MLAMAN: a novel multi-level authentication model and protocol for preventing wormhole attack in mobile ad hoc network

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Wormhole attack is a serious security issue in Mobile Ad hoc Network where malicious nodes may distort the network topology and obtain valuable information. Many solutions, based on round trip time, packet traversal time, or hop-count, have been proposed to detect wormholes. However, these solutions were only partially successful in dealing with node high-speed mobility, variable tunnel lengths, and fake information by malicious nodes. To address those issues, this paper proposes a novel multi-level authentication model and protocol (MLAMAN) for detecting and preventing wormhole attacks reliably. MLAMAN allows all intermediate nodes to authenticate control packets on a hop-by-hop basis and at three levels: (1) the packet level where the integrity of the packets can be verified, (2) the node membership level where a public key holder-member can be certified, and (3) the neighborhood level where the neighborhood relationship between nodes can be determined. The novelty of the model is that it prevents malicious nodes from joining the network under false information and pretense. It detects wormhole nodes effectively under various scenarios including variable tunnel lengths and speeds of moving nodes. The effectiveness of our approach is confirmed by simulation results through various scenarios

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

© 2019 Ngoc T. Luong et al. Request route flooding attack is one of the main challenges in the security of Mobile Ad Hoc Networks (MANETs) as it is easy to initiate and difficult to prevent. A malicious node can launch an attack simply by sending an excessively high number of route request (RREQ) packets or useless data packets to nonexistent destinations. As a result, the network is rendered useless as all its resources are used up to serve this storm of RREQ packets and hence unable to perform its normal routing duty. Most existing research efforts on detecting such a flooding attack use the number of RREQs originated by a node per unit time as the threshold to classify an attacker. These algorithms work to some extent; however, they suffer high misdetection rate and reduce network performance. This paper proposes a new flooding attacks detection algorithm (FADA) for MANETs based on a machine learning approach. The algorithm relies on the route discovery history information of each node to capture similar characteristics and behaviors of nodes belonging to the same class to decide if a node is malicious. The paper also proposes a new flooding attacks prevention routing protocol (FAPRP) by extending the original AODV protocol and integrating FADA algorithm. The performance of the proposed solution is evaluated in terms of successful attack detection ratio, packet delivery ratio, and routing load both in normal and under RREQ attack scenarios using NS2 simulation. The simulation results show that the proposed FAPRP can detect over 99% of RREQ flooding attacks for all scenarios using route discovery frequency vector of sizes larger than 35 and performs better in terms of packet delivery ratio and routing load compared to existing solutions for RREQ flooding attacks

Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks

© 2018 IEEE. We propose a novel edge computing network architecture that enables edge nodes to cooperate in sharing computing and radio resources to minimize the total energy consumption of mobile users while meeting their delay requirements. To find the optimal task offloading decisions for mobile users, we first formulate the joint task offloading and resource allocation optimization problem as a mixed integer non-linear programming (MINLP). The optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computational intractable problem. To circumvent, we relax the binary decision variables to transform the MINLP to a relaxed optimization problem with real variables. After proving that the relaxed problem is a convex one, we propose two solutions namely ROP and IBBA. ROP is adopted from the interior point method and IBBA is developed from the branch and bound algorithm. Through the numerical results, we show that our proposed approaches allow minimizing the total energy consumption and meet all delay requirements for mobile users

NEU-chatbot: Chatbot for admission of National Economics University

In the last few years, intelligent chatbot systems have been prevalent in various application fields, especially in education. Therefore, the demand for such online consulting services like chatbots is getting higher respectively. However, most communications between potential students and universities are performed manually, which is very time-consuming procedure, becoming a burden on the head of admissions. In this paper, we introduce an AI-based chatbot where students can instantly get daily updates of curriculum, admission for new students, tuition fees, IELTS writing task II score, etc. Our chatbot was developed by Deep Learning models, which are already integrated into the Rasa framework. We also proposed a rational pipeline for Vietnamese chatbots with our data preprocessing to obtain optimal accuracy and to avoid the overfitting of the model. Our model can detect more than fifty types of questions from users' input with an accuracy of 97.1% on test set. The chatbot was applied for National Economics University's official admission Fanpage on the Facebook platform, which is the most famous social network in Vietnam. This research shows detailed guidelines on how to build an AI chatbot from scratch, and the techniques we used, which can be applied to any language globally

Optimal Energy Efficiency with Delay Constraints for Multi-layer Cooperative Fog Computing Networks

We develop a joint offloading and resource allocation framework for a multi-layer cooperative fog computing network, aiming to minimize the total energy consumption of multiple mobile devices subject to their service delay requirements. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computationally intractable problem. To tackle it, we first propose an improved branch-and-bound algorithm (IBBA) that is implemented in a centralized manner. However, due to the large size of the cooperative fog computing network, the computational complexity of the proposed IBBA is relatively high. To speed up the optimal solution searching as well as to enable its distributed implementation, we then leverage the unique structure of the underlying problem and the parallel processing at fog nodes. To that end, we propose a distributed framework, namely feasibility finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. The master problem (MP) is then equipped with powerful cutting-planes to exploit the fact of resource limitation at fog nodes. The subproblems (SP) for resource allocation can find their closed-form solutions using our fast solution detection method. These (simpler) subproblems can then be solved in parallel at fog nodes. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., compared with the centralized IBBA approach). The FFBD with the fast solution detection method, namely FFBD-F, can reduce up to $60\%$ and $90\%$ of computation time, respectively, compared with those of the conventional FFBD, namely FFBD-S, and IBBA

Phosphate Adsorption by Silver Nanoparticles-Loaded Activated Carbon derived from Tea Residue.

This study presents the removal of phosphate from aqueous solution using a new silver nanoparticles-loaded tea activated carbon (AgNPs-TAC) material. In order to reduce costs, the tea activated carbon was produced from tea residue. Batch adsorption experiments were conducted to evaluate the effects of impregnation ratio of AgNPs and TAC, pH solution, contact time, initial phosphate concentration and dose of AgNPs-AC on removing phosphate from aqueous solution. Results show that the best conditions for phosphate adsorption occurred at the impregnation ratio AgNPs/TAC of 3% w/w, pH 3, and contact time lasting 150 min. The maximum adsorption capacity of phosphate on AgNPs-TAC determined by the Langmuir model was 13.62 mg/g at an initial phosphate concentration of 30 mg/L. The adsorption isotherm of phosphate on AgNPs-TAC fits well with both the Langmuir and Sips models. The adsorption kinetics data were also described well by the pseudo-first-order and pseudo-second-order models with high correlation coefficients of 0.978 and 0.966, respectively. The adsorption process was controlled by chemisorption through complexes and ligand exchange mechanisms. This study suggests that AgNPs-TAC is a promising, low cost adsorbent for phosphate removal from aqueous solution

Marine Scientific Research in the South China Sea

The research project aims to identify options for multilateral marine science research (MSR) mechanisms in South China Sea that could be piloted and discussed with ASEAN partners. The project will enable the UK to expand engagement with ASEAN as a partner of choice for expertise on maritime issues

Identification of possible virulence marker from Campylobacter jejuni isolates

This is the final version of the article. Available from the publisher via the DOI in this record.A novel protein translocation system, the type-6 secretion system (T6SS), may play a role in virulence of Campylobacter jejuni. We investigated 181 C. jejuni isolates from humans, chickens, and environmental sources in Vietnam, Thailand, Pakistan, and the United Kingdom for T6SS. The marker was most prevalent in human and chicken isolates from Vietnam.The work was partly supported by the UK Biotechnology and Biological Sciences Research Council, award BB/1024631/1 to R.T., D.S., and O.C.; by a Wellcome Trust Institutional Strategic Support Award (WT097835MF); and by a studentship awarded to J.H. Mr Harrison is a PhD student at the University of Exeter under the supervision of D.S. His research focuses on using bioinformatic methods to investigate the comparative genomics of emerging diseases and plant-associated microbes

Multiple FadD Acyl-CoA Synthetases Contribute to Differential Fatty Acid Degradation and Virulence in Pseudomonas aeruginosa

A close interconnection between nutrient metabolism and virulence factor expression contributes to the pathophysiology of Pseudomonas aeruginosa as a successful pathogen. P. aeruginosa fatty acid (FA) degradation is complicated with multiple acyl-CoA synthetase homologs (FadDs) expressed in vivo in lung tissue during cystic fibrosis infections. The promoters of two genetically linked P. aeruginosa fadD genes (fadD1 and fadD2) were mapped and northern blot analysis indicated they could exist on two different transcripts. These FadDs contain ATP/AMP signature and FA-binding motifs highly homologous to those of the Escherichia coli FadD. Upon introduction into an E. coli fadD-/fadR- double mutant, both P. aeruginosa fadDs functionally complemented the E. coli fadD-/fadR- mutant, allowing degradation of different chain-length FAs. Chromosomal mutagenesis, growth analysis, induction studies, and determination of kinetic parameters suggested that FadD1 has a substrate preference for long-chain FAs while FadD2 prefers shorter-chain FAs. When compared to the wild type strain, the fadD2 mutant exhibited decreased production of lipase, protease, rhamnolipid and phospholipase, and retardation of both swimming and swarming motilities. Interestingly, fadD1 mutant showed only increased swarming motility. Growth analysis of the fadD mutants showed noticeable deficiencies in utilizing FAs and phosphatidylcholine (major components of lung surfactant) as the sole carbon source. This defect translated into decreased in vivo fitness of P. aeruginosa in a BALB/c mouse lung infection model, supporting the role of lipids as a significant nutrient source for this bacterium in vivo

Detection and monitoring of cancers with biosensors in Vietnam

Biosensors are able to provide fast, accurate and reliable detec-tions and monitoring of cancer cells, as well as to determine the effectiveness of anticancer chemotherapy agents in cancer treatments. These have attracted a great attention of research communities, especially in the capabilities of detecting the path-ogens, viruses and cancer cells in narrow scale that the conven-tional apparatus and techniques do not have. This paper pre-sents technologies and applications of biosensors for detections of cancer cells and related diseases, with the focus on the cur-rent research and technology development about biosensors in Vietnam, a typical developing country with a very high number of patients diagnosed with cancers in recent years, but having a very low cancer survival rate. The role of biosensors in early detections of diseases, cancer screening, diagnosis and treat-ment, is more and more important; especially it is estimated that by 2020, 60-70% new cases of cancers and nearly 70% of cancer deaths will be in economically disadvantaged countries. The paper is also aimed to open channels for the potential R&D collaborations with partners in Vietnam in the areas of innovative design and development of biosensors in particular and medical technology devices in general