Onion Routing is an encrypted communication system developed by the U.S. Naval Laboratory that uses existing Internet equipment to communicate anonymously. Miscreants use this means to conduct illegal transactions in the dark web, posing a security risk to citizens and the country. For this means of anonymous communication, website fingerprinting methods have been used in existing studies. These methods often have high overhead and need to run on devices with high performance, which makes the method inflexible. In this paper, we propose a lightweight method to address the high overhead problem that deep learning website fingerprinting methods generally have, so that the method can be applied on common devices while also ensuring accuracy to a certain extent. The proposed method refers to the structure of Inception net, divides the original larger convolutional kernels into smaller ones, and uses group convolution to reduce the website fingerprinting and computation to a certain extent without causing too much negative impact on the accuracy. The method was experimented on the data set collected by Rimmer et al. to ensure the effectiveness.

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.