Biblio

Mobile Adhoc networks (MANETs) comprises of mobile devices or nodes that are connected wirelessly and have no infrastructure. Detecting malicious activities in MANETs is a challenging task as they are vulnerable to attacks where the performance of the entire network degrades. Hence it is necessary to provide security to the network so that the nodes are prone to attack. Selecting a good routing protocol in MANET is also important as frequent change of topology causes the route reply to not arrive at the source node. In this paper, R-AODV (Reverse Adhoc On-Demand Distance Vector) protocol along with ECC (Elliptic Key Cryptography) algorithm is designed and implemented to detect and to prevent the malicious node and to secure data transmission against blackhole attack. The main objective is to keep the data packets secure. ECC provides a smaller key size compared to other public-key encryption and eliminates the requirement of pre-distributed keys also makes the path more secure against blackhole attacks in a MANET. The performance of this proposed system is simulated by using the NS-2.35 network simulator. Simulation results show that the proposed protocol provides good experimental results on various metrics like throughput, end-to-end delay, and PDR. Analysis of the results points to an improvement in the overall network performance.

Blackhole (BH) attacks are one of the most serious threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks are widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a detection-and-prevention method that uses local information shared with neighboring nodes. Our experiments show that the proposed method successfully detects and contains even smart BH threats. Consequently, the attack success rate decreases.

Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). Due to no fixed infrastructure of MANETs, a detection node playing as a collector should discover available routes to a collection node for data collection. Notably, route discovery suffers from many attacks (e.g., wormhole attack), thus the detection node should also collect securityrelated data during route discovery and analyze these data for determining reliable routes. However, few literatures provide incentives for security-related data collection in MANETs, and thus the detection node might not collect sufficient data, which greatly impacts the accuracy of attack detection and security measurement. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of RREQ forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization at the same time. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.

The Ad-hoc vehicle networks (VANETs) recently stressed communications and networking technologies. VANETs vary from MANETs in tasks, obstacles, system architecture and operation. Smart vehicles and RSUs communicate through unsafe wireless media. By nature, they are vulnerable to threats that can lead to life-threatening circumstances. Due to potentially bad impacts, security measures are needed to recognize these VANET assaults. In this review paper of VANET security, the new VANET approaches are summarized by addressing security complexities. Second, we're reviewing these possible threats and literature recognition mechanisms. Finally, the attacks and their effects are identified and clarified and the responses addressed together.

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.

Taking care of security at the cloud is a major issue that needs to be carefully considered and solved for both individuals as well as organizations. Organizations usually expect more trust from employees as well as customers in one hand. On the other hand, cloud users expect their private data is maintained and secured. Although this must be case, however, some malicious outsiders of the cloud as well as malicious insiders who are cloud internal users tend to disclose private data for their malicious uses. Although outsiders of the cloud should be a concern, however, the more serious problems come from Insiders whose malicious actions are more serious and sever. Hence, insiders' threats in the cloud should be the top most problem that needs to be tackled and resolved. This paper aims to find a proper solution for the insider threat problem in the cloud. The paper presents a Mobile Edge Computing (MEC) mitigation model as a solution that suits the specialized nature of this problem where the solution needs to be very close to the place where insiders reside. This in fact gives real-time responses to attack, and hence, reduces the overhead in the cloud.

Proliferation of the Internet usage is rapidly increasing, and it is necessary to support the performance requirements for multimedia applications, including lower latency, improved security, faster content retrieval, and adjustability to the traffic load. Nevertheless, because the current Internet architecture is a host-oriented one, it often fails to support the necessary demands such as fast content delivery. A promising networking paradigm called Information-Centric Networking (ICN) focuses on the name of the content itself rather than the location of that content. A distinguished alternative to this ICN concept is Content-Centric Networking (CCN) that exploits more of the performance requirements by using in-network caching and outperforms the current Internet in terms of content transfer time, traffic load control, mobility support, and efficient network management. In this paper, instead of using the saturated method of validating a theory by simulation, we present a testbed-based performance evaluation of the ICN network. We used several new functions of the proposed testbed to improve the performance of the basic CCN. In this paper, we also show that the proposed testbed architecture performs better in terms of content delivery time compared to the basic CCN architecture through graphical results.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based n ×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( 2.4 ×104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

This paper describes a novel distributed mobility management (DMM) scheme for the "named-object" information centric network (ICN) architecture in which the routers forward data based on unique identifiers which are dynamically mapped to the current network addresses of a device. The work proposes and evaluates two specific handover schemes namely, hard handoff with rebinding and soft handoff with multihoming intended to provide seamless data transfer with improved throughput during handovers. The evaluation of the proposed handover schemes using system simulation along with proof-of-concept implementation in ORBIT testbed is described. The proposed handoff and scheduling throughput gains are 12.5% and 44% respectively over multiple interfaces when compared to traditional IP network with equal share split scheme. The handover performance with respect to RTT and throughput demonstrate the benefits of clean slate network architecture for beyond 5G networks.

Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

MANETs are wireless networks, providing properties such as self-configuration, mobility, and flexibility to the network, which make them a popular and widely used technique. As the usage and popularity of the networks increases, security becomes the most important factor to be concerned. For the sake of security, several protocols and methodologies have been developed for the networks. Along with the increase in security mechanisms, the number of attacks and attackers also increases and hence the threat to the network and secure communication within it increases as well. Some of the attacks have been resolved by the proposed methodologies but some are still a severe threat to the framework, one such attack is Black Hole Attack. The proposed work integrates the SUPERMAN (Security Using Pre-Existing Routing for Mobile Ad-hoc Networks) framework with appropriate methodology to detect and prevent the network from the Black Hole Attack. The mechanism is based on the AODV (Ad-hoc On-demand Distance Vector) routing protocol. In the methodology, the source node uses two network routes, from the source to the destination, one for sending the data packet and another for observing the intermediate nodes of the initial route. If any node is found to be a Black Hole node, then the route is dropped and the node is added to the Black Hole list and a new route to send the data packet to the destination is discovered.

Self-organising networks, such as mobile ad-hoc networks (MANETs), are growing more and more in importance each day. However, due to their nature and constraints MANETs are vulnerable to a wide array of attacks, such as black hole attacks. Furthermore, there are numerous routing protocols in use in MANETs, and what works for one might not for another. In this paper, we present a review of previous surveys of black hole attack solutions, followed by a collation of recently published papers categorised by original routing protocol and evaluated on a set of common metrics. Finally, we suggest areas for further research.

The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.

Augmented reality (AR) has been identified as a key technology to enhance worker utility in the context of increasing automation of repeatable procedures. AR can achieve this by assisting the user in performing complex and frequently changing procedures. Crucial to the success of procedure assistance AR applications is user acceptability, which can be measured by user quality of experience (QoE). An active research topic in QoE is the identification of implicit metrics that can be used to continuously infer user QoE during a multimedia experience. A user's QoE is linked to their affective state. Affective state is reflected in facial expressions. Emotions shown in micro facial expressions resemble those expressed in normal expressions but are distinguished from them by their brief duration. The novelty of this work lies in the evaluation of micro facial expressions as a continuous QoE metric by means of correlation analysis to the more traditional and accepted post-experience self-reporting. In this work, an optimal Rubik's Cube solver AR application was used as a proof of concept for complex procedure assistance. This was compared with a paper-based procedure assistance control. QoE expressed by affect in normal and micro facial expressions was evaluated through correlation analysis with post-experience reports. The results show that the AR application yielded higher task success rates and shorter task durations. Micro facial expressions reflecting disgust correlated moderately to the questionnaire responses for instruction disinterest in the AR application.

Mobile edge computing (MEC), an emerging technology, has the characteristics of low latency, mobile energy savings, and context-awareness. As a type of access network, wireless mesh network (WMN) has gained wide attention due to its flexible network architecture, low deployment cost, and self-organization. The combination of MEC and WMN can solve the shortcomings of traditional wireless communication such as storage capacity, privacy, and security. In this paper, we propose a location-aware (LA) algorithm to cognize the location and a location-aware offloading policy (LAOP) algorithm considering the energy consumption and time delay. Simulation results show that the proposed LAOP algorithm can obtain a higher completion rate and lower average processing delay compared with the other two methods.

The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.