Unleashing the Power of Multi-Agent Deep Learning: Cyber-Attack Detection in IoT

Detecting botnet and malware cyber-attacks is a critical task in ensuring the security of computer networks. Traditional methods for identifying such attacks often involve static rules and signatures, which can be easily evaded by attackers. Dl is a subdivision of ML, has shown promise in enhancing the accuracy of detecting botnets and malware by analyzing large amounts of network traffic data and identifying patterns that are difficult to detect with traditional methods. In order to identify abnormal traffic patterns that can be a sign of botnet or malware activity, deep learning models can be taught to learn the intricate interactions and correlations between various network traffic parameters, such as packet size, time intervals, and protocol headers. The models can also be trained to detect anomalies in network traffic, which could indicate the presence of unknown malware. The threat of malware and botnet assaults has increased in frequency with the growth of the IoT. In this research, we offer a unique LSTM and GAN-based method for identifying such attacks. We utilise our model to categorise incoming traffic as either benign or malicious using a dataset of network traffic data from various IoT devices. Our findings show how well our method works by attaining high accuracy in identifying botnet and malware cyberattacks in IoT networks. This study makes a contribution to the creation of stronger and more effective security systems for shielding IoT devices from online dangers.  One of the major advantages of using deep learning for botnet and malware detection is its ability to adapt to new and previously unknown attack patterns, making it a useful tool in the fight against constantly evolving cyber threats. However, DL models require large quantity of labeled data for training, and their performance can be affected by the quality and quantity of the data used.  Deep learning holds great potential for improving the accuracy and effectiveness of botnet and malware detection, and its continued development and application could lead to significant advancements in the field of cybersecurity

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been developed to effectively capture the evolving patterns in dynamic graphs. While these models have demonstrated their superiority, being widely adopted in various important fields, their vulnerabilities against adversarial attacks remain largely unexplored. In this paper, we propose T-SPEAR, a simple and effective adversarial attack method for link prediction on continuous-time dynamic graphs, focusing on investigating the vulnerabilities of TGNNs. Specifically, before the training procedure of a victim model, which is a TGNN for link prediction, we inject edge perturbations to the data that are unnoticeable in terms of the four constraints we propose, and yet effective enough to cause malfunction of the victim model. Moreover, we propose a robust training approach T-SHIELD to mitigate the impact of adversarial attacks. By using edge filtering and enforcing temporal smoothness to node embeddings, we enhance the robustness of the victim model. Our experimental study shows that T-SPEAR significantly degrades the victim model's performance on link prediction tasks, and even more, our attacks are transferable to other TGNNs, which differ from the victim model assumed by the attacker. Moreover, we demonstrate that T-SHIELD effectively filters out adversarial edges and exhibits robustness against adversarial attacks, surpassing the link prediction performance of the naive TGNN by up to 11.2% under T-SPEAR

Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems

Adversarial AI Testcases for Maritime Autonomous Systems

Contemporary maritime operations such as shipping are a vital component constituting global trade and defence. The evolution towards maritime autonomous systems, often providing significant benefits (e.g., cost, physical safety), requires the utilisation of artificial intelligence (AI) to automate the functions of a conventional crew. However, unsecured AI systems can be plagued with vulnerabilities naturally inherent within complex AI models. The adversarial AI threat, primarily only evaluated in a laboratory environment, increases the likelihood of strategic adversarial exploitation and attacks on mission-critical AI, including maritime autonomous systems. This work evaluates AI threats to maritime autonomous systems in situ. The results show that multiple attacks can be used against real-world maritime autonomous systems with a range of lethality. However, the effects of AI attacks vary in a dynamic and complex environment from that proposed in lower entropy laboratory environments. We propose a set of adversarial test examples and demonstrate their use, specifically in the marine environment. The results of this paper highlight security risks and deliver a set of principles to mitigate threats to AI, throughout the AI lifecycle, in an evolving threat landscape.</jats:p

SECURING 5G NETWORKS WITH FEDERATED LEARNING AND GAN

The threat landscape of the 5G network is quite vast due to the complexity of its architecture and its use of virtualized network functions. This landscape can be divided into two categories: Attacks against the Access point and Attacks against the Core. This thesis has been dedicated to analyzing the threats that plague the 5G network with a special focus on the access point. The architecture for the access point was simulated with a federated learning environment to not only secure the privacy of the user data but to also present a realistic scenario from which to perceive the 5G network. The main objective of the thesis was to secure the access point of the 5G network in this federated learning environment. This was accomplished by placing an Intrusion Detection System at the endpoint which would classify the data as either benign or malicious. The effectiveness of this model was checked by simulating a malicious user and con- ducting certain adversarial attacks to determine if the model could defend against them. The study was conducted by performing two specific attacks i.e Label-Flipping attack and Genera- tive Adversarial Networks. The attacks were successful and revealed that a new system should be designed and developed that could be resilient against these types of attacks