Using response action with Intelligent Intrusion detection and prevention System against web application malware

Findings: After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system

Proposing a secure component-based-application logic and system’s integration testing approach

Software engineering moved from traditional methods of software enterprise applications to com-ponent based development for distributed system’s applications. This new era has grown up forlast few years, with component-based methods, for design and rapid development of systems, butfact is that , deployment of all secure software features of technology into practical e-commercedistributed systems are higher rated target for intruders. Although most of research has been con-ducted on web application services that use a large share of the present software, but on the otherside Component Based Software in the middle tier ,which rapidly develops application logic, alsoopen security breaching opportunities .This research paper focus on a burning issue for researchersand scientists ,a weakest link in component based distributed system, logical attacks, that cannotbe detected with any intrusion detection system within the middle tier e-commerce distributed ap-plications. We proposed An Approach of Secure Designing application logic for distributed system,while dealing with logically vulnerability issue

A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks

With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe