A Survey on Emulation Testbeds for Mobile Ad-hoc Networks

AbstractMobile Ad hoc Network (MANET) can be said as a collection of mobile nodes, which builds a dynamic topology and a A resource constrained network. In this paper, we present a survey of various testbeds for Mobile Ad hoc Networks. Emulator provides environment without modifications to the software and validates software solutions for ad hoc network. A field test will show rather the simulation work is going on right track or not and going from the simulator to the real thing directly to analyze the performance and compare the results of routing protocols and mobility models. Analyzing and choosing an appropriate emulator according to the given environment is a time-consuming process. We contribute a survey of emulation testbeds for the choice of appropriate research tools in the mobile ad hoc networks

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Real-time QoS Routing Scheme in SDN-based Robotic Cyber-Physical Systems

Industrial cyber-physical systems (CPS) have gained enormous attention of manufacturers in recent years due to their automation and cost reduction capabilities in the fourth industrial revolution (Industry 4.0). Such an industrial network of connected cyber and physical components may consist of highly expensive components such as robots. In order to provide efficient communication in such a network, it is imperative to improve the Quality-of-Service (QoS). Software Defined Networking (SDN) has become a key technology in realizing QoS concepts in a dynamic fashion by allowing a centralized controller to program each flow with a unified interface. However, state-of-the-art solutions do not effectively use the centralized visibility of SDN to fulfill QoS requirements of such industrial networks. In this paper, we propose an SDN-based routing mechanism which attempts to improve QoS in robotic cyber-physical systems which have hard real-time requirements. We exploit the SDN capabilities to dynamically select paths based on current link parameters in order to improve the QoS in such delay-constrained networks. We verify the efficiency of the proposed approach on a realistic industrial OpenFlow topology. Our experiments reveal that the proposed approach significantly outperforms an existing delay-based routing mechanism in terms of average throughput, end-to-end delay and jitter. The proposed solution would prove to be significant for the industrial applications in robotic cyber-physical systems

A Composite Trust Model for Secure Routing in Mobile Ad-Hoc Networks

It is imperative to address the issue of secure routing in mobile ad-hoc networks (MANETs) where the nodes seek for cooperative and trusted behaviour from the peer nodes in the absence of well-established infrastructure and centralized authority. Due to the inherent absence of security considerations in the traditional ad-hoc routing protocols, providing security and reliability in the routing of data packets is a major challenge. This work addresses this issue by proposing a composite trust metric based on the concept of social trust and quality-of-service (QoS) trust. Extended from the ad-hoc on-demand distance vector (AODV) routing protocol, we propose an enhanced trust-based model integrated with an attack-pattern discovery mechanism, which attempts to mitigate the adversaries craving to carry out distinct types of packet-forwarding misbehaviours. We present the detailed mode of operations of three distinct adversary models against which the proposed scheme is evaluated. Simulation results under different network conditions depict that the combination of social and QoS trust components provides significant improvement in packet delivery ratio, routing overhead, and energy consumption compared to an existing trust-based scheme

Managing Industrial Communication Delays with Software-Defined Networking

Recent technological advances have fostered the development of complex industrial cyber-physical systems which demand real-time communication with delay guarantees. The consequences of delay requirement violation in such systems may become increasingly severe. In this paper, we propose a contract-based fault-resilient methodology which aims at managing the communication delays of real-time flows in industries. With this objective, we present a light-weight mechanism to estimate end-to-end delay in the network in which the clocks of the switches are not synchronized. The mechanism aims at providing high level of accuracy with lower communication overhead. We then propose a contract-based framework using software-defined networking where the components are associated with delay contracts and a resilience manager. The proposed resilience management framework contains: (1) contracts which state guarantees about components behaviors, (2) observers which are responsible to detect contract failure (fault), (3) monitors to detect events such as run-time changes in the delay requirements and link failure, (4) control logic to take suitable decisions based on the type of the fault, (5) resilience manager to decide response strategies containing the best course of action as per the control logic decision. Finally, we present a delay-aware path finding algorithm which is used to route/reroute the real-time flows to provide resiliency in the case of faults and, to adapt to the changes in the network state. Performance of the proposed framework is evaluated with the Ryu SDN controller and Mininet network emulator

A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users

Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise, depending on the current healthcare environment. A Mobile Application (Mobile App) based recommendation system is a technology that allows users to select an apt activity that might suit their preferences. However, most of the current applications require constant input from end-users and struggle to include those who have hectic schedules or are not dedicated and self-motivated. This research introduces a methodology that uses a �Selective Cluster Cube� recommender system to intelligently monitor and classify user behavior by collecting accelerometer data and synchronizing with its calendar. We suggest customized daily workouts based on historical user and related user habits, interests, physical status, and accessibility. Simultaneously, the exposure of customer requirements to the server is also a significant concern. Developing privacy-preserving protocols with basic cryptographic techniques (e.g., protected multi-party computing or HE) is a standard solution to address privacy issues, but in combination with state-of-the-art advising frameworks, it frequently provides far-reaching solutions. This paper proposes a novel framework, a Privacy Protected Recommendation System (PRIPRO), that employs HE for securing private user data. The PRIPRO model is compared for accuracy and robustness using standard evaluation parameters against three datasets