization of WSNs needs to satisfy constraints introduced by factors such as limited power, limited communication bandwidth, limited processing capacity, and small storage capacity. Therefore, the design of efficient technique for optimizing the capabilities of networks is becoming an increasingly critical aspect in networking. This paper addresses constrained optimization problems namely the Capacitated Minimum Forest (CMF) problem. To utilize the critical WSNs resources precisely, the development of algorithms with quality guaranteed solutions in WSNs is needed. We proposed that optimal approximation algorithms achieve highest optimization goal which minimize Cost of network resource consumption.
{"title":"Optimized Technique for Capacitated Minimum Forest Problem In Wireless Sensor Networks","authors":"Shwetal R. Jaiswal","doi":"10.1109/EITES.2015.12","DOIUrl":"https://doi.org/10.1109/EITES.2015.12","url":null,"abstract":"Recent advances in low power radios and sensor technology have enabled the pervasive deployment of sensor networks consisting of sensor nodes that are very small in size and relatively inexpensive. Wireless Sensor Networks (WSNs) have been seen more as a solution to large scale tracking and monitoring applications, because their low data rate, low energy consumption and short range communication presents the great opportunity to instrument and monitor the physical world at unprecedented scale. However realization of WSNs needs to satisfy constraints introduced by factors such as limited power, limited communication bandwidth, limited processing capacity, and small storage capacity. Therefore, the design of efficient technique for optimizing the capabilities of networks is becoming an increasingly critical aspect in networking. This paper addresses constrained optimization problems namely the Capacitated Minimum Forest (CMF) problem. To utilize the critical WSNs resources precisely, the development of algorithms with quality guaranteed solutions in WSNs is needed. We proposed that optimal approximation algorithms achieve highest optimization goal which minimize Cost of network resource consumption.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129524281","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}
The algorithm proposed in this paper enhances the K Strange points clustering algorithm by selecting the first of unchanging K strange points as the minimum of the dataset and then finds the next strange point as the point which is farthest from the minimum and continues this process till it finds the K points which are farthest and almost equally spaced from each other. It then assigns the remaining points in the dataset into clusters formed by these K farthest or Strange points. The algorithm presented in this paper successfully addresses the issues related to longer execution time and formation of inaccurate clusters seen in the K Strange points clustering algorithm.
{"title":"Enhanced K Strange Points Clustering Algorithm","authors":"Terence Johnson, S. Singh","doi":"10.1109/EITES.2015.14","DOIUrl":"https://doi.org/10.1109/EITES.2015.14","url":null,"abstract":"The algorithm proposed in this paper enhances the K Strange points clustering algorithm by selecting the first of unchanging K strange points as the minimum of the dataset and then finds the next strange point as the point which is farthest from the minimum and continues this process till it finds the K points which are farthest and almost equally spaced from each other. It then assigns the remaining points in the dataset into clusters formed by these K farthest or Strange points. The algorithm presented in this paper successfully addresses the issues related to longer execution time and formation of inaccurate clusters seen in the K Strange points clustering algorithm.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771366","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}
We consider the scenario where a consumer can securely outsource their network telemetry data to a Cloud Service Provider and enable a third party to audit such telemetry for any security forensics. Especially we consider the use case of privacy preserving search in network log audits. In this paper we experiment with advances in Identity Based Encryption and Attribute-Based encryption schemes for auditing network logs.
{"title":"Experiments in Encrypted and Searchable Network Audit Logs","authors":"Bhanu Prakash Gopularam, Sashank Dara, N. Nalini","doi":"10.1109/EITES.2015.13","DOIUrl":"https://doi.org/10.1109/EITES.2015.13","url":null,"abstract":"We consider the scenario where a consumer can securely outsource their network telemetry data to a Cloud Service Provider and enable a third party to audit such telemetry for any security forensics. Especially we consider the use case of privacy preserving search in network log audits. In this paper we experiment with advances in Identity Based Encryption and Attribute-Based encryption schemes for auditing network logs.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125773509","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}
Non-exclusive clustering is a partitioning based clustering scheme wherein the data points are clustered such that they belong to one or more clusters. Usually in real world applications, the datasets that we work with are not entirely exclusive in nature. In applications such as gene expression data analysis and satellite image processing, non-exclusive algorithms need to be employed for better and more accurate cluster analysis. Therefore, we intend to tackle such problems with a non-exclusive clustering algorithm, closely determined by a nonexclusivity score (NES). The NES is based on a feature class correlation measure, which helps to determine the significant overlap between the data points in the dataset and aids us in comprehending the clusters to which they belong to.
{"title":"Non-exclusive Clustering: A Partitioning Approach","authors":"N. Agarwal, H. A. Ahmed, D. Bhattacharyya","doi":"10.1109/EITES.2015.9","DOIUrl":"https://doi.org/10.1109/EITES.2015.9","url":null,"abstract":"Non-exclusive clustering is a partitioning based clustering scheme wherein the data points are clustered such that they belong to one or more clusters. Usually in real world applications, the datasets that we work with are not entirely exclusive in nature. In applications such as gene expression data analysis and satellite image processing, non-exclusive algorithms need to be employed for better and more accurate cluster analysis. Therefore, we intend to tackle such problems with a non-exclusive clustering algorithm, closely determined by a nonexclusivity score (NES). The NES is based on a feature class correlation measure, which helps to determine the significant overlap between the data points in the dataset and aids us in comprehending the clusters to which they belong to.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129033591","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}
Peer-to-Peer (P2P) networks have seen a rapid growth, spanning diverse applications like online anonymity (Tor), online payment (Bit coin), file sharing (Bit Torrent), etc. However, the success of these applications has raised concerns among ISPs and Network administrators. These types of traffic worsen the congestion of the network, and create security vulnerabilities. Hence, P2P traffic identification has been researched actively in recent times. Early P2P traffic identification approaches were based on port-based inspection. Presently, Deep Packet Inspection (DPI) is a prominent technique used to identify P2P traffic. But it relies on payload signatures which are not resilient against port masquerading, traffic encryption and NATing. In this paper, we propose a novel P2P traffic identification mechanism based on the host behaviour from the transport layer headers. A set of heuristics was identified by analysing the off-line datasets collected in our test bed. This approach is privacy preserving as it does not examine the payload content. The usefulness of these heuristics is shown on real-time traffic traces received from our campus backbone, where in the best case only 0.20% of flows were unknown.
{"title":"Heuristic-Based Real-Time P2P Traffic Identification","authors":"Jagan Mohan Reddy, C. Hota","doi":"10.1109/EITES.2015.16","DOIUrl":"https://doi.org/10.1109/EITES.2015.16","url":null,"abstract":"Peer-to-Peer (P2P) networks have seen a rapid growth, spanning diverse applications like online anonymity (Tor), online payment (Bit coin), file sharing (Bit Torrent), etc. However, the success of these applications has raised concerns among ISPs and Network administrators. These types of traffic worsen the congestion of the network, and create security vulnerabilities. Hence, P2P traffic identification has been researched actively in recent times. Early P2P traffic identification approaches were based on port-based inspection. Presently, Deep Packet Inspection (DPI) is a prominent technique used to identify P2P traffic. But it relies on payload signatures which are not resilient against port masquerading, traffic encryption and NATing. In this paper, we propose a novel P2P traffic identification mechanism based on the host behaviour from the transport layer headers. A set of heuristics was identified by analysing the off-line datasets collected in our test bed. This approach is privacy preserving as it does not examine the payload content. The usefulness of these heuristics is shown on real-time traffic traces received from our campus backbone, where in the best case only 0.20% of flows were unknown.","PeriodicalId":170773,"journal":{"name":"2015 International Conference on Emerging Information Technology and Engineering Solutions","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133470748","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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