A New Distributed Intrusion Detection System Based on Multi-Agent System for Cloud Environment

Cloud computing, like any distributed computing system, is continually exposed to many threats and attacks of various origins. Thus, cloud security is now a very important concern for both providers and users. Intrusion detection systems (IDSs) are used to detect attacks in this environment. The goal of security administrators (for both customers and providers) is to prevent and detect attacks while avoiding disruption of the smooth operation of the cloud. Making IDSs efficient is not an easy task in a distributed environment such as the cloud. This problem remains open, and to our knowledge, there are no satisfactory solutions for the automated evaluation and analysis of cloud security. The features of the multi-agent system paradigm, such as adaptability, collaboration, and distribution, make it possible to handle this evolution of cloud computing in an efficient and controlled manner. As a result, multi-agent systems are well suited to the effective management of cloud security. In this paper, we propose an efficient, reliable and secure distributed IDS (DIDS) based on a multi-agent approach to identify and prevent new and complex malicious attacks in this environment. Moreover, some experiments were conducted to evaluate the performance of our model

Securing Cloud Computing from Different Attacks Using Intrusion Detection Systems

Cloud computing is a new way of integrating a set of old technologies to implement a new paradigm that creates an avenue for users to have access to shared and configurable resources through internet on-demand. This system has many common characteristics with distributed systems, hence, the cloud computing also uses the features of networking. Thus the security is the biggest issue of this system, because the services of cloud computing is based on the sharing. Thus, a cloud computing environment requires some intrusion detection systems (IDSs) for protecting each machine against attacks. The aim of this work is to present a classification of attacks threatening the availability, confidentiality and integrity of cloud resources and services. Furthermore, we provide literature review of attacks related to the identified categories. Additionally, this paper also introduces related intrusion detection models to identify and prevent these types of attacks

In Silico Identification of Specialized Secretory-Organelle Proteins in Apicomplexan Parasites and In Vivo Validation in Toxoplasma gondii

Apicomplexan parasites, including the human pathogens Toxoplasma gondii and Plasmodium falciparum, employ specialized secretory organelles (micronemes, rhoptries, dense granules) to invade and survive within host cells. Because molecules secreted from these organelles function at the host/parasite interface, their identification is important for understanding invasion mechanisms, and central to the development of therapeutic strategies. Using a computational approach based on predicted functional domains, we have identified more than 600 candidate secretory organelle proteins in twelve apicomplexan parasites. Expression in transgenic T. gondii of eight proteins identified in silico confirms that all enter into the secretory pathway, and seven target to apical organelles associated with invasion. An in silico approach intended to identify possible host interacting proteins yields a dataset enriched in secretory/transmembrane proteins, including most of the antigens known to be engaged by apicomplexan parasites during infection. These domain pattern and projected interactome approaches significantly expand the repertoire of proteins that may be involved in host parasite interactions

Weighting based approach for learning resources recommendations

Personalized e-learning systems based on recommender systems refines enormous amount of data and provides suggestions on learning resources which is appealing to the learner. Although, the recommender systems depends on content based approach or collaborative filtering technique to make recommendations, these methods suffers from cold start and data sparsity problems. To overcome the limitations of the aforementioned problems, a weight based approach is proposed for better performance. The main criterion for building a personalized recommender system is to exploit useful content and provide better recommendations with minimal processing time. The proposed system is a web based client side application which uses user profiles to form neighborhoods and calculates predictions using weights. For newcomers a profile is constructed based on learning styles. The resources which might be of interest to the user are predicted from calculated predictions

Securing Cloud Computing from Different Attacks Using Intrusion Detection Systems

Cloud computing is a new way of integrating a set of old technologies to implement a new paradigm that creates an avenue for users to have access to shared and configurable resources through internet on-demand. This system has many common characteristics with distributed systems, hence, the cloud computing also uses the features of networking. Thus the security is the biggest issue of this system, because the services of cloud computing is based on the sharing. Thus, a cloud computing environment requires some intrusion detection systems (IDSs) for protecting each machine against attacks. The aim of this work is to present a classification of attacks threatening the availability, confidentiality and integrity of cloud resources and services. Furthermore, we provide literature review of attacks related to the identified categories. Additionally, this paper also introduces related intrusion detection models to identify and prevent these types of attacks