Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the workload of the cloud. We also propose a multi attack detection mechanism called LocKedge Low Complexity Cyberattack Detection in IoT Edge Computing, which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.
University of Murcia（穆尔西亚大学）；University of Zurich UZH（苏黎世大学（UZH））；armasuisse Science & Technology（瑞士国防科技与采购局科技处）
Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated learning (FL) addresses these limitations by creating federated and privacy-preserving models. However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data fal…
As the internet penetration rate in Africa increases, so does the proliferation of the Internet of Things (IoT) devices. Along with this growth in internet access is the risk of cyberattacks to vulnerable IoT devices mushrooming in the African cyberspace. One way to determine IoT vulnerabilities is to find open ports within Africa s cyberspace. Our research leverages Shodan search engine, a powerful tool for discovering IoT devices facing the public internet, to find open ports across Africa. We conduct an analysis of our findings, ranking countries from most to least vulnerable to cyberattack…
This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation o…
The Ukraine power grid cyberattacks remind us that the smart Internet of Things (IoT) can help us control our light-bulbs, but if under attacks it might also take us into darkness. Nowadays, many literatures have tried to address the concerns on IoT security, but few of them take into consideration the sever threats to IoT coming from the advances of quantum computing. As a promising candidate for the future post-quantum cryptography standard, lattice-based cryptography enjoys the advantages of strong security guarantees and high efficiency, which make it extremely suitable for IoT application…
Internet of Things (IoT) networks have become an increasingly attractive target of cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to implement network intrusion detection systems to protect IoT networks. For the successful training of such ML models, selecting the right data features is crucial, maximising the detection accuracy and computational efficiency. This paper comprehensively analyses feature sets' importance and predictive power for detecting network attacks. Three feature selection algorithms: chi-square, information gain and correlation, have been ut…
Joint Stock "Research and production company "Kryptonite"（“氪石”科研生产联合股份公司）
Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions…
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction
Badji Mokhtar-Annaba University（巴吉·穆克塔尔-安纳巴大学）；University of La Rochelle（拉罗谢尔大学）；National Higher School of Technology and Engineering -Annaba（安纳巴国家技术与工程高等学校）
Unknown Attack Detection in IoT Networks using Large Language Models: A Robust, Data-efficient Approach
University of Ottawa（渥太华大学）；School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气与计算机工程学院）；Research Ireland Lero Centre, University of Limerick（利默里克大学爱尔兰研究Lero中心）
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究院）
Interaction between devices, people, and the Internet has given birth to a new digital communication model, the Internet of Things (IoT). The seamless network of these smart devices is the core of this IoT model. However, on the other hand, integrating smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch an attack to compromise the devices using malware proliferation techniques. Therefore, malware detection is considered a lifeline for the survival of IoT devices against cyb…
Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of attackers is shown to be effective against a wide variety of attack types. Previous works, however, focus solely on anomalies in network traffic to detect bots and botnet. In this work we take a more robust approach of analyzing the heterogeneous events including network traffic, file download events, SSH logins and chain of commands input by attack…
The 5G network would fuel next-gen, bandwidth-heavy technologies such as automation, IoT, and AI on the factory floor. It will improve efficiency by powering AR overlays in workflows, as well as ensure safer practices and reduce the number of defects through predictive analytics and real-time detection of damage. The Dynamic Spectrum Sharing (DSS) in 5G networks will permit 5G NR and 4G LTE to coexist and will provide cost-effective and efficient solutions that enable a smooth transition from 4G to 5G. However, this increases the attack surface in the 5G networks. To the best of our knowledge,…
Advancements in computer networks and communication technologies like software defined networks (SDN), Internet of things (IoT), microservices architecture, cloud computing and network function virtualization (NFV) have opened new fronts and challenges for security experts to combat against modern cyberattacks. Relying on perimeter defense and signature-based network security solutions like Intrusion Detection and Prevention Systems (IDS/IPS) have failed to deliver adequate level of security against new attack vectors such as advance persistent threats, zero days, ransomware, botnets and other…
