Alerts reported by intrusion detection systems (IDSes) are often the starting points for attack campaign discovery and response procedures. However, the sheer number of alerts compared to the number of real attacks, along with the complexity of alert investigations, poses a challenge to achieving effective alert triage with limited computational resources. Automated procedures and human analysts could suffer from the burden of analyzing floods of alerts, and fail to respond to critical alerts promptly.

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100 percent, and the semantic segmentation based attacks have a success rate over 90 percent among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.

Tor is the most widely used anonymity network, currently serving millions of users each day. However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks. For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users. To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor. TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users. Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse. We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.

Random walks form a critical foundation in many social network based security systems and applications. Currently, the design of such social security mechanisms is limited to the classical paradigm of using fixed-length random walks for all nodes on a social graph. However, the fixed-length walk paradigm induces a poor trade-off between security and other desirable properties. In this paper, we propose SmartWalk, a security enhancing system which incorporates adaptive random walks in social network security applications. We utilize a set of supervised machine learning techniques to predict the necessary random walk length based on the structural characteristics of a social graph. Using experiments on multiple real world topologies, we show that the desired walk length starting from a specific node can be well predicted given the local features of the node, and limited knowledge for a small set of training nodes. We describe node-adaptive and path-adaptive random walk usage models, where the walk length adaptively changes based on the starting node and the intermediate nodes on the path, respectively. We experimentally demonstrate the applicability of adaptive random walks on a number of social network based security and privacy systems, including Sybil defenses, anonymous communication and link privacy preserving systems, and show up to two orders of magnitude improvement in performance.