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Prof. University of the West Scotland
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, PA1 2BE, UK

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Research Keywords & Expertise

0 Artificial Intelligence (AI) applied to Data Mining/Big Data
0 Scheduling and Logistics Problems
0 Smart Decision Support Techniques and Intelligent Systems
0 Trust/Security Modelling in Networks
0 Cloud and Distributed Systems

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Article
Published: 26 August 2021 in Wireless Personal Communications
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Trade-off between energy conservation and efficiency is one of the most important issues in designing Wireless Sensor Network (WSN) based applications. Network life time is primarily determined by the life time of battery. Recently, energy harvesting techniques that will recharge the battery in different non-conventional ways are being investigated by researchers. In this paper, an adaptive cross layer protocol is proposed which will provide trade off between energy harvesting time and active time for message transmission with the aim of increasing network lifetime. Depending on the value of various network parameters like, remaining energy of node, node density, message density in a particular region of the network, the cross-layer protocol will change its policy. The paper also proposes a cluster head selection method that ensures maximum network life time and higher quality of service. The result shows an overall increase in network lifetime as compared to other protocols.

ACS Style

Arindam Giri; Subrata Dutta; Sarmistha Neogy; Bikrant Koirala; Keshav Dahal. Adaptive Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN. Wireless Personal Communications 2021, 1 -19.

AMA Style

Arindam Giri, Subrata Dutta, Sarmistha Neogy, Bikrant Koirala, Keshav Dahal. Adaptive Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN. Wireless Personal Communications. 2021; ():1-19.

Chicago/Turabian Style

Arindam Giri; Subrata Dutta; Sarmistha Neogy; Bikrant Koirala; Keshav Dahal. 2021. "Adaptive Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN." Wireless Personal Communications , no. : 1-19.

Conference paper
Published: 08 August 2021 in Intelligent Sustainable Systems
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The Ethereum blockchain allows, through software called smart contract, to automate the contract execution between multiple parties without requiring a trusted middle party. However, smart contracts are vulnerable to attacks. Tools and programming practices are available to support the development of secure smart contracts. These approaches are effective to mitigate the smart contract vulnerabilities, but the unsophisticated ecosystem of the smart contract prevents these approaches from being foolproof. Besides, the Blockchain immutability does not allow smart contracts deployed in the Blockchain to be updated. Thus, businesses and developers would develop new contracts if vulnerabilities were detected in their smart contracts deployed in Ethereum, which would imply new costs for the business. To support developers and businesses in the smart contract security decision makings, we investigate the applicability of the security code metric from non-blockchain into the smart contract domain. We use the Goal Question Metric (GQM) approach to analyze the applicability of these metrics into the smart contract domain based on metric construct and measurement. As a result, we found 15 security code metrics that can be applied to smart contract development.

ACS Style

Aboua Ange Kevin N’Da; Santiago Matalonga; Keshav Dahal. Applicability of the Software Security Code Metrics for Ethereum Smart Contract. Intelligent Sustainable Systems 2021, 106 -119.

AMA Style

Aboua Ange Kevin N’Da, Santiago Matalonga, Keshav Dahal. Applicability of the Software Security Code Metrics for Ethereum Smart Contract. Intelligent Sustainable Systems. 2021; ():106-119.

Chicago/Turabian Style

Aboua Ange Kevin N’Da; Santiago Matalonga; Keshav Dahal. 2021. "Applicability of the Software Security Code Metrics for Ethereum Smart Contract." Intelligent Sustainable Systems , no. : 106-119.

Journal article
Published: 07 June 2021 in IEEE Transactions on Intelligent Transportation Systems
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Existing navigation services provide route options based on a single metric without considering user's preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users' travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user's travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

ACS Style

Peisong Li; Xinheng Wang; Honghao Gao; Xiaolong Xu; Muddesar Iqbal; Keshav Dahal. A Dynamic and Scalable User-Centric Route Planning Algorithm Based on Polychromatic Sets Theory. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -11.

AMA Style

Peisong Li, Xinheng Wang, Honghao Gao, Xiaolong Xu, Muddesar Iqbal, Keshav Dahal. A Dynamic and Scalable User-Centric Route Planning Algorithm Based on Polychromatic Sets Theory. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-11.

Chicago/Turabian Style

Peisong Li; Xinheng Wang; Honghao Gao; Xiaolong Xu; Muddesar Iqbal; Keshav Dahal. 2021. "A Dynamic and Scalable User-Centric Route Planning Algorithm Based on Polychromatic Sets Theory." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.

Journal article
Published: 07 June 2021 in Applied Soft Computing
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Wi-Fi fingerprint systems provide cost-effective and reliable solution for indoor positioning. However, such systems incur high calibration cost in the training phase and high searching overhead in the positioning phase. Moreover, huge storage requirement for the radio map of a large-scale fingerprint system is another major issue. Several solutions based on crowd-sourcing or machine learning technique have been proposed in literature to reduce the calibration overhead. On the other hand, various clustering methods have been proposed over the past decade to reduce the searching overhead. However, none of the existing systems has addressed the issue of high storage requirement for the fingerprint database constructed in the training phase. Moreover, presence of outlier in the received signal strength (RSS) measurements severely impacts the positioning accuracy of such systems. Thus, this paper proposes an efficient clustering strategy for fingerprint based positioning systems to reduce the storage overhead and searching overhead incurred by such systems and also proposes a robust outlier mitigation technique to improve their positioning accuracy. The performances of our proposed positioning system are evaluated and compared with five existing fingerprint techniques in both the simulation test bed as well as real indoor environment via extensive experimentation. The experimental results demonstrate that our proposed system can not only reduce the storage overhead and searching overhead but also improve the positioning accuracy compared to the other existing techniques.

ACS Style

Pampa Sadhukhan; Supriya Gain; Keshav Dahal; Samiran Chattopadhyay; Nilkantha Garain; Xinheng Wang. An efficient clustering with robust outlier mitigation for Wi-Fi fingerprint based indoor positioning. Applied Soft Computing 2021, 109, 107549 .

AMA Style

Pampa Sadhukhan, Supriya Gain, Keshav Dahal, Samiran Chattopadhyay, Nilkantha Garain, Xinheng Wang. An efficient clustering with robust outlier mitigation for Wi-Fi fingerprint based indoor positioning. Applied Soft Computing. 2021; 109 ():107549.

Chicago/Turabian Style

Pampa Sadhukhan; Supriya Gain; Keshav Dahal; Samiran Chattopadhyay; Nilkantha Garain; Xinheng Wang. 2021. "An efficient clustering with robust outlier mitigation for Wi-Fi fingerprint based indoor positioning." Applied Soft Computing 109, no. : 107549.

Journal article
Published: 19 March 2021 in IEEE Transactions on Computational Social Systems
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Location-aware recommendation is considered as one of human behavior cognitive analyses in the world of human-machine-environment system. The development of 5G technology and ubiquitous mobile devices has led to the emergence of a new online platform, location-based social networks (LBSNs), which allows users to share their locations. The essential feature of LBSNs is to provide users with location recommendations that help them explore new places and also to make LBSNs more prevalent to users. Most of the existing research is focusing on the introduction of new features and how these new features affect the check-in behaviors of the users. In addition, the dependencies between each feature and the probability of a user visiting the site is always a principle to follow. However, a user's decision could be determined by considering several features at the same time. When a full model is applied by considering all the features, an overfitting problem could be occurred owing to the lack of sufficient data for each individual user. In this article, an intermediate solution was proposed to address all of these problems by fragmenting the model into several partial models, where each partial model is responsible for a few features. An additive strategy was also implemented to support the development of personalized partial models. Furthermore, a partition-based approach was introduced to explore the hidden patterns from the geographically clustered check-in data. The performance of the approaches has been evaluated by using the data sets from Foursquare and it demonstrates that the proposed approach outperforms the state-of-the-art approaches.

ACS Style

Elahe Naserian; Xinheng Wang; Keshav P. Dahal; Jose M. Alcaraz-Calero; Honghao Gao. A Partition-Based Partial Personalized Model for Points of Interest Recommendations. IEEE Transactions on Computational Social Systems 2021, PP, 1 -15.

AMA Style

Elahe Naserian, Xinheng Wang, Keshav P. Dahal, Jose M. Alcaraz-Calero, Honghao Gao. A Partition-Based Partial Personalized Model for Points of Interest Recommendations. IEEE Transactions on Computational Social Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Elahe Naserian; Xinheng Wang; Keshav P. Dahal; Jose M. Alcaraz-Calero; Honghao Gao. 2021. "A Partition-Based Partial Personalized Model for Points of Interest Recommendations." IEEE Transactions on Computational Social Systems PP, no. 99: 1-15.

Journal article
Published: 01 February 2021 in Sustainability
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In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

ACS Style

Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability 2021, 13, 1522 .

AMA Style

Raja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Wajahat Ali Khan, Asad Masood Khattak, Bashir Hayat. Entropy Based Features Distribution for Anti-DDoS Model in SDN. Sustainability. 2021; 13 (3):1522.

Chicago/Turabian Style

Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Wajahat Ali Khan; Asad Masood Khattak; Bashir Hayat. 2021. "Entropy Based Features Distribution for Anti-DDoS Model in SDN." Sustainability 13, no. 3: 1522.

Journal article
Published: 24 September 2020 in Computers & Security
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The massive growth of data in the network leads to attacks or intrusions. An intrusion detection system detects intrusions from high volume datasets but increases complexities. A network generates a large number of unlabeled data that is free from labeling costs. Unsupervised feature selection handles these data and reduces computational complexities. In this paper, we have proposed a clustering method based on unsupervised feature selection and cluster center initialization for intrusion detection. This method computes initial centers using sets of semi-identical instances, which indicate dense data space and avoid outliers as initial cluster centers. A spatial distance between data points and cluster centers create micro-clusters. Similar micro-clusters merge into a cluster that is an arbitrary shape. The proposed cluster center initialization based clustering method performs better than basic clustering, which takes fewer iterations to form final clusters and provides better accuracy. We simulated a wormhole attack and generated the Wormhole dataset in the mobile ad-hoc network in NS-3. Micro-clustering methods have executed on different network datasets (KDD, CICIDS2017, and Wormhole dataset), which outperformed for new attacks or those contain few samples. Experimental results confirm that the proposed method is suitable for LAN and mobile ad-hoc network, varying data density, and large datasets.

ACS Style

Mahendra Prasada; Sachin Tripathia; Keshav Dahalb. Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Computers & Security 2020, 99, 102062 .

AMA Style

Mahendra Prasada, Sachin Tripathia, Keshav Dahalb. Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Computers & Security. 2020; 99 ():102062.

Chicago/Turabian Style

Mahendra Prasada; Sachin Tripathia; Keshav Dahalb. 2020. "Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection." Computers & Security 99, no. : 102062.

Journal article
Published: 16 December 2019 in Sensors
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Energy prediction plays a vital role in designing an efficient power management system for any environmentally powered Wireless Sensor Networks (WSNs). Most of the Moving Average (MA)-based energy prediction methods depend on past energy readings of the concerned node to predict its future energy availability. However, in case of RF powered WSNs the harvesting history of the main node along with neighbouring nodes can also be used to develop a more robust prediction technique. In this paper, we propose a Multi-Node energy prediction method for Radio Frequency Energy Harvesting (RF-EH) WSNs, which predicts the future energy availability by taking into account harvesting history of all nodes surrounding the main node. We analyse the effective distance for prediction and also develop a mathematical model to compute the optimum value of prediction interval, which has a major effect in prediction accuracy and system design, considering energy neutrality. Results show that Multi-Node prediction is less sensitive to prediction interval while inheriting the advantages of MA techniques. Also, nodes located at a larger distance were utilized less for prediction, and as the prediction interval increased, the utilization of more distant nodes decreased. Furthermore, we also establish a linear relation between the prediction interval and the energy threshold limit.

ACS Style

Bikrant Koirala; Keshav Dahal; Paul Keir; Wenbing Chen. A Multi-Node Energy Prediction Approach Combined with Optimum Prediction Interval for RF Powered WSNs. Sensors 2019, 19, 5551 .

AMA Style

Bikrant Koirala, Keshav Dahal, Paul Keir, Wenbing Chen. A Multi-Node Energy Prediction Approach Combined with Optimum Prediction Interval for RF Powered WSNs. Sensors. 2019; 19 (24):5551.

Chicago/Turabian Style

Bikrant Koirala; Keshav Dahal; Paul Keir; Wenbing Chen. 2019. "A Multi-Node Energy Prediction Approach Combined with Optimum Prediction Interval for RF Powered WSNs." Sensors 19, no. 24: 5551.

Journal article
Published: 02 December 2019 in Applied Soft Computing
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The exponential growth of network size leads to increase attacks and intrusions. Detection of these attacks from the network has turned into a noteworthy issue of security. An intrusion detection system is an important approach to achieves high detection rate. A high dimensional dataset increase complexities of detection systems. In this paper, we have designed a novel intelligent system that comprises the feature selection with a hybrid approach of the Rough set theory and the Bayes theorem. The proposed feature selection computed core features and ranked them based on estimated probability. In a decision system, an object may belong to a single or multiple decision, and a feature contains a set of objects that occurrences compute an estimated probability. The rough set theory is being applied to classify information into lower and upper approximations. Uncertain information is distinguished using rough set approximations and solved by the Bayes theorem. In this research work, it has also been highlighted the quantitative realism of recently generated dataset and compared to publicly available datasets. This approach reduces false alarm rate, computational complexity, training complexity and increases detection rate. Comparisons with relevant classifiers are also tabled that show proposed method performs better than existing classifiers.

ACS Style

Mahendra Prasad; Sachin Tripathi; Keshav Dahal. An efficient feature selection based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing 2019, 87, 105980 .

AMA Style

Mahendra Prasad, Sachin Tripathi, Keshav Dahal. An efficient feature selection based Bayesian and Rough set approach for intrusion detection. Applied Soft Computing. 2019; 87 ():105980.

Chicago/Turabian Style

Mahendra Prasad; Sachin Tripathi; Keshav Dahal. 2019. "An efficient feature selection based Bayesian and Rough set approach for intrusion detection." Applied Soft Computing 87, no. : 105980.

Journal article
Published: 22 November 2019 in IEEE Internet of Things Journal
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The Internet-of-Things (IoT) has formed a whole new layer of the world built on internet, reaching every connected devices, actuators and sensors. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provides varied data streams due to the nature of the individual’s preference and versatile devices pool. The conventional single tuple expiration driven sliding window method is not adequate to provide efficient anonymization. Furthermore, minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real datasets shows that X-BAND is effective and efficient compared to famous conventional anonymization algorithm FADS. X-BAND demonstrated 5% to 11% and 1% to 3% less information loss on real dataset Adult and PM2.5 respectively while performing similar on clustering, comparable to re-using suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.

ACS Style

Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. $X-BAND$ : Expiration Band for Anonymizing Varied Data Streams. IEEE Internet of Things Journal 2019, 7, 1438 -1450.

AMA Style

Ankhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal. $X-BAND$ : Expiration Band for Anonymizing Varied Data Streams. IEEE Internet of Things Journal. 2019; 7 (2):1438-1450.

Chicago/Turabian Style

Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal. 2019. "$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams." IEEE Internet of Things Journal 7, no. 2: 1438-1450.

Journal article
Published: 01 November 2019 in Future Generation Computer Systems
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Distributed Denial of Service (DDoS) is one of the most rampant attacks in the modern Internet of Things (IoT) network infrastructures. Security plays a very vital role for an ever-growing heterogeneous network of IoT nodes, which are directly connected to each other. Due to the preliminary stage of Software Defined Networking (SDN), in the IoT network, sampling based measurement approaches currently results in low-accuracy, higher memory consumption, higher-overhead in processing and network, and low attack-detection. To deal with these aforementioned issues, this paper proposes sFlow and adaptive polling based sampling with Snort Intrusion Detection System (IDS) and deep learning based model, which helps to lower down the various types of prevalent DDoS attacks inside the IoT network. The flexible decoupling property of SDN enables us to program network devices for required parameters without utilizing third-party propriety based hardware or software. Firstly, in data-plane, to lower down processing and network overhead of switches, we deployed sFlow and adaptive polling based sampling individually. Secondly, in control-plane, to optimize detection accuracy, we deployed Snort IDS collaboratively with Stacked Autoencoders (SAE) deep learning model. Furthermore, after applying performance metrics on collected traffic streams, we quantitatively investigate trade off among attack detection accuracy and resources overhead. The evaluation of the proposed system demonstrates higher detection accuracy with 95% of True Positive rate with less than 4% of False Positive rate within sFlow based implementation compared to adaptive polling.

ACS Style

Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Ali Kashif Bashir; Rao Mumtaz; J. González. Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Generation Computer Systems 2019, 111, 763 -779.

AMA Style

Raja Majid Ali Ujjan, Zeeshan Pervez, Keshav Dahal, Ali Kashif Bashir, Rao Mumtaz, J. González. Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN. Future Generation Computer Systems. 2019; 111 ():763-779.

Chicago/Turabian Style

Raja Majid Ali Ujjan; Zeeshan Pervez; Keshav Dahal; Ali Kashif Bashir; Rao Mumtaz; J. González. 2019. "Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN." Future Generation Computer Systems 111, no. : 763-779.

Research article
Published: 01 September 2019 in IET Networks
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Life in modern society becomes easier due to the rapid growth of different technologies like real-time analytic, ubiquitous wireless communication, commodity sensors, machine learning and embedded systems. Nowadays, there seems to be a need to merge these technologies in the form of Internet of Things (IoT) so that smart systems can be achieved. On the other hand, cloud computing is a pillar in IoT by which end users get connected through the cloud servers for getting different services. However, to recognise the legitimacy of communicators during communication sessions through insecure channels like the Internet, serious issues in cloud-based IoT applications need to be addressed. Thus, authentication procedure is highly desirable to remove the unapproved access in IoT applications. This study presents an ElGamal cryptosystem and biometric information along with a user's password-based authentication scheme for cloud-based IoT applications refereed as SAS-Cloud. Security of the proposed scheme has been analysed by well popular random oracle model and it is found that SAS-Cloud has the ability to defend all the possible attacks. Furthermore, the performance of SAS-Cloud has been evaluated and it was found that SAS-Cloud has better efficiency than other existing competing ElGamal cryptosystem-based authentication schemes.

ACS Style

Tanmoy Maitra; Mohammad S. Obaidat; Debasis Giri; Subrata Dutta; Keshav Dahal. ElGamal cryptosystem‐based secure authentication system for cloud‐based IoT applications. IET Networks 2019, 8, 289 -298.

AMA Style

Tanmoy Maitra, Mohammad S. Obaidat, Debasis Giri, Subrata Dutta, Keshav Dahal. ElGamal cryptosystem‐based secure authentication system for cloud‐based IoT applications. IET Networks. 2019; 8 (5):289-298.

Chicago/Turabian Style

Tanmoy Maitra; Mohammad S. Obaidat; Debasis Giri; Subrata Dutta; Keshav Dahal. 2019. "ElGamal cryptosystem‐based secure authentication system for cloud‐based IoT applications." IET Networks 8, no. 5: 289-298.

Journal article
Published: 24 July 2019 in Applied Sciences
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Identity management (IdM) is a method used to determine user identities. The centralized aspect of IdM introduces a serious concern with the growing value of personal information, as well as with the General Data Protection Regulation (GDPR). The problem with currently-deployed systems and their dominating approach, with identity providers (IdP) and single-point services, is that a third party is in charge of maintaining and controlling the personal data. The main challenge to manage data securely lies in trusting humans and institutes who are responsible for controlling the entire activity. Identities are not owned by the rightful owners or the user him/herself, but by the mentioned providers. With the rise of blockchain technology, self-sovereign identities are in place utilizing decentralization; unfortunately, the flaws still exist. In this research, we propose DNS-IdM, a smart contract-based identity management system that enables users to maintain their identities associated with certain attributes, accomplishing the self-sovereign concept. DNS-IdM has promising outcomes in terms of security and privacy. Due to the decentralized nature, DNS-IdM is able to avoid not only the conventional security threats, but also the limitations of the current decentralized identity management systems.

ACS Style

Jamila Alsayed Kassem; Sarwar Sayeed; Hector Marco-Gisbert; Zeeshan Pervez; Keshav Dahal. DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network. Applied Sciences 2019, 9, 2953 .

AMA Style

Jamila Alsayed Kassem, Sarwar Sayeed, Hector Marco-Gisbert, Zeeshan Pervez, Keshav Dahal. DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network. Applied Sciences. 2019; 9 (15):2953.

Chicago/Turabian Style

Jamila Alsayed Kassem; Sarwar Sayeed; Hector Marco-Gisbert; Zeeshan Pervez; Keshav Dahal. 2019. "DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network." Applied Sciences 9, no. 15: 2953.

Research article
Published: 16 July 2019 in IET Image Processing
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Salient object detection (SOD) has been attracting a lot of interest, and recently many computational models have been developed. Here, the authors formulate a SOD model, in which saliency map is computed as a combination of the colour, its distribution-based saliency and orientation saliency. Similar to traditional SODs, the proposed method is based on super-pixel segmentation and super-pixel utilises both colour and its distribution-based saliency to generate a coarse saliency map. However, distinct from traditional SODs, authors further use orientation contrast to optimise the coarse saliency map to obtain an improved saliency map. Authors’ contributions are twofold. First, the authors combine colour uniqueness and its distribution with local orientation information (LOI) used in Itti's model to effectively improve profiles of salient regions. Second, a reciprocal function is defined to substitute the Gabor function used in LOI, and the authors have proved that the substitution could detect relatively homogeneous and uniform regions at the boundary of salient object, whereas it is what the traditional models lack. Authors’ approach significantly outperforms state-of-the-art methods on four benchmark datasets, while the authors demonstrate that the proposed method runs as fast as most existing algorithms.

ACS Style

Wenbing Chen; Keshav Dahal; Shuxian Huang. Salient object detection via reciprocal function filter. IET Image Processing 2019, 13, 1616 -1624.

AMA Style

Wenbing Chen, Keshav Dahal, Shuxian Huang. Salient object detection via reciprocal function filter. IET Image Processing. 2019; 13 (10):1616-1624.

Chicago/Turabian Style

Wenbing Chen; Keshav Dahal; Shuxian Huang. 2019. "Salient object detection via reciprocal function filter." IET Image Processing 13, no. 10: 1616-1624.

Conference paper
Published: 01 December 2018 in 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)
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In the last decade, e-commerce has been grown rapidly and become a familiar tool of shopping for many people. However, some people still have concerns while making online purchases due to its uncertain attributes. In fact, there are many online consumers have suffered from monetary loose problem due to some reasons which the lack of the trust in e-commerce is one of them. Therefore, there is a great demand for a mechanism that helps to evaluate the trust throughout the online transactions. One of them is the existing mechanism of the trust management which is used in some e-commerce websites (e.g. eBay). Such a mechanism evaluates the trust by computing a trust value of any seller only based on the previous rating of the past transactions. Therefore, the trust value is only able to show the general status of the trust without taking into the account the new transaction. Consequently, there is a great possibility for the frauds to be committed by some of the malicious people. For example, some of them can easily build a good reputation by making many transactions by selling cheap products with good qualities and start to commit frauds by selling more expensive products. This kind of frauds is named by [1] as the value imbalance problem. Therefore, there is a great demand for a trust evaluation mechanism which consider the new transaction as well as the past transactions. In this paper, we propose a new method which considers three dimensions that play important roles in any online transaction to help the buyers to detect the frauds. This method measures the similarity between the new transaction and the past transactions in the products types dimension, the number of the products sold dimension and the transactions amounts dimension.

ACS Style

Nasser Alsharif; Keshav Dahal; Zeeshan Pervez; Pradorn Sureephong. Multi-Dimensional E-commerce Trust Evaluation Method. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) 2018, 1 -7.

AMA Style

Nasser Alsharif, Keshav Dahal, Zeeshan Pervez, Pradorn Sureephong. Multi-Dimensional E-commerce Trust Evaluation Method. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). 2018; ():1-7.

Chicago/Turabian Style

Nasser Alsharif; Keshav Dahal; Zeeshan Pervez; Pradorn Sureephong. 2018. "Multi-Dimensional E-commerce Trust Evaluation Method." 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) , no. : 1-7.

Conference paper
Published: 01 December 2018 in 2018 IEEE Global Communications Conference (GLOBECOM)
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Providing location recommendations has become an essential feature for location-based social networks (LBSNs), as it helps the users to explore new places and makes LBSNs more prevalent to them. Existing studies mostly focus on introducing the new features that affect users' check-in behaviours in LBSNs. However, despite the difference in the type of the features exploited, they mostly follow the same principle - characterizing dependencies between the probability of a user visiting a point-of-interest (POI) and each feature separately. The decision of a user on where to go in an LBSN, however, is driven by multiple features that act simultaneously. On the other hand, applying a full model which considers all the features jointly suffers from overfitting, as for each user there is limited available data. In this paper, we propose an intermediate solution by fragmenting the model into multiple partial models which each takes the subset of the features as the input. The proposed approach focuses on building the personalized partial models (PRMs) which are further combined by applying an additive approach. Experiments on two datasets from Foursquare show that our proposed method outperforms the state-of-the-art approaches in POI recommendation.

ACS Style

Elahe Naserianhanzaei; Xinheng Wang; Keshav Dahal. APPR: Additive Personalized Point-of-Interest Recommendation. 2018 IEEE Global Communications Conference (GLOBECOM) 2018, 1 -7.

AMA Style

Elahe Naserianhanzaei, Xinheng Wang, Keshav Dahal. APPR: Additive Personalized Point-of-Interest Recommendation. 2018 IEEE Global Communications Conference (GLOBECOM). 2018; ():1-7.

Chicago/Turabian Style

Elahe Naserianhanzaei; Xinheng Wang; Keshav Dahal. 2018. "APPR: Additive Personalized Point-of-Interest Recommendation." 2018 IEEE Global Communications Conference (GLOBECOM) , no. : 1-7.

Conference paper
Published: 01 December 2018 in 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)
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Spontaneous response after any kind of disaster is essential to rescue victims at the disaster-affected regions. Efficient and effective relief logistics scheduling is crucial to reduce the disaster impact on the people in the affected areas. Such scheduling plan remains challenging in the field of relief logistics and related study areas because of the constraints such as time, cost, priorities and limited resources. Also, the nature of requirements of victims changes dynamically that makes the scheduling task more challenging. This leads to formulate a distinctive relief logistics scheduling covering disaster regions requirement and priorities. With the limited availability of the vehicles, the scheduling needs to be planned in iterative manner with the multiple time slots based on the availability of the vehicles. A greedy heuristic with priority based search generates the optimal set of scheduling sequences in the iterative time-slot scenarios. Covering priority aspect gives an effective alternative for the rational scheduling of the relief logistics. In this paper, we focus especially on an optimal weighted priority relief logistics scheduling model based on different priorities with least dependency on the choice of scheduling sequence. The resulting model is appropriate for relief logistics scheduling in the early phase of disaster response. The simulated result shows that the weighted priority relief logistics scheduling model generates comparatively better scheduling schedule in comparison with sequences generated by applying the priorities individually.

ACS Style

Bhupesh Kumar Mishra; Tek Narayan Adhikari; Keshav Dahal; Zeeshan Pervez. Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) 2018, 1 -8.

AMA Style

Bhupesh Kumar Mishra, Tek Narayan Adhikari, Keshav Dahal, Zeeshan Pervez. Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search. 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). 2018; ():1-8.

Chicago/Turabian Style

Bhupesh Kumar Mishra; Tek Narayan Adhikari; Keshav Dahal; Zeeshan Pervez. 2018. "Priority-Index Based Multi-Priority Relief Logistics Scheduling with Greedy Heuristic Search." 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) , no. : 1-8.

Conference paper
Published: 01 December 2018 in 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)
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In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption.

ACS Style

Hussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal; Baraq Ghaleb. The RPL Load Balancing in IoT Network with Burst Traffic Scenarios. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) 2018, 1 -7.

AMA Style

Hussien Saleh Altwassi, Zeeshan Pervez, Keshav Dahal, Baraq Ghaleb. The RPL Load Balancing in IoT Network with Burst Traffic Scenarios. 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). 2018; ():1-7.

Chicago/Turabian Style

Hussien Saleh Altwassi; Zeeshan Pervez; Keshav Dahal; Baraq Ghaleb. 2018. "The RPL Load Balancing in IoT Network with Burst Traffic Scenarios." 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) , no. : 1-7.

Journal article
Published: 24 September 2018 in IEEE Systems Journal
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Localization of sensor nodes is one of the important issues in wireless sensor networks (WSNs). The location of a node can be used as the location of the occurrence of an event. Error handling and scalability are key research issues that need to be taken care of while estimating the efficiency of any localization algorithm. In this paper, we propose an approach of error correction mechanism in addition to the minimization of error in multihop system (MEMHS) for the localization algorithm. The MEMHS algorithm deals with a scalable error correction of a multiliterate localization process using a few geographical positioning system enabled nodes. The MEMHS authors assumed that an error propagates linearly and is equal in any direction. In the present study, the authors show that an error propagates nonlinearly with respect to the hop count, and the magnitude of error (X-coordinate or Y-coordinate) depends on the direction of equator lines. This paper proposes a modified algorithm of MEMHS. Furthermore, an optimum deployment strategy is introduced so that maximum number of sensor nodes can be localized. By analyzing the proposed algorithm in comparison with the MEMHS algorithm, it is found that the proposed algorithm has a better performance in terms of error correction.

ACS Style

Subrata Dutta; Mohammad S. Obaidat; Keshav Dahal; Debasis Giri; Sarmistha Neogy. M-MEMHS: Modified Minimization of Error in Multihop System for Localization of Unknown Sensor Nodes. IEEE Systems Journal 2018, 13, 215 -225.

AMA Style

Subrata Dutta, Mohammad S. Obaidat, Keshav Dahal, Debasis Giri, Sarmistha Neogy. M-MEMHS: Modified Minimization of Error in Multihop System for Localization of Unknown Sensor Nodes. IEEE Systems Journal. 2018; 13 (1):215-225.

Chicago/Turabian Style

Subrata Dutta; Mohammad S. Obaidat; Keshav Dahal; Debasis Giri; Sarmistha Neogy. 2018. "M-MEMHS: Modified Minimization of Error in Multihop System for Localization of Unknown Sensor Nodes." IEEE Systems Journal 13, no. 1: 215-225.

Journal article
Published: 03 August 2018 in Information Sciences
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The Internet-of-Things (IoT) produces and transmits enormous amounts of data. Extracting valuable information from this enormous volume of data has become an important consideration for businesses and research. However, extracting information from this data without providing privacy protection puts individuals at risk. Data has to be sanitized before use, and anonymization provides solution to this problem. Since, IoT is a collection of numerous different devices, data streams from these devices tend to vary over time thus creating varied data streams. However, implementing traditional data stream anonymization approaches only provide privacy protection for data streams that have predefined and fixed attributes. Therefore, conventional methods cannot directly work on varied data streams. In this work, we propose K-VARP (K-anonymity for VARied data stream via Partitioning) to publish varied data streams. K-VARP reads the tuple and assigns them to partitions based on description, and all tuples must be anonymized before expiring. It tries to anonymize expiring tuple within a partition if its partition is eligible to produce a K-anonymous cluster. Otherwise, partition merging is applied. In K-VARP we propose a new merging criterion called R-likeness to measure similarity distance between tuple and partitions. Moreover, flexible re-using and imputation free-publication is implied in K-VARP to achieve better anonymization quality and performance. Our experiments on a real datasets show that K-VARP is efficient and effective compared to existing algorithms. K-VARP demonstrated approximately three to nine and ten to twenty percent less information loss on two real datasets, while forming a similar number of clusters within a comparable computation time.

ACS Style

Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal; Steve Eager. K-VARP: K-anonymity for varied data streams via partitioning. Information Sciences 2018, 467, 238 -255.

AMA Style

Ankhbayar Otgonbayar, Zeeshan Pervez, Keshav Dahal, Steve Eager. K-VARP: K-anonymity for varied data streams via partitioning. Information Sciences. 2018; 467 ():238-255.

Chicago/Turabian Style

Ankhbayar Otgonbayar; Zeeshan Pervez; Keshav Dahal; Steve Eager. 2018. "K-VARP: K-anonymity for varied data streams via partitioning." Information Sciences 467, no. : 238-255.