Outputs (40)

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. (2024)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., PIRAS, L. and AL-KADRI, M.O. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of information security and applications [online], 82, article number 103741. Available from: https://doi.org/10.1016/j.jisa.2024.103741

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to redu... Read More about Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI..

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics. (2023)
Journal Article
SRIVASTAVA, G., MEKALA, M.S., HAJAR, M.S. and KALUTARAGE, H. 2023. C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics. IEEE transactions on consumer electronics [online], Early Access. Available from: https://doi.org/10.1109/TCE.2023.3342635

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is t... Read More about C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics..

3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks. (2023)
Journal Article
HAJAR, M.S., KALUTARAGE, H.K. and AL-KADRI, M.O. 2023. 3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks. Computer networks [online], 237, article number 110073. Available from: https://doi.org/10.1016/j.comnet.2023.110073

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraint... Read More about 3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks..

Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and MADZUDZO, G. 2023. Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. Journal of information security and applications [online], 77, article number 103570. Available from: https://doi.org/10.1016/j.jisa.2023.103570

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate... Read More about Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system..

Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. (2023)
Journal Article
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2023. Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. Computer and security [online], 133, article 103388. Available from: https://doi.org/10.1016/j.cose.2023.103388

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenge... Read More about Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring..

Android code vulnerabilities early detection using AI-powered ACVED plugin. (2023)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android code vulnerabilities early detection using AI-powered ACVED plugin. In Atluri, V. and Ferrara, A.L. (eds.) Data and applications security and privacy XXXVII; proceedings of the 37th annual IFIP WG (International Federation for Information Processing Working Group) 11.3 Data and applications security and privacy 2023 (DBSec 2023), 19-21 July 2023, Sophia-Antipolis, France. Lecture notes in computer science (LNCS), 13942. Cham: Springer [online], pages 339-357. Available from: https://doi.org/10.1007/978-3-031-37586-6_20

During Android application development, ensuring adequate security is a crucial and intricate aspect. However, many applications are released without adequate security measures due to the lack of vulnerability identification and code verification at... Read More about Android code vulnerabilities early detection using AI-powered ACVED plugin..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. (2023)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. In De Capitani di Vimercati, S. and Samarati, P. (eds.) Proceedings of the 20th International conference on security and cryptography, 10-12 July 2023, Rome, Italy, volume 1. Setúbal: SciTePress [online], pages 659-666. Available from: https://doi.org/10.5220/0012060400003555

Ensuring the security of Android applications is a vital and intricate aspect requiring careful consideration during development. Unfortunately, many apps are published without sufficient security measures, possibly due to a lack of early vulnerabili... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models..

AI-powered vulnerability detection for secure source code development. (2023)
Conference Proceeding
RAJAPAKSHA, S., SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2023. AI-powered vulnerability detection for secure source code development. In Bella, G., Doinea, M. and Janicke, H. (eds.) Innovative security solutions for information technology and communications: revised selected papers of the 15th International conference on Security for information technology and communications 2022 (SecITC 2022), 8-9 December 2022, [virtual conference]. Lecture notes in computer sciences, 13809. Cham: Springer [online], pages 275-288. Available from: https://doi.org/10.1007/978-3-031-32636-3_16

Vulnerable source code in software applications is causing paramount reliability and security issues. Software security principles should be integrated to reduce these issues at the early stages of the development lifecycle. Artificial Intelligence (... Read More about AI-powered vulnerability detection for secure source code development..

AI-based intrusion detection systems for in-vehicle networks: a survey. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A., MADZUDZO, G. and CHEAH, M. 2023. Al-based intrusion detection systems for in-vehicle networks: a survey. ACM computing survey [online], 55(11), article no. 237, pages 1-40. Available from: https://doi.org/10.1145/3570954

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to nume... Read More about AI-based intrusion detection systems for in-vehicle networks: a survey..

DQR: a double Q learning multi agent routing protocol for wireless medical sensor network. (2023)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2023. DQR: a double Q learning multi agent routing protocol for wireless medical sensor network. In Li, F., Liang, K., Lin, Z. and Katsikas, S.K. (eds.) Security and privacy in communication networks: proceedings of the 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022), 17-19 October 2022, Kansas City, USA. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 462. Cham: Springer [online], pages 611-629. Available from: https://doi.org/10.1007/978-3-031-25538-0_32

Wireless Medical Sensor Network (WMSN) offers innovative solutions in the healthcare domain. It alleviates the patients' everyday life difficulties and supports the already overloaded medical staff with continuous monitoring tools. However, widesprea... Read More about DQR: a double Q learning multi agent routing protocol for wireless medical sensor network..

Android source code vulnerability detection: a systematic literature review. (2023)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android source code vulnerability detection: a systematic literature review. ACM computing surveys [online], 55(9), article 187, pages 1-37. Available from: https://doi.org/10.1145/3556974

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not add... Read More about Android source code vulnerability detection: a systematic literature review..

RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks. (2023)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2023. RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks. In Proceedings of the 20th IEEE (Institute of Electrical and Electronics Engineers) Consumer communications and networking conference 2023 (CCNC 2023), 8-11 January 2023, Las Vegas, USA. Piscataway: IEEE [online], pages 781-789. Available from: https://doi.org/10.1109/CCNC51644.2023.10060225

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widesp... Read More about RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks..

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. (2022)
Thesis
HAJAR, M.S. 2022. A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987863

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraint... Read More about A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks..

Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. (2022)
Thesis
ZAKARIYYA, I. 2022. Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987917

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection... Read More about Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring..

Resource efficient federated deep learning for IoT security monitoring. (2022)
Conference Proceeding
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2022. Resource efficient federated deep learning for IoT security monitoring. In Li, W., Furnell, S. and Meng, W. (eds.) Attacks and defenses for the Internet-of-Things: revised selected papers from the 5th International workshop on Attacks and defenses for Internet-of-Things 2022 (ADIoT 2022), in conjunction with 27th European symposium on research in computer security 2022 (ESORICS 2022) 29-30 Septempber 2022, Copenhagen, Denmark. Lecture notes in computer science (LNCS), 13745. Cham: Springer [online], pages 122-142. Available from: https://doi.org/10.1007/978-3-031-21311-3_6

Federated Learning (FL) uses a distributed Machine Learning (ML) concept to build a global model using multiple local models trained on distributed edge devices. A disadvantage of the FL paradigm is the requirement of many communication rounds before... Read More about Resource efficient federated deep learning for IoT security monitoring..

A robust exploration strategy in reinforcement learning based on temporal difference error. (2022)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2022. A robust exploration strategy in reinforcement learning based on temporal difference error. In Aziz, H., Corrêa, D. and French, T. (eds.) AI 2022: advances in artificial intelligence; proceedings of the 35th Australasian joint conference 2022 (AI 2022), 5-8 December 2022, Perth, Australia. Lecture notes in computer science (LNCS), 13728. Cham: Springer [online], pages 789-799. Available from: https://doi.org/10.1007/978-3-031-22695-3_55

Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation trade-off is still a fundamental dilemma in reinforcement learning. The learning agent needs to learn how to deal with a stochastic environment in orde... Read More about A robust exploration strategy in reinforcement learning based on temporal difference error..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset] (2022)
Dataset
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models [Dataset]. Hosted on GitHub (online). Available from: https://github.com/softwaresec-labs/LVDAndro

Many of the Android apps get published without appropriate security considerations, possibly due to not verifying code or not identifying vulnerabilities at the early stages of development. This can be overcome by using an AI based model trained on a... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset].

Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. (2022)
Conference Proceeding
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., MADZUDZO, G. and PETROVSKI, A.V. 2022. Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. In Jančárková, T., Visky, G. and Winther, I. (eds.). Proceedings of 14th International conference on Cyber conflict 2022 (CyCon 2022): keep moving, 31 May - 3 June 2022, Tallinn, Estonia. Tallinn: CCDCOE, pages 309-330. Hosted on IEEE Xplore [online]. Available from: https://doi.org/10.23919/CyCon55549.2022.9811048

The growth of information technologies has driven the development of the transportation sector, including connected and autonomous vehicles. Due to its communication capabilities, the controller area network (CAN) is the most widely used in-vehicle c... Read More about Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security..

Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks. (2022)
Conference Proceeding
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2022. Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks. In CPPS '22: proceedings of the 8th ACM (Association for Computing Machinery) Cyber-physical system security workshop 2022 (CPSS '22), co-located with the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIACCS '22) Nagasaki, Japan (virtual event). New York: ACM [online], pages 41-51. Available from: https://doi.org/10.1145/3494107.3522772

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection... Read More about Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks..

Developing secured android applications by mitigating code vulnerabilities with machine learning. (2022)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2022. Developing secured android applications by mitigating code vulnerabilities with machine learning. In ASIA CCS '22: proceedings of the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIA CCS 2022), 30 May - 3 June 2022, Nagasaki, Japan. New York: ACM [online], pages 1255-1257. Available from: https://doi.org/10.1145/3488932.3527290

Mobile application developers sometimes might not be serious about source code security and publish apps to the marketplaces. Therefore, it is essential to have a fully automated security solutions generator to integrate security-by-design into the d... Read More about Developing secured android applications by mitigating code vulnerabilities with machine learning..

AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021) (2021)
Conference Proceeding
SANI, S. and KALUTARAGE, H. (eds.) 2021. AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-3125/

This volume consists of the papers that were presented at the 1st International Workshop on Artificial Intelligence and Cyber Security, co-located with the 41st SGAI International Conference on Artificial Intelligence (AI-2021) on December 14th, 2021... Read More about AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021).

Improving intrusion detection through training data augmentation. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Improving intrusion detection through training data augmentation. In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 17. Available from: https://doi.org/10.1109/SIN54109.2021.9699293

Imbalanced classes in datasets are common problems often found in security data. Therefore, several strategies like class resampling and cost-sensitive training have been proposed to address it. In this paper, we propose a data augmentation strategy... Read More about Improving intrusion detection through training data augmentation..

Reasoning with counterfactual explanations for code vulnerability detection and correction. (2021)
Conference Proceeding
WIJEKOON, A. and WIRATUNGA, N. 2021. Reasoning with counterfactual explanations for code vulnerability detection and correction. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], pages 1-13. Available from: http://ceur-ws.org/Vol-3125/paper1.pdf

Counterfactual explanations highlight "actionable knowledge" which helps the end-users to understand how a machine learning outcome could be changed to a more desirable outcome. In code vulnerability detection, understanding these "actionable" correc... Read More about Reasoning with counterfactual explanations for code vulnerability detection and correction..

Memory efficient federated deep learning for intrusion detection in IoT networks. (2021)
Conference Proceeding
ZAKARIYYA, A. KALUTARAGE, H. and AL-KADRI, M.O. 2021. Memory efficient federated deep learning for intrusion detection in IoT networks. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], pages 85-99. Available from: http://ceur-ws.org/Vol-3125/paper7.pdf

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet o... Read More about Memory efficient federated deep learning for intrusion detection in IoT networks..

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..

Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. (2019)
Conference Proceeding
KALUTARAGE, H.K., AL-KADRI, M.O., CHEAH, M. and MADZUDZO, G. 2019. Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. In Hof, H.-J., Fritz, M., Kraub, C. and Wasenmüller, O. (eds.). Proceedings of 2019 Computer science in cars symposium (CSCS 2019), 8 October 2019, Kaiserslautern, Germany. New York: ACM [online], article number 7. Available from: https://doi.org/10.1145/3359999.3360496

Automotive electronics is rapidly expanding. An average vehicle contains million lines of software codes, running on 100 of electronic control units (ECUs), in supporting number of safety, driver assistance and infotainment functions. These ECUs are... Read More about Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus..

Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder. (2019)
Conference Proceeding
JIA, G., MILLER, P., HONG, X., KALUTARAGE, H. and BAN, T. 2019. Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder. In Proceedings of 18th Institution of Electrical and Electronics Engineers (IEEE) international Trust, security and privacy in computing and communications conference, co-located with 13th IEEE international Big data science and engineering conference (TrustCom/BigDataSE), 5-8 August 2019, Rotorua, New Zealand. Piscataway: IEEE [online], pages 458-465. Available from: https://doi.org/10.1109/TrustCom/BigDataSE.2019.00068

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and... Read More about Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder..

Strengthening student engagement: evaluating the role of the digital skills agenda in higher education. (2019)
Presentation / Conference
LAWANI, A., SINGH, A., MCNEIL, A., DURACK, B. and KALUTARAGE, H. 2019. Strengthening student engagement: evaluating the role of the digital skills agenda in higher education. Presented at the 2019 Department for the Enhancement of Learning, Teaching and Access (DELTA) learning and teaching conference (LTC 2019): learning without borders, 2 May 2019, Aberdeen, UK.

Digital technology can contribute to all three areas of the TEF: teaching quality; learning environment; and student outcomes (Davies S, Mullan and Feldman 2017). Digital skills are helpful in designing enhanced and effective learning activities (Cop... Read More about Strengthening student engagement: evaluating the role of the digital skills agenda in higher education..

Feature trade-off analysis for reconnaissance detection. (2018)
Book Chapter
KALUTARAGE, H.K. and SHAIKH, S.A. 2018. Feature trade-off analysis for reconnaissance detection. In Heard, N., Adams, N., Rubin-Delanchy, P. and Turcotte, M. (eds.) Data science for cyber security. Security science and technology, 3. London: World Scientific [online], chapter 5, pages 95-126. Available from: https://doi.org/10.1142/9781786345646_005

An effective cyber early warning system (CEWS) should pick up threat activity at an early stage, with an emphasis on establishing hypotheses and predictions as well as generating alerts on (unclassified) situations based on preliminary indications. T... Read More about Feature trade-off analysis for reconnaissance detection..

Towards a threat assessment framework for apps collusion. (2017)
Journal Article
KALUTARAGE, H.K., NGUYEN, H.N. and SHAIKH, S.A. 2017. Towards a threat assessment framework for apps collusion. Telecommunication systems [online], 66(3), pages 417-430. Available from: https://doi.org/10.1007/s11235-017-0296-1

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited... Read More about Towards a threat assessment framework for apps collusion..

Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks. (2015)
Journal Article
KALUTARAGE, H.K., SHAIKH, S.A., WICKRAMASINGHE, I.P., ZHOU, Q. and JAMES, A.E. 2015. Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks. Computers and electrical engineering [online], 47, pages 327-344. Available from: https://doi.org/10.1016/j.compeleceng.2015.07.007

Stealthy attackers move patiently through computer networks – taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a chall... Read More about Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks..