Biblio

Lightweight virtualization represented by container technology provides a virtual environment for cloud services with more flexibility and efficiency due to the kernel-sharing property. However, the shared kernel also means that the system isolation mechanisms are incomplete. Attackers can scan the shared system configuration files to explore vulnerabilities for launching attacks. Previous works mainly eliminate the problem by fixing operating systems or using access control policies, but these methods require significant modifications and cannot meet the security needs of individual containers accurately. In this paper, we present ConfigRand, a moving target defense framework to prevent the information leakages due to the shared kernel in the container-based cloud. The ConfigRand deploys deceptive system configurations for each container, bounding the scan of attackers aimed at the shared kernel. In design of ConfigRand, we (1) propose a framework applying the moving target defense philosophy to periodically generate, distribute, and deploy the deceptive system configurations in the container-based cloud; (2) establish a model to formalize these configurations and quantify their heterogeneity; (3) present a configuration movement strategy to evaluate and optimize the variation of configurations. The results show that ConfigRand can effectively prevent the information leakages due to the shared kernel and apply to typical container applications with minimal system modification and performance degradation.

Unlike traditional processors, Internet of Things (IoT) devices are short of resources to incorporate mature protections (e.g. MMU, TrustZone) against modern control-flow attacks. Remote (control-flow) attestation is fast becoming a key instrument in securing such devices as it has proven the effectiveness on not only detecting runtime malware infestation of a remote device, but also saving the computing resources by moving the costly verification process away. However, few control-flow attestation schemes have been able to draw on any systematic research into the software specificity of bare-metal systems, which are widely deployed on resource-constrained IoT devices. To our knowledge, the unique design patterns of the system limit implementations of such expositions. In this paper, we present the design and proof-of-concept implementation of LAPE, a lightweight attestation of program execution scheme that enables detecting control-flow attacks for bare-metal systems without requiring hardware modification. With rudimentary memory protection support found in modern IoT-class microcontrollers, LAPE leverages software instrumentation to compartmentalize the firmware functions into several ”attestation compartments”. It then continuously tracks the control-flow events of each compartment and periodically reports them to the verifier. The PoC of the scheme is incorporated into an LLVM-based compiler to generate the LAPE-enabled firmware. By taking experiments with several real-world IoT firmware, the results show both the efficiency and practicality of LAPE.

Modern operating systems are typical multitasking systems: Running multiple tasks at the same time. Therefore, a large number of system calls belonging to different processes are invoked at the same time. By associating these invocations, one can construct the system dependency graph. In rapidly evolving system dependency graphs, how to quickly find outliers is an urgent issue for intrusion detection. Clustering analysis based on graph similarity will help solve this problem. In this paper, an extended Weisfeiler-Lehman(WL) kernel is proposed. Firstly, an embedded vector with indefinite dimensions is constructed based on the original dependency graph. Then, the vector is compressed with Simhash to generate a fingerprint. Finally, anomaly detection based on clustering is carried out according to these fingerprints. Our scheme can achieve prominent detection with high efficiency. For validation, we choose StreamSpot, a relevant prior work, to act as benchmark, and use the same data set as it to carry out evaluations. Experiments show that our scheme can achieve the highest detection precision of 98% while maintaining a perfect recall performance. Moreover, both quantitative and visual comparisons demonstrate the outperforming clustering effect of our scheme than StreamSpot.