Key management for beyond 5G mobile small cells: a survey

The highly anticipated 5G network is projected to be introduced in 2020. 5G stakeholders are unanimous that densification of mobile networks is the way forward. The densification will be realized by means of small cell technology, and it is capable of providing coverage with a high data capacity. The EU-funded H2020-MSCA project “SECRET” introduced covering the urban landscape with mobile small cells, since these take advantages of the dynamic network topology and optimizes network services in a cost-effective fashion. By taking advantage of the device-to-device communications technology, large amounts of data can be transmitted over multiple hops and, therefore, offload the general network. However, this introduction of mobile small cells presents various security and privacy challenges. Cryptographic security solutions are capable of solving these as long as they are supported by a key management scheme. It is assumed that the network infrastructure and mobile devices from network users are unable to act as a centralized trust anchor since these are vulnerable targets to malicious attacks. Security must, therefore, be guaranteed by means of a key management scheme that decentralizes trust. Therefore, this paper surveys the state-of-the-art key management schemes proposed for similar network architectures (e.g., mobile ad hoc networks and ad hoc device-to-device networks) that decentralize trust. Furthermore, these key management schemes are evaluated for adaptability in a network of mobile small cells

State-of-the-art authentication and verification schemes in VANETs:A survey

Vehicular Ad-Hoc Networks (VANETs), a subset of Mobile Ad-Hoc Networks (MANETs), are wireless networks formed around moving vehicles, enabling communication between vehicles, roadside infrastructure, and servers. With the rise of autonomous and connected vehicles, security concerns surrounding VANETs have grown. VANETs still face challenges related to privacy with full-scale deployment due to a lack of user trust. Critical factors shaping VANETs include their dynamic topology and high mobility characteristics. Authentication protocols emerge as the cornerstone of enabling the secure transmission of entities within a VANET. Despite concerted efforts, there remains a need to incorporate verification approaches for refining authentication protocols. Formal verification constitutes a mathematical approach enabling developers to validate protocols and rectify design errors with precision. Therefore, this review focuses on authentication protocols as a pivotal element for securing entity transmission within VANETs. It presents a comparative analysis of existing protocols, identifies research gaps, and introduces a novel framework that incorporates formal verification and threat modeling. The review considers key factors influencing security, sheds light on ongoing challenges, and emphasises the significance of user trust. The proposed framework not only enhances VANET security but also contributes to the growing field of formal verification in the automotive domain. As the outcomes of this study, several research gaps, challenges, and future research directions are identified. These insights would offer valuable guidance for researchers to establish secure authentication communication within VANETs

Performance Evaluation of Underwater Routing Protocols DHRP, LASR and DFR for Underwater Wireless Sensor Network using MATLAB

Communication issues in Underwater Wireless Sensor Networks (UWSNs) are the main problem. In this research paper and we proposed “Dolphin Heterogeneous Routing Protocol” (DHRP) and it determine the most efficient path to destination, it balance the energy and it increase the lifetime of nodes. Due to the lack of growth in underwater wireless communications, Communication cables are still used for underwater communication. The use of wires to ensure the communication of sensor nodes at the ocean's depths is extremely costly. In underwater wireless sensor networks, determining the optimum route to convey sensed data to the destination in the shortest amount of time has become a major difficulty (UWSN). Because of the challenging communication medium, UWSN routing protocols are incompatible with those used in traditional sensor networks. Existing routing protocols have the problem of requiring more energy to send data packets, as well as experiencing higher delays due to the selection of ineffective routes. This research introduces the Dolphin Heterogeneous Routing Protocol (DHRP) to tackle the routing issues faced by UWSN. The swarming behavior of dolphins in search of food is the inspiration for DHRP. In order to find the best route in UWSN, DHRP goes through six essential processes are initialization, searching, calling, reception, predation and termination

A Unique Pipeline Model to Improve Anomaly Detection in High Dimensional Data

This paper presents a comprehensive method for dimension reduction and detecting anomalies in high-dimensional data (on healthcare datasets) using R. Realizing that traditional linear methods such as Principal Component Analysis (PCA) often ignore the complexity of the non-linear manifold of the data, our approach exploits iterative learning, on the belief that high-dimensional data is largely based on a low-dimensional manifold. The methodology starts by preparing the data using R libraries like Keras, dplyr, and ggplot2, addressing challenges like missing values ??and visualizing meaningful information. Using the Mahalanobis distance, the paper identifies and removes country-specific outliers. The pipelined model integrates Principal Component Analysis (PCA) for data transformation and combines an Autoencoder with t-SNE for dimensionality reduction. This refined dataset is then used to train a Multi-Layer Perceptron (MLP) artificial neural network, which facilitates anomaly detection based on reconstruction errors, illustrated by the point cloud. Additionally, the paper explores metric multidimensional scaling using artificial neural networks, tests large datasets such as healthcare and wine, and compares the results of the work using conventional techniques. This study highlights the effectiveness of integrating various pre-processing, visualization, and artificial neural network strategies through R for effective anomaly detection

Ensemble Approach for DDoS Attack Detection in Cloud Computing Using Random Forest and GWO

When multiple technologies are added to a traditional network, it becomes increasingly difficult to meet newly imposed requirements, such as those regarding security. Since the widespread adoption of telecommunication technologies for the past decade, there have been an enhancement in the number of security threats that are more appealing. However, many new security concerns have arisen as a consequence of the introduction of the novel technology. One of the most significant of these is the potential for distributed denial of service attacks. Therefore, a DDoS detection method based on Random Forest Classifier and Grey Wolf Optimization algorithms in this work was developed to mitigate the DDoS threat. The results of the evaluation show that the Random Forest Classifier can achieve substantial performance improvements with respect to 99.96% accuracy. Comparison is also made to several state-of-the-art techniques for detecting of DDoS attacks for the real dataset

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This doctoral research therefore aims to advance the state of the art in routing by proposing a lightweight, reliable routing protocol for WMSN. Ensuring a reliable path between the source and the destination requires making trustaware routing decisions to avoid untrustworthy paths. A lightweight and effective Trust Management System (TMS) has been developed to evaluate the trust relationship between the sensor nodes with a view to differentiating between trustworthy nodes and untrustworthy ones. Moreover, a resource-conservative Reinforcement Learning (RL) model has been proposed to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, with a view to addressing the inborn overestimation problem in Q-learning-based routing protocols, we adopted double Q-learning to overcome the positive bias of using a single estimator. An energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Finally, a realistic trust management testbed has been developed to overcome the limitations of using numerical analysis to evaluate proposed trust management schemes, particularly in the context of WMSN. The proposed testbed has been developed as an additional module to the NS-3 simulator to fulfill usability, generalisability, flexibility, scalability and high-performance requirements

Secure Data Aggregation in Vehicular-Adhoc Networks: A Survey

AbstractVehicular ad hoc networks (VANETs) are an upcoming technology that is gaining momentum in recent years. That may be the reason that the network attracts more and more attention from both industry and academia. Due to the limited bandwidth of wireless communication medium, scalability is a major problem. Data aggregation is a solution to this. The goal of data aggregation is to combine the messages and disseminate this in larger region. While doing aggregation integrity of the information can not be easily verified and attacks may be possible. Hence aggregation must be secure. Although there are several surveys covering VANETs, they do not concentrate on security issues specifically on data aggregation. In this paper, we discuss and analyse various data aggregation techniques and their solutions

An Efficient Encryption System on 2D Sine Logistic Map based Diffusion

An optimal cryptographic model is proposed, enabling the feature of 2D sine logistic map-based diffusion algorithm. The 2D sine logistic map process is merged with the algorithm as it has the ability to provide random number generator as well as to overcome blank. The previous existing models based on image encryption use to work on raw images but without alteration for the process of confusion and diffusion. The main disadvantage as the nearby pixel values for an image always remains similar. This issue is resolved by a Pseudo random generator process which is based on key stream that alters pixel value. Furthermore 2D sine logistic map based diffusion process has shown an improvement in the key sensitivity and the complex relationships that use to get developed between cipher and test image.2D sine logistic map with diffusion method used to keep pixels intact with each other to such an extent as even a single bit modification in the intensity value of an original image pixel will lead to a huge change in most of the pixels of the cipher and thus makes the model very sensitive to make any changes in the pixel value or secret key for an image.  As seen and analyzed with a variety of test results that strategic model used for encryption can easily encrypt the plain image into a cipher of a random binary sequence