Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem in the context of computer vision or console games, this paper focuses on reinforcement learning in autonomous cyber defence under partial observability. We demonstrate that under the black-box setting, where the attacker has no direct access to the target RL model, causative attacks-attacks that target the training process-can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.

Compared with traditional news media, social media nowadays provides a richer and more timely source of news. We are interested in multi-spatial level event detection from geo-tagged tweet streams. Specifically, in this paper we (1) examine the statistical characteristic for the time series of the number of geo-tagged tweets posted from specific regions during a short time interval, e.g., one minute; (2) verify from over thirty datasets that while almost all such time series exhibit self-similarity, those that correspond to events, especially short-term and unplanned outbursts, follow a power-law distribution; (3) demonstrate that these findings can be applied to facilitate event detection from tweet streams. We propose two algorithms-Power-law basic and Power-law advanced, where Power-law basic only checks the existence of power-law distributions in the time series from tweet streams at multi-spatial scales, without looking into the content of each tweet, and Power-law advanced integrates power-law verification with semantic analysis via word embedding. Our experiments on multiple datasets show that when combined with a Quad-tree, the seemingly naive algorithm of Power-law basic achieves comparable results with more advanced event detection methods, while the semantic analysis enhanced version, Power-law advanced, can significantly increase both the precision and the recall.

Cloud computing enables users to consume various IT resources in an on-demand manner, and with low management overhead. However, customers can face new security risks when they use cloud computing platforms. In this paper, we focus on one such threat-the co-resident attack, where malicious users build side channels and extract private information from virtual machines co-located on the same server. Previous works mainly attempt to address the problem by eliminating side channels. However, most of these methods are not suitable for immediate deployment due to the required modifications to current cloud platforms. We choose to solve the problem from a different perspective, by studying how to improve the virtual machine allocation policy, so that it is difficult for attackers to co-locate with their targets. Specifically, we (1) define security metrics for assessing the attack; (2) model these metrics, and compare the difficulty of achieving co-residence under three commonly used policies; (3) design a new policy that not only mitigates the threat of attack, but also satisfies the requirements for workload balance and low power consumption; and (4) implement, test, and prove the effectiveness of the policy on the popular open-source platform OpenStack.

While cloud computing has facilitated easy and affordable access to IT resources, it has also introduced a wide range of security risks from almost every layer and component of cloud systems. In this paper, we focus on one risk at the virtual machine level and the co-resident attack, where by constructing various types of side channels, malicious users can obtain sensitive information from other virtual machines that co-locate on the same physical server. Most previous work has focused on the elimination of side channels, or more generally speaking, the possible countermeasures after attackers co-locate with their targets. In contrast, we provide a different perspective, and propose a defence mechanism that makes it difficult and expensive for attackers to achieve co-residence in the first place. Specifically, we first identify the potential differences between the behaviors of attackers and legal users. Second, we apply clustering analysis and semi-supervised learning techniques to classify users. Third, we model the problem as a two-player security game, and give a detailed analysis of the optimum strategies for both players. Finally, we demonstrate that the attacker's overall cost is increased dramatically by one-to-two orders of magnitude as a result of our defence mechanism.

While the services-based model of cloud computing makes more and more IT resources available to a wider range of customers, the massive amount of data in cloud platforms is becoming a target for malicious users. Previous studies show that attackers can co-locate their virtual machines (VMs) with target VMs on the same server, and obtain sensitive information from the victims using side channels. This paper investigates VM allocation policies and practical countermeasures against this novel kind of co-resident attack by developing a set of security metrics and a quantitative model. A security analysis of three VM allocation policies commonly used in existing cloud computing platforms reveals that the server's configuration, oversubscription and background traffic have a large impact on the ability to prevent attackers from co-locating with the targets. If the servers are properly configured, and oversubscription is enabled, the best policy is to allocate new VMs to the server with the most VMs. Based on these results, a new strategy is introduced that effectively decreases the probability of attackers achieving co-residence. The proposed solution only requires minor changes to current allocation policies, and hence can be easily integrated into existing cloud platforms to mitigate the threat of co-resident attacks.

Analysing and modelling the characteristics of virtual machine (VM) usage gives cloud providers crucial information when dimensioning cloud infrastructure and designing appropriate allocation policies. In addition, administrators can use these models to build a normal behaviour profile of job requests, in order to differentiate malicious and normal activities. Finally, it allows researchers to design more accurate simulation environments. An open challenge is to empirically develop and verify an accurate model of VM usage for users in these applications. In this paper, we study the VM usage in the popular Amazon EC2 and Windows Azure cloud platforms, in terms of the VM request arrival and departure processes, and the number of live VMs in the system. We find that both the VM request arrival and departure processes exhibit self-similarity and follow the power law distribution. Our analysis also shows that the autoregressive integrated moving average (ARIMA) model can be used to fit and forecast the VM demands, which is an important requirement for managing the workload in cloud services.