Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System

A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique

Optimising routing and trustworthiness of ad hoc networks using swarm intelligence

This thesis was submitted for the degree of Doctor of Philsophy and awarded by Brunel UniversityThis thesis proposes different approaches to address routing and security of MANETs using swarm technology. The mobility and infrastructure-less of MANET as well as nodes misbehavior compose great challenges to routing and security protocols of such a network. The first approach addresses the problem of channel assignment in multichannel ad hoc networks with limited number of interfaces, where stable route are more preferred to be selected. The channel selection is based on link quality between the nodes. Geographical information is used with mapping algorithm in order to estimate and predict the links’ quality and routes life time, which is combined with Ant Colony Optimization (ACO) algorithm to find most stable route with high data rate. As a result, a better utilization of the channels is performed where the throughput increased up to 74% over ASAR protocol. A new smart data packet routing protocol is developed based on the River Formation Dynamics (RFD) algorithm. The RFD algorithm is a subset of swarm intelligence which mimics how rivers are created in nature. The protocol is a distributed swarm learning approach where data packets are smart enough to guide themselves through best available route in the network. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive data packets in order to maintain and find better routes. Data packets act like swarm agents (drops) where they carry their path information and update routing information without the need for backward agents. These data packets modify the routing information based on different network metrics. As a result, data packet can guide themselves through better routes. In the second approach, a hybrid ACO and RFD smart data packet routing protocol is developed where the protocol tries to find shortest path that is less congested to the destination. Simulation results show throughput improvement by 30% over AODV protocol and 13% over AntHocNet. Both delay and jitter have been improved more than 96% over AODV protocol. In order to overcome the problem of source routing introduced due to the use of the ACO algorithm, a solely RFD based distance vector protocol has been developed as a third approach. Moreover, the protocol separates reactive learned information from proactive learned information to add more reliability to data routing. To minimize the power consumption introduced due to the hybrid nature of the RFD routing protocol, a forth approach has been developed. This protocol tackles the problem of power consumption and adds packets delivery power minimization to the protocol based on RFD algorithm. Finally, a security model based on reputation and trust is added to the smart data packet protocol in order to detect misbehaving nodes. A trust system has been built based on the privilege offered by the RFD algorithm, where drops are always moving from higher altitude to lower one. Moreover, the distributed and undefined nature of the ad hoc network forces the nodes to obligate to cooperative behaviour in order not to be exposed. This system can easily and quickly detect misbehaving nodes according to altitude difference between active intermediate nodes

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions

Security and Privacy in Molecular Communication and Networking: Opportunities and Challenges

International audienceMolecular Communication (MC) is an emerging andpromising communication paradigm for several multi-disciplinarydomains like bio-medical, industry and military. Differently to thetraditional communication paradigm, the information is encodedon the molecules, that are then used as carriers of information.Novel approaches related to this new communication paradigmhave been proposed, mainly focusing on architectural aspects andcategorization of potential applications. So far, security and privacyaspects related to the molecular communication systems havenot been investigated at all and represent an open question thatneed to be addressed. The main motivation of this paper lies onproviding some first insights about security and privacy aspects ofMC systems, by highlighting the open issues and challenges andabove all by outlining some specific directions of potential solutions.Existing cryptographicmethods and security approaches arenot suitable for MC systems since do not consider the pecific issuesand challenges, that need ad-hoc solutions.We will discuss directionsin terms of potential solutions by trying to highlight themain advantages and potential drawbacks for each direction considered.We will try to answer to the main questions: 1) why thissolution can be exploited in the MC field to safeguard the systemand its reliability? 2) which are the main issues related to the specificapproach

A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attacks. The proposed technique exploits information gain ratio (IGR) and principal component analysis (PCA) for feature selection and a support vector machine (SVM)-based predictor model, which can also detect input traffic anomaly. A real benchmark dataset obtained from National Advanced IPv6 Center of Excellence laboratory is used to evaluate the proposed technique. The evaluation process is conducted with two experiments. The first experiment investigates the effect of IGR and PCA feature selection methods to identify the most contributed features for the SVM training model. The second experiment evaluates the capability of SVM to detect RA flooding attacks. The results show that the proposed technique demonstrates excellent detection accuracy and is thus an effective choice for detecting RA flooding attacks. The main contribution of this study is identification of a set of new features that are related to RA flooding attack by utilizing IGR and PCA algorithms. The proposed technique in this paper can effectively detect the presence of RA flooding attack in IPv6 network

Using Deception to Enhance Security: A Taxonomy, Model, and Novel Uses

As the convergence between our physical and digital worlds continue at a rapid pace, securing our digital information is vital to our prosperity. Most current typical computer systems are unwittingly helpful to attackers through their predictable responses. In everyday security, deception plays a prominent role in our lives and digital security is no different. The use of deception has been a cornerstone technique in many successful computer breaches. Phishing, social engineering, and drive-by-downloads are some prime examples. The work in this dissertation is structured to enhance the security of computer systems by using means of deception and deceit