Biblio

Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.

Honeypots are servers or systems built to mimic critical parts of a network, distracting attackers while logging their information to develop attack profiles. This paper discusses the design and implementation of a honeypot disguised as a REpresentational State Transfer (REST) Application Programming Interface (API). We discuss the motivation for this work, design features of the honeypot, and experimental performance results under various traffic conditions. We also present analyses of both a distributed denial of service (DDoS) attack and a cross-site scripting (XSS) malware insertion attempt against this honeypot.

There are continuous hacking and social issues regarding APT (Advanced Persistent Threat - APT) attacks and a number of antivirus businesses and researchers are making efforts to analyze such APT attacks in order to prevent or cope with APT attacks, some host PC security technologies such as firewalls and intrusion detection systems are used. Therefore, in this study, malignant behavior patterns were extracted by using an API of PE files. Moreover, the FP-Growth Algorithm to extract behavior information generated in the host PC in order to overcome the limitation of the previous signature-based intrusion detection systems. We will utilize this study as fundamental research about a system that extracts malignant behavior patterns within networks and APIs in the future.

In recent years, with the advances in JavaScript engines and the adoption of HTML5 APIs, web applications begin to show a tendency to shift their functionality from the server side towards the client side, resulting in dense and complex interactions with HTML documents using the Document Object Model (DOM). As a consequence, client-side vulnerabilities become more and more prevalent. In this paper, we focus on DOM-sourced Cross-site Scripting (XSS), which is a kind of severe but not well-studied vulnerability appearing in browser extensions. Comparing with conventional DOM-based XSS, a new attack surface is introduced by DOM-sourced XSS where the DOM could become a vulnerable source as well besides common sources such as URLs and form inputs. To discover such vulnerability, we propose a detecting framework employing hybrid analysis with two phases. The first phase is the lightweight static analysis consisting of a text filter and an abstract syntax tree parser, which produces potential vulnerable candidates. The second phase is the dynamic symbolic execution with an additional component named shadow DOM, generating a document as a proof-of-concept exploit. In our large-scale real-world experiment, 58 previously unknown DOM-sourced XSS vulnerabilities were discovered in user scripts of the popular browser extension Greasemonkey.

We present work undertaken at our institutional repository to enhance metadata and re-organize digital objects according to new information architecture, in an effort to minimize administrative object management and processing, and improve object discovery and use. This work was partly motivated by the launch of a new discovery platform at our institution, which aggregates metadata and full text from our four open access repositories into a cohesive, consistent, and enhanced searching and browsing experience. The platform provides digital object identifier (DOI) assignment, metadata access via various formats, and an open metadata and full text application program interface (API) for researchers, amongst other features. Functionality of these platform features relies heavily on accurate object representation and metadata. This work facilitates and improves the discovery and engagement of the diverse digital objects available from our institution, so they can be used and analyzed in new, flexible, and innovative ways by a myriad of communities and disciplines.

Vulnerability Detection Tools (VDTs) have been researched and developed to prevent problems with respect to security. Such tools identify vulnerabilities that exist on the server in advance. By using these tools, administrators must protect their servers from attacks. They have, however, different results since methods for detection of different tools are not the same. For this reason, it is recommended that results are gathered from many tools rather than from a single tool but the installation which all of the tools have requires a great overhead. In this paper, we propose a novel vulnerability detection mechanism using Open API and use OpenVAS for actual testing.

Federated cloud networks are formed by federating virtual network segments from different clouds, e.g. in a hybrid cloud, into a single federated network. Such networks should be protected with a global federated cloud network security policy. The availability of network function virtualisation and service function chaining in cloud platforms offers an opportunity for implementing and enforcing global federated cloud network security policies. In this paper we describe an approach for enforcing global security policies in federated cloud networks. The approach relies on a service manifest that specifies the global network security policy. From this manifest configurations of the security functions for the different clouds of the federation are generated. This enables automated deployment and configuration of network security functions across the different clouds. The approach is illustrated with a case study where communications between trusted and untrusted clouds, e.g. public clouds, are encrypted. The paper discusses future work on implementing this architecture for the OpenStack cloud platform with the service function chaining API.

The Internet of Things(IoT) has become a popular technology, and various middleware has been proposed and developed for IoT systems. However, there have been few studies on the data management of IoT systems. In this paper, we consider graph database models for the data management of IoT systems because these models can specify relationships in a straightforward manner among entities such as devices, users, and information that constructs IoT systems. However, applying a graph database to the data management of IoT systems raises issues regarding distribution and security. For the former issue, we propose graph database operations integrated with REST APIs. For the latter, we extend a graph edge property by adding access protocol permissions and checking permissions using the APIs with authentication. We present the requirements for a use case scenario in addition to the features of a distributed graph database for IoT data management to solve the aforementioned issues, and implement a prototype of the graph database.

The perception of lack of control over resources deployed in the cloud may represent one of the critical factors for an organization to decide to cloudify or not their own services. Furthermore, in spite of the idea of offering security-as-a-service, the development of secure cloud applications requires security skills that can slow down the adoption of the cloud for nonexpert users. In the recent years, the concept of Security Service Level Agreements (Security SLA) is assuming a key role in the provisioning of cloud resources. This paper presents the SPECS framework, which enables the development of secure cloud applications covered by a Security SLA. The SPECS framework offers APIs to manage the whole Security SLA life cycle and provides all the functionalities needed to automatize the enforcement of proper security mechanisms and to monitor userdefined security features. The development process of SPECS applications offering security-enhanced services is illustrated, presenting as a real-world case study the provisioning of a secure web server.

Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.

Hashing algorithms are used extensively in information security and digital forensics applications. This paper presents an efficient parallel algorithm hash computation. It's a modification of the SHA-1 algorithm for faster parallel implementation in applications such as the digital signature and data preservation in digital forensics. The algorithm implements recursive hash to break the chain dependencies of the standard hash function. We discuss the theoretical foundation for the work including the collision probability and the performance implications. The algorithm is implemented using the OpenMP API and experiments performed using machines with multicore processors. The results show a performance gain by more than a factor of 3 when running on the 8-core configuration of the machine.

Hashing algorithms are used extensively in information security and digital forensics applications. This paper presents an efficient parallel algorithm hash computation. It's a modification of the SHA-1 algorithm for faster parallel implementation in applications such as the digital signature and data preservation in digital forensics. The algorithm implements recursive hash to break the chain dependencies of the standard hash function. We discuss the theoretical foundation for the work including the collision probability and the performance implications. The algorithm is implemented using the OpenMP API and experiments performed using machines with multicore processors. The results show a performance gain by more than a factor of 3 when running on the 8-core configuration of the machine.
 

Web Services can be invoked from anywhere through internet without having enough knowledge about the implementation details. In some cases, single service cannot accomplish user needs. One or more services must be composed which together satisfy the user needs. Therefore, security is the most important concern not only at single service level but also at composition level. Several attacks are possible on SOAP messages communicated among Web Services because of their standardized interfaces. Examples of Web Service attacks are oversize payload, SOAPAction spoofing, XML injection, WS-Addressing spoofing, etc. Most of the existing works provide solution to ensure basic security features of Web Services such as confidentiality, integrity, authentication, authorization, and non-repudiation. Very few of the existing works provide solutions such as schema validation and schema hardening for attacks on Web Services. But these solutions do not address and provide attack specific solutions for SOAP messages communicated between Web Service. Hence, it is proposed to provide solutions for two of the prevailing Web Service attacks. Since new types of Web Service attacks are evolving over time, the proposed security solutions are implemented as APIs that are pluggable in any server where the Web Service is deployed.
 

Cryptographic misuse affects a sizeable portion of Android applications. However, there is only an empirical study that has been made about this problem. In this paper, we perform a systematic analysis on the cryptographic misuse, build the cryptographic misuse vulnerability model and implement a prototype tool Crypto Misuse Analyser (CMA). The CMA can perform static analysis on Android apps and select the branches that invoke the cryptographic API. Then it runs the app following the target branch and records the cryptographic API calls. At last, the CMA identifies the cryptographic API misuse vulnerabilities from the records based on the pre-defined model. We also analyze dozens of Android apps with the help of CMA and find that more than a half of apps are affected by such vulnerabilities.
 

This research focuses on hyper visor security from holistic perspective. It centers on hyper visor architecture - the organization of the various subsystems which collectively compromise a virtualization platform. It holds that the path to a secure hyper visor begins with a big-picture focus on architecture. Unfortunately, little research has been conducted with this perspective. This study investigates the impact of monolithic and micro kernel hyper visor architectures on the size and scope of the attack surface. Six architectural features are compared: management API, monitoring interface, hyper calls, interrupts, networking, and I/O. These subsystems are core hyper visor components which could be used as attack vectors. Specific examples and three leading hyper visor platforms are referenced (ESXi for monolithic architecture; Xen and Hyper-V for micro architecture). The results describe the relative strengths and vulnerabilities of both types of architectures. It is concluded that neither design is more secure, since both incorporate security tradeoffs in core processes.