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

The role of clathrin in post-golgi trafficking in toxoplasma gondii

Apicomplexan parasites are single eukaryotic cells with a highly polarised secretory system that contains unique secretory organelles (micronemes and rhoptries) that are required for host cell invasion. In contrast, the role of the endosomal system is poorly understood in these parasites. With many typical endocytic factors missing, we speculated that endocytosis depends exclusively on a clathrin-mediated mechanism. Intriguingly, in Toxoplasma gondii we were only able to observe the endogenous clathrin heavy chain 1 (CHC1) at the Golgi, but not at the parasite surface. For the functional characterisation of Toxoplasma gondii CHC1 we generated parasite mutants conditionally expressing the dominant negative clathrin Hub fragment and demonstrate that CHC1 is essential for vesicle formation at the trans-Golgi network. Consequently, the functional ablation of CHC1 results in Golgi aberrations, a block in the biogenesis of the unique secretory microneme and rhoptry organelles, and of the pellicle. However, we found no morphological evidence for clathrin mediating endocytosis in these parasites and speculate that they remodelled their vesicular trafficking system to adapt to an intracellular lifestyle

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

Prostate cancer bone metastases promote both osteolytic and osteoblastic activity

Advanced prostate cancer is frequently accompanied by the development of metastasis to bone. In the past, prostate cancer bone metastases were characterized as being osteoblastic (i.e., increasing bone density) based on radiographs. However, emerging evidence suggests that development of prostate cancer bone metastases requires osteoclastic activity in addition to osteoblastic activity. The complexities of how prostate tumor cells influence bone remodeling are just beginning to be elucidated. Prostate cancer cells produce a variety of pro-osteoblastic factors that promote bone mineralization. For example, both bone morphogenetic proteins and endothelin-1 have well recognized pro-osteoblastic activities and are produced by prostate cancer cells. In addition to factors that enhance bone mineralization prostate cancer cells produced factors that promote osteoclast activity. Perhaps the most critical pro-osteoclastogenic factor produced by prostate cancer cells is receptor activator of NFΚB ligand (RANKL), which has been shown to be required for the development of osteoclasts. Blocking RANKL results in inhibiting prostate cancer-induced osteoclastogenesis and inhibits development and progression of prostate tumor growth in bone. These findings suggest that targeting osteoclast activity may be of therapeutic benefit. However, it remains to be defined how prostate cancer cells synchronize the combination of osteoclastic and osteoblastic activity. We propose that as the bone microenvironment is changed by the developing cancer, this in turn influences the prostate cancer cells' balance between pro-osteoclastic and pro-osteoblastic activity. Accordingly, the determination of how the prostate cancer cells and bone microenvironment crosstalk are important to elucidate how prostate cancer cells modulate bone remodeling. © 2003 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34904/1/10662_ftp.pd

Caracterisation de proteines des micronemes et des granules denses chez Toxoplasma gondii

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 81940 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

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