Outputs (46)

FedSim: similarity guided model aggregation for federated learning. (2021)
Journal Article
PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and KALUTARAGE, H. 2022. FedSim: similarity guided model aggregation for federated learning. Neurocomputing [online], 483: distributed machine learning, optimization and applications, pages 432-445. Available from: https://doi.org/10.1016/j.neucom.2021.08.141

Federated Learning (FL) is a distributed machine learning approach in which clients contribute to learning a global model in a privacy preserved manner. Effective aggregation of client models is essential to create a generalised global model. To what... Read More about FedSim: similarity guided model aggregation for federated learning..

TrustMod: a trust management module for NS-3 simulator. (2021)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2021. TrustMod: a trust management module for NS-3 simulator. In Zhao, L., Kumar, N., Hsu, R.C. and Zou, D. (eds.) Proceedings of 20th IEEE (Institute of Electrical and Electronics Engineers) International conference on Trust, security and privacy in computing and communications 2021 (IEEE TrustCom 2021), 20-21 October 2021, Shenyang, China: [virtual event]. Piscataway: IEEE [online], pages 51-60. Available from: https://doi.org/10.1109/TrustCom53373.2021.00025

Trust management offers a further level of defense against internal attacks in ad hoc networks. Deploying an effective trust management scheme can reinforce the overall network security. Regardless of limitations, however, security researchers often... Read More about TrustMod: a trust management module for NS-3 simulator..

Android mobile malware detection using machine learning: a systematic review. (2021)
Journal Article
SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2021. Android mobile malware detection using machine learning: a systematic review. Electronics [online], 10(13), article 1606. Available from: https://doi.org/10.3390/electronics10131606

With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and... Read More about Android mobile malware detection using machine learning: a systematic review..

Effective detection of cyber attack in a cyber-physical power grid system. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Effective detection of cyber attack in a cyber-physical power grid system. In Arai, K. (ed) Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021), 29-30 April 2021, Vancouver, Canada. Advances in intelligent systems and computing, 1363. Cham: Springer [online], 1, pages 812-829. Available from: https://doi.org/10.1007/978-3-030-73100-7_57

Advancement in technology and the adoption of smart devices in the operation of power grid systems have made it imperative to ensure adequate protection for the cyber-physical power grid system against cyber-attacks. This is because, contemporary cyb... Read More about Effective detection of cyber attack in a cyber-physical power grid system..

LTMS: a lightweight trust management system for wireless medical sensor networks. (2021)
Conference Proceeding
HAJAR, M.S., AL-KADRI, M.O. and KALUTARAGE, H. 2020. LTMS: a lightweight trust management system for wireless medical sensor networks. In Wang, G., Ko, R., Bhuiyan, M.Z.A. and Pan, Y. (eds.). Proceedings of 19th Institute of Electrical and Electronics Engineers (IEEE) Trust, security and privacy in computing and communication international conference 2020 (TrustCom 2020), 29 Dec 2020 - 1 Jan 2021, Guangzhou, China. Piscataway: IEEE [online], pages 1783-1790. Available from: https://doi.org/10.1109/TrustCom50675.2020.00245

Wireless Medical Sensor Networks (WMSNs) offer ubiquitous health applications that enhance patients' quality of life and support national health systems. Detecting internal attacks on WMSNs is still challenging since cryptographic measures can not pr... Read More about LTMS: a lightweight trust management system for wireless medical sensor networks..

A survey on wireless body area networks: architecture, security challenges and research opportunities. (2021)
Journal Article
HAJAR, M.S., AL-KADRI, M.O. and KALUTARAGE, H.K. 2021. A survey on wireless body area networks: architecture, security challenges and research opportunities. Computers and security [online], 104, article ID 102211. Available from: https://doi.org/10.1016/j.cose.2021.102211

In the era of communication technologies, wireless healthcare networks enable innovative applications to enhance the quality of patients’ lives, provide useful monitoring tools for caregivers, and allows timely intervention. However, due to the sensi... Read More about A survey on wireless body area networks: architecture, security challenges and research opportunities..

Resource efficient boosting method for IoT security monitoring. (2021)
Conference Proceeding
ZAKARIYYA, I., AL-KADRI, M.O. and KALUTARAGE, H. 2021. Resource efficient boosting method for IoT security monitoring. In Proceedings of 18th Institute of Electrical and Electronics Engineers (IEEE) Consumer communications and networking conference 2021 (CCNC 2021), 9-12 January 2021, [virtual conference]. Piscataway: IEEE [online], article 9369620. Available from: https://doi.org/10.1109/ccnc49032.2021.9369620

Machine learning (ML) methods are widely proposed for security monitoring of Internet of Things (IoT). However, these methods can be computationally expensive for resource constraint IoT devices. This paper proposes an optimized resource efficient ML... Read More about Resource efficient boosting method for IoT security monitoring..

Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. (2020)
Journal Article
WICKRAMASINGHE, I. and KALUTARAGE, H. 2021. Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft computing [online], 25(3), pages 2277-2293. Available from: https://doi.org/10.1007/s00500-020-05297-6

Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications, ranging from product recommendations through medical diagnosis to controlling autonomous v... Read More about Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation..

ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. (2020)
Conference Proceeding
HAJAR, M.S., AL-KADRI, M.O. and KALUTARAGE, H. 2020. ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) Communications and network security conference (CNS 2020), 29 June - 1 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9162325. Available from: https://doi.org/10.1109/CNS48642.2020.9162325

Wireless Medical Sensor Networks (WMSN) will play a significant role in the advancements of modern healthcare applications. Security concerns are still the main obstacle to the widespread adoption of this technology. Conventional security approaches,... Read More about ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks..

Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. (2019)
Conference Proceeding
ZAKARIYYA, I., AL-KADRI, M.O., KALUTARGE, H. and PETROVSKI, A. 2019. Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. In Obaidat, M. and Samarati, P. (eds.) Proceedings of the 16th International security and cryptography conference (SECRYPT 2019), co-located with the 16th International joint conference on e-business and telecommunications (ICETE 2019), 26-28 July 2019, Prague, Czech Republic. Setúbal, Portugal: SciTePress [online], 2, pages 523-528. Available from: https://doi.org/10.5220/0008119205230528.

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to i... Read More about Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm..