Biblio

Container technology has been used for running multiple isolated operating system distros on a host or deploying large scale microservice-based applications. In most cases, containers share the same kernel with the host and other containers on the same host, and the application in the container can make system calls of the host kernel like a normal process on the host. Seccomp is a security mechanism for the Linux kernel, through which we can prohibit certain system calls from being executed by the program. Docker began to support the seccomp mechanism from version 1.10 and disables around 44 system calls out of 300+ by default. However, for a particular container, there are still many system calls that are unnecessary for running it allowed to be executed, and the abuse of system calls by a compromised container can trigger the security vulnerabilities of a host kernel. Unfortunately, Docker does not provide a way to get the necessary system calls for a particular container. In this paper, we propose RSDS, a method combining dynamic analysis and static analysis to get the necessary system calls for a particular container. Our experiments show that our solution can reduce system calls by 69.27%-85.89% compared to the default configuration on an x86-64 PC with Ubuntu 16.04 host OS and does not affect the functionalities of these containers.

The increasing vulnerabilities in Android kernel make it an attractive target to the attackers. Most kernel-targeted attacks are initiated through system calls. For security purpose, Google has introduced a Linux kernel security mechanism named “seccomp” since Android O to constrain the system calls accessible to the Android apps. Unfortunately, existing Android seccomp mechanism provides a fairly coarse-grained restriction by enforcing a unified seccomp policy containing more than 250 system calls for Android apps, which greatly reduces the effectiveness of seccomp. Also, it lacks an approach to profile the unnecessary system calls for a given Android app. In this paper we present a two-level control scheme named SASAK, which can shrink the attack surface of Android kernel by strictly constraining the system calls available to the Android apps with seccomp mechanism. First, instead of leveraging a unified seccomp policy for all Android apps, SASAK introduces an architecture- dedicated system call constraining by enforcing two separate and refined seccomp policies for the 32-bit Android apps and 64-bit Android apps, respectively. Second, we provide a tool to profile the necessary system calls for a given Android app and enforce an app-dedicated seccomp policy to further reduce the allowed system calls for the apps selected by the users. The app-dedicated control could dynamically change the seccomp policy for an app according to its actual needs. We implement a prototype of SASAK and the experiment results show that the architecture-dedicated constraining reduces 39.6% system calls for the 64-bit apps and 42.5% system calls for the 32-bit apps. 33% of the removed system calls for the 64-bit apps are vulnerable, and the number for the 32-bit apps is 18.8%. The app-dedicated restriction reduces about 66.9% and 62.5% system calls on average for the 64-bit apps and 32-bit apps, respectively. In addition, SASAK introduces negligible performance overhead.

We propose a novel visualization technique (Eagle-Eye) for intrusion detection, which visualizes a host as a commu- nity of system call traces in two-dimensional space. The goal of EagleEye is to visually cluster the system call traces. Although human eyes can easily perceive anomalies using EagleEye view, we propose two different methods called SAM and CPM that use the concept of data depth to help administrators distinguish between normal and abnormal behaviors. Our experimental results conducted on Australian Defence Force Academy Linux Dataset (ADFA-LD), which is a modern system calls dataset that includes new exploits and attacks on various programs, show EagleEye's efficiency in detecting diverse exploits and attacks.

Android operating systems have become a prime target for cyber attackers due to security vulnerabilities in the underlying operating system and application design. Recently, anomaly detection techniques are widely studied for security vulnerabilities detection and classification. However, the ability of the attackers to create new variants of existing malware using various masking techniques makes it harder to deploy these techniques effectively. In this research, we present a robust and effective vulnerabilities detection approach based on anomaly detection in a system calls of benign and malicious Android application. The anomaly in our study is type, frequency, and sequence of system calls that represent a vulnerability. Our system monitors the processes of benign and malicious application and detects security vulnerabilities based on the combination of parameters and metrics, i.e., type, frequency and sequence of system calls to classify the process behavior as benign or malign. The detection algorithm detects the anomaly based on the defined scoring function f and threshold ρ. The system refines the detection process by applying machine learning techniques to find a combination of system call metrics and explore the relationship between security bugs and the pattern of system calls detected. The experiment results show the detection rate of the proposed algorithm based on precision, recall, and f-score for different machine learning algorithms.

Malware technology makes it difficult for malware analyst to detect same malware files with different obfuscation technique. In this paper we are trying to tackle that problem by analyzing the sequence of system call from an executable file. Malware files which actually are the same should have almost identical or at least a similar sequence of system calls. In this paper, we are going to create a model for each malware class consists of malwares from different families based on its sequence of system calls. Method/algorithm that's used in this paper is profile hidden markov model which is a very well-known tool in the biological informatics field for comparing DNA and protein sequences. Malware classes that we are going to build are trojan and worm class. Accuracy for these classes are pretty high, it's above 90% with also a high false positive rate around 37%.