Outputs (682)

BCFR: Blockchain-based Controller Against False Flow Rule Injection in SDN (2020)
Presentation / Conference Contribution
Boukria, S., Guerroumi, M., & Romdhani, I. (2020). BCFR: Blockchain-based Controller Against False Flow Rule Injection in SDN. In 2019 IEEE Symposium on Computers and Communications (ISCC) (1034-1039). https://doi.org/10.1109/ISCC47284.2019.8969780

Software Defined Networking (SDN) technology increases the evolution of Internet and network development. SDN, with its logical centralization of controllers and global network overview changes the network's characteristics, on term of flexibility, a... Read More about BCFR: Blockchain-based Controller Against False Flow Rule Injection in SDN.

Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection (2020)
Journal Article
Tian, Z., Shi, W., Tan, Z., Qiu, J., Sun, Y., Jiang, F., & Liu, Y. (in press). Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection. Mobile Networks and Applications, https://doi.org/10.1007/s11036-020-01656-7

Organizations' own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for ma... Read More about Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection.

Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method (2019)
Presentation / Conference Contribution
Ou, W., Deng, M., Luo, E., Shi, W., Tan, Z., & Bhuiyan, M. (2019, July). Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method. Presented at The 2019 IEEE International Conference on Cyber Physical and Social Computing (CPSCom-2019), Atlanta, USA

Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time co... Read More about Multi-miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method.

Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node (2019)
Presentation / Conference Contribution
Thomson, C., Wadhaj, I., Tan, Z., & Al-Dubai, A. (2019, August). Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node. Presented at 2019 IEEE International Conference on Smart Internet of Things, Tianjin, China

In Wireless Sensor Networks (WSNs) the use of Mobile Sink Nodes (MSNs) has been proposed in order to negate the ”hotspot” issue. This where nodes closest to the sink node shall run out of energy fastest, affecting network lifetime. However, in using... Read More about Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node.

FRCA: A Novel Flexible Routing Computing Approach for Wireless Sensor Networks (2019)
Journal Article
Liu, P., Wang, X., Hawbani, A., Busaileh, O., Zhao, L., & Al-Dubai, A. (2020). FRCA: A Novel Flexible Routing Computing Approach for Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 19(11), 2623-2639. https://doi.org/10.1109/tmc.2019.2928805

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining desired performance due to the complicated and changeable environment situations and application requirement... Read More about FRCA: A Novel Flexible Routing Computing Approach for Wireless Sensor Networks.

A Novel Trust Evaluation Process for Secure Localization using a Decentralized Blockchain in Wireless Sensor Networks (2019)
Journal Article
Kim, T., Goyat, R., Rai, M. K., Kumar, G., Buchanan, W. J., Saha, R., & Thomas, R. (2019). A Novel Trust Evaluation Process for Secure Localization using a Decentralized Blockchain in Wireless Sensor Networks. IEEE Access, 7, 184133-184144. https://doi.org/10.1109/access.2019.2960609

In this research paper, blockchain-based trust management model is proposed to enhance trust relationship among beacon nodes and to eradicate malicious nodes in Wireless Sensor Networks (WSNs). This composite trust evaluation involves behavioral-base... Read More about A Novel Trust Evaluation Process for Secure Localization using a Decentralized Blockchain in Wireless Sensor Networks.

Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme (2019)
Presentation / Conference Contribution
Babaagba, K. O., Tan, Z., & Hart, E. (2019, November). Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme. Presented at The 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019), Guangzhou, China

The ability to detect metamorphic malware has generated significant research interest over recent years, particularly given its proliferation on mobile devices. Such malware is particularly hard to detect via signature-based intrusion detection syste... Read More about Nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

PTAOD: A Novel Framework For Supporting Approximate Outlier Detection over Streaming Data for Edge Computing (2019)
Journal Article
Zhu, R., Yu, T., Tan, Z., Du, W., Zhao, L., Li, J., & Xia, X. (2020). PTAOD: A Novel Framework For Supporting Approximate Outlier Detection over Streaming Data for Edge Computing. IEEE Access, 8, 1475-1485. https://doi.org/10.1109/ACCESS.2019.2962066

Outlier detection over sliding window is a fundamental problem in the domain of streaming data management, which has been studied over 10 years. The key of supporting outlier detection is to construct a neighbour-list for each object. It is used for... Read More about PTAOD: A Novel Framework For Supporting Approximate Outlier Detection over Streaming Data for Edge Computing.

A Lightweight and Efficient Digital Image Encryption Using Hybrid Chaotic Systems for Wireless Network Applications (2019)
Journal Article
Almalkawi, I. T., Halloush, R., Alsarhan, A., Al-Dubai, A., & Al-karaki, J. N. (2019). A Lightweight and Efficient Digital Image Encryption Using Hybrid Chaotic Systems for Wireless Network Applications. Journal of Information Security and Applications, 49, https://doi.org/10.1016/j.jisa.2019.102384

Due to limited processing capabilities and other constraints of most wireless networks, many existing security algorithms do not consider the network efficiency. This is because most of these security solutions exhibit intolerable overhead and consid... Read More about A Lightweight and Efficient Digital Image Encryption Using Hybrid Chaotic Systems for Wireless Network Applications.

Routing Schemes in Software-Defined Vehicular Networks: Design, Open Issues and Challenges (2020)
Journal Article
Zhao, L., Al-Dubai, A., Zomaya, A. Y., Min, G., Hawbani, A., & Li, J. (2021). Routing Schemes in Software-Defined Vehicular Networks: Design, Open Issues and Challenges. IEEE Intelligent Transportation Systems Magazine, 13(4), 217-226. https://doi.org/10.1109/MITS.2019.2953557

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing sche... Read More about Routing Schemes in Software-Defined Vehicular Networks: Design, Open Issues and Challenges.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2018)
Journal Article
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), Article 2383. https://doi.org/10.1038/s41467-018-04316-3

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-... Read More about Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks (2017)
Journal Article
Torres Vega, M., Koonen, A. M. J., Liotta, A., & Famaey, J. (2018). Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks. IEEE Wireless Communications Letters, 7(2), 278-281. https://doi.org/10.1109/lwc.2017.2771771

In light of the extreme radio congestion, the time has come to consider the upper parts of the electromagnetic spectrum. Optical beam-steered wireless communications offer great potential for future indoor short-range connectivity, due to virtually u... Read More about Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks.

Self-Learning Power Control in Wireless Sensor Networks (2018)
Journal Article
Chincoli, M., & Liotta, A. (2018). Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), Article 375. https://doi.org/10.3390/s18020375

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for... Read More about Self-Learning Power Control in Wireless Sensor Networks.

Spatial anomaly detection in sensor networks using neighborhood information (2016)
Journal Article
Bosman, H. H., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2017). Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 33, 41-56. https://doi.org/10.1016/j.inffus.2016.04.007

The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challen... Read More about Spatial anomaly detection in sensor networks using neighborhood information.

A topological insight into restricted Boltzmann machines (2016)
Journal Article
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016). A topological insight into restricted Boltzmann machines. Machine Learning, 104(2-3), 243-270. https://doi.org/10.1007/s10994-016-5570-z

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density esti... Read More about A topological insight into restricted Boltzmann machines.

Decentralized dynamic understanding of hidden relations in complex networks (2018)
Journal Article
Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018). Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-19356-4

Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossibl... Read More about Decentralized dynamic understanding of hidden relations in complex networks.

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines (2017)
Journal Article
Mocanu, D. C., Bou Ammar, H., Puig, L., Eaton, E., & Liotta, A. (2017). Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognition, 69, 325-335. https://doi.org/10.1016/j.patcog.2017.04.017

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dime... Read More about Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines.

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks (2019)
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserv... Read More about Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks.

PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme (2019)
Journal Article
Khan, R., Zakarya, M., Tan, Z., Usman, M., Jan, M. A., & Khan, M. (2019). PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme. International Journal of Communication Systems, 32(18), Article e4144. https://doi.org/10.1002/dac.4144

Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion a... Read More about PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme.

Double-Arc Parallel Coordinates and its Axes re-Ordering Methods (2020)
Journal Article
Lu, L., Wang, W., & Tan, Z. (2020). Double-Arc Parallel Coordinates and its Axes re-Ordering Methods. Mobile Networks and Applications, 25(4), 1376-1391. https://doi.org/10.1007/s11036-019-01455-9

The Parallel Coordinates Plot (PCP) is a popular technique for the exploration of high-dimensional data. In many cases, researchers apply it as an effective method to analyze and mine data. However, when today's data volume is getting larger, visual... Read More about Double-Arc Parallel Coordinates and its Axes re-Ordering Methods.