All Outputs (44)

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..