DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

MULTI-GIGABIT PATTERN FOR DATA IN NETWORK SECURITY

In the current scenario network security is emerging the world. Matching large sets of patterns against an incoming stream of data is a fundamental task in several fields such as network security or computational biology. High-speed network intrusion detection systems (IDS) rely on efficient pattern matching techniques to analyze the packet payload and make decisions on the significance of the packet body. However, matching the streaming payload bytes against thousands of patterns at multi-gigabit rates is computationally intensive. Various techniques have been proposed in past but the performance of the system is reducing because of multi-gigabit rates.Pattern matching is a significant issue in intrusion detection systems, but by no means the only one. Handling multi-content rules, reordering, and reassembling incoming packets are also significant for system performance. We present two pattern matching techniques to compare incoming packets against intrusion detection search patterns. The first approach, decoded partial CAM (DpCAM), pre-decodes incoming characters, aligns the decoded data, and performs logical AND on them to produce the match signal for each pattern. The second approach, perfect hashing memory (PHmem), uses perfect hashing to determine a unique memory location that contains the search pattern and a comparison between incoming data and memory output to determine the match. The suggested methods have implemented in vhdl coding and we use Xilinx for synthesis

A Neural Network Based Security Tool for Analyzing Software

Part 4: Intelligent Computational SystemsInternational audienceThe need to secure software application in today’s hostile computer environment cannot be overlooked. The increase in attacks aimed at software directly in the last decade and the demand for more secure software applications has drawn the attention of the software industry into looking for better ways in which software can be developed more securely. To achieve this, it has been suggested that security needs to be integrated into every phase of software development lifecycle (SDLC). In line with this view, security tools are now used during SDLC to integrate security into software applications. Here, we propose a neural network based security tool for analyzing software design for security flaws. Our findings show that the trained neural network was able to match possible attack patterns to design scenarios presented to it. With the information on the attack pattern identified, developers can make informed decision in mitigating risks in their designs

Assessing The Security Posture Of Openemr Using Capec Attack Patterns

Attack patterns describe the common methods of exploiting software. Good software engineering practices and principles alone are not enough to produce secure software. It is also important to know how software it attacked and to guard against it. Knowledge of attack patterns provides a good perspective of an attacker, thus enabling developers and testers to build secure software. CAPEC list is a taxonomy of attack patterns which we believe can enhance security testing. This research seeks to assess the security posture of OpenEMR 4.1.1, an open source Electronic Medical Record (EMR) system, based on CAPEC attack patterns. Five categories of CAPEC attack patterns were analyzed to find their relevance and applicability to OpenEMR. Whereas inapplicable attack patterns were not further considered, applicable attack patterns were further tested to assess OpenEMR vulnerability to them. Various security testing tools were used to carry out the tests. Attack patterns helped to focus black-box and white-box testing procedures on what and where to test. OpenEMR was found to be vulnerable to a number of vulnerabilities such as cross site scripting, authentication bypass, session sidejacking, among others. A number of exploitations were carried out based on the vulnerabilities discovered

Neuro-Fuzzy Based Software Risk Estimation Tool

To develop the secure software is one of the major concerns in the software industry. To make the easier task of finding and fixing the security flaws, software developers should integrate the security at all stages of Software Development Life Cycle (SDLC).In this paper, based on Neuro- Fuzzy approach software Risk Prediction tool is created. Firstly Fuzzy Inference system is created and then Neural Network based three different training algorithms: BR (Bayesian Regulation), BP (Back propagation) and LM (Levenberg-Marquardt) are used to train the neural network. From the results it is conclude that for the Software Risk Estimation, BR (Bayesian Regulation) performs better and also achieves the greater accuracy than other algorithms