Biblio

Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are
writing the code of the application, might help Android application developers in releasing applications to end-users that have less security and privacy risk. The goal of this paper
is to aid Android application developers in assessing the security and privacy risk associated with Android applications by using static code metrics as predictors. In our paper, we consider security and privacy risk of Android application as how susceptible the application is to leaking private information of end-users and to releasing vulnerabilities. We investigate how effectively static code metrics that are extracted from the source code of Android applications, can be used to predict security and privacy risk of Android applications. We collected 21 static code metrics of 1,407 Android applications, and use the collected static code metrics to predict security and privacy risk of the applications. As the oracle of security and privacy risk, we used Androrisk, a tool that quantifies the amount of security and privacy risk of an Android application using analysis of Android permissions and dynamic analysis. To accomplish our goal, we used statistical learners such as, radial-based support vector machine (r-SVM). For r-SVM, we observe a precision of 0.83. Findings from our paper suggest that with proper selection of static code metrics, r-SVM can be used effectively to predict security and privacy risk of Android applications

This paper presents an approach for securing software application chains in cloud environments. We use the concept of workflow management systems to explain the model. Our prototype is based on the Kepler scientific workflow system enhanced with a security analytics package. This model can be applied to other cloud based systems. Depending on the information being received from the cloud, this approach can also offer information about internal states of the resources in
the cloud. The approach we use hinges on (1) an ability to limit attacks to Input, Remote, and Output channels (or flows), and (2) validate the flows using operational profile (OP) or certification based signals. OP based validation is a statistical approach and may miss some of the attacks. However, where enumeration is possible (e.g., static web sites), this approach can offer high assurances of validity of the flows. It is also assumed that workflow components are sound so long as the input flows are limited to operational profile. Other acceptance testing approaches could be used to validate the flows. Work in progress has two thrusts: (1) using cloud-based Kepler workflows to probe and assess security states and operation of cloud resources (specifically VMs) under different workloads leveraging DACSA sensors; and (2) analyzing effectiveness of the proposed approach in securing workflows.

Hadoop has become increasingly popular as it rapidly processes data in parallel. Cloud computing gives reliability, flexibility, scalability, elasticity and cost saving to cloud users. Deploying Hadoop in cloud can benefit Hadoop users. Our evaluation exhibits that various internal cloud attacks can bypass current Hadoop security mechanisms, and compromised Hadoop components can be used to threaten overall Hadoop. It is urgent to improve compromise resilience, Hadoop can maintain a relative high security level when parts of Hadoop are compromised. Hadoop has two vulnerabilities that can dramatically impact its compromise resilience. The vulnerabilities are the overloaded authentication key, and the lack of fine-grained access control at the data access level. We developed a security enhancement for a public cloud-based Hadoop, named SEHadoop, to improve the compromise resilience through enhancing isolation among Hadoop components and enforcing least access privilege for Hadoop processes. We have implemented the SEHadoop model, and demonstrated that SEHadoop fixes the above vulnerabilities with minimal or no run-time overhead, and effectively resists related attacks.

To help establish a more scientific basis for security science, which will enable the development of fundamental theories and move the field from being primarily reactive to primarily proactive, it is important for research results to be reported in a scientifically rigorous manner. Such reporting will allow for the standard pillars of science, namely replication, meta-analysis, and theory building. In this paper we aim to establish a baseline of the state of scientific work in security through the analysis of indicators of scientific research as reported in the papers from the 2015 IEEE Symposium on Security and Privacy. To conduct this analysis, we developed a series of rubrics to determine the completeness of the papers relative to the type of evaluation used (e.g. case study, experiment, proof). Our findings showed that while papers are generally easy to read, they often do not explicitly document some key information like the research objectives, the process for choosing the cases to include in the studies, and the threats to validity. We hope that this initial analysis will serve as a baseline against which we can measure the advancement of the science of security.

Cyber-attacks and breaches are often detected too late to avoid damage. While “classical” reactive cyber defenses usually work only if we have some prior knowledge about the attack methods and “allowable” patterns, properly constructed redundancy-based anomaly detectors can be more robust and often able to detect even zero day attacks. They are a step toward an oracle that uses knowable behavior of a healthy system to identify abnormalities. In the world of Internet of Things (IoT), security, and anomalous behavior of sensors and other IoT components, will be orders of magnitude more difficult unless we make those elements security aware from the start. In this article we examine the ability of redundancy-based a nomaly detectors to recognize some high-risk and difficult to detect attacks on web servers—a likely management interface for many IoT stand-alone elements. In real life, it has taken long, a number of years in some cases, to identify some of the vulnerabilities and related attacks. We discuss practical relevance of the approach in the context of providing high-assurance Webservices that may belong to autonomous IoT applications and devices

Platform as a Service (PaaS) provides middleware resources to cloud customers. As demand for PaaS services increases, so do concerns about the security of PaaS. This paper discusses principal PaaS security and integrity requirements, and vulnerabilities and the corresponding countermeasures. We consider three core cloud elements: multi-tenancy, isolation, and virtualization and how they relate to PaaS services and security trends and concerns such as user and resource isolation, side-channel vulnerabilities in multi-tenant environments, and protection of sensitive data

This paper investigates security of Kepler scientificbworkflow engine. We are especially interested in Kepler-based scientific workflows that may operate in cloud environments. We find that (1) three security properties (i.e., input validation, remote access validation, and data integrity) are essential for making Kepler-based workflows more secure, and (2) that use of the Kepler provenance module may help secure Keplerbased workflows. We implemented a prototype security enhanced Kepler engine to demonstrate viability of use of the Kepler provenance module in provision and management of the desired security properties.