Localization by Salp Swarm Optimization with Doppler Effect in Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately been widely used due to its abundant practice in methods that have to be spread over a large range. In any wireless application, the position precision of node is an important core component. Node localization intends to calculate the geographical coordinates of unknown nodes by the assistance of known nodes. In a multidimensional space, node localization is well-thought-out as an optimization problem that can be solved by relying on any metaheuristic’s algorithms for optimal outputs. This paper presents a new localization model using Salp Swarm optimization Algorithm with Doppler Effect (LOSSADE) that exploit the strengths of both methods. The Doppler effect iteratively considers distance between the nodes to determine the position of the nodes. The location of the salp leader and the prey will get updated using the Doppler shift. The performance validation of the presented approach simulated by MATLAB in the network environment with random node deployment. A detailed experimental analysis takes place and the results are investigated under a varying number of anchor nodes, and transmission range in the given search area. The obtained simulation results are compared over the traditional algorithm along with other the state-of-the-art methods shows that the proposed LOSSADE model depicts better localization performance in terms of robustness, accuracy in locating target node position and computation time.

A Biometric AsymmetricCryptosystem Software Module Based on Convolutional Neural Networks

During the research, the analysis of the existing biometric cryptographic systems was carried out. Some methods that help to generate biometric features were considered and compared with a cryptographic key. For comparing compact vectors of biometric images and cryptographic keys, the following methods are analyzed: designing and training of bidirectional associative memory; designing and training of single-layer and multilayer neural networks. As a result of comparative analysis of algorithms for extracting primary biometric features and comparing the generated image to a private key within the proposed authentication system, it was found that deep convolutional networks and neural network bidirectional associative memory are the most effective approach to process the data. In the research, an approach based on the integration of a biometric system and a cryptographic module was proposed, which allows using of a generated secret cryptographic key based on a biometric sample as the output of a neural network. The RSA algorithm is chosen to generate a private cryptographic key by use of convolutional neural networks and Python libraries. The software authentication module is implemented based on the client-server architecture using various internal Python libraries. Such authentication system should be used in systems where the user data and his valuable information resources are stored or where the user can perform certain valuable operations for which a cryptographic key is required. Proposed software module based on convolutional neural networks will be a perfect tool for ensuring the confidentiality of information and for all information-communication systems, because protecting information system from unauthorized access is one of the most pressing problems. This approach as software module solves the problem of secure generating and storing the secret key and author propose combination of the convolutional neural network with bidirectional associative memory, which is used to recognize the biometric sample, generate the image, and match it with a cryptographic key. The use of this software approach allows today to reduce the probability of errors of the first and second kind in authentication system and absolute number of errors was minimized by an average of 1,5 times. The proportion of correctly recognized images by the comparating together convolutional networks and neural network bidirectional associative memory in the authentication software module increased to 96,97%, which is on average from 1,08 times up to 1,01 times The authors further plan a number of scientific and technical solutions to develop and implement effective methods, tools to meet the requirements, principles and approaches to cybersecurity and cryptosystems for provide integrity and onfidentiality of information in experimental computer systems and networks.

Generalized Galois-Fibonacci Matrix Generators Pseudo-Random Sequences

The article discusses various options for constructing binary generators of pseudo-random numbers (PRN) based on the so-called generalized Galois and Fibonacci matrices. The terms "Galois matrix" and "Fibonacci matrix" are borrowed from the theory of cryptography, in which the linear feedback shift registers (LFSR) generators of the PRN according to the Galois and Fibonacci schemes are widely used. The matrix generators generate identical PRN sequences as the LFSR generators. The transition from classical to generalized matrix PRN generators (PRNG) is accompanied by expanding the variety of generators, leading to a significant increase in their cryptographic resistance. This effect is achieved both due to the rise in the number of elements forming matrices and because generalized matrices are synthesized based on primitive generating polynomials and polynomials that are not necessarily primitive. Classical LFSR generators of PRN (and their matrix equivalents) have a significant drawback: they are susceptible to Berlekamp-Messi (BM) attacks. Generalized matrix PRNG is free from BM attack. The last property is a consequence of such a feature of the BM algorithm. This algorithm for cracking classical LFSR generators of PRN solves the problem of calculating the only unknown – a primitive polynomial generating the generator. For variants of generalized matrix PRNG, it becomes necessary to determine two unknown parameters: both an irreducible polynomial and a forming element that produces a generalized matrix. This problem turns out to be unsolvable for the BM algorithm since it is designed to calculate only one unknown parameter. The research results are generalized for solving PRNG problems over a Galois field of odd characteristics.

A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems

Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection & Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and falsepositive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.

A Node Confident based IDS to Avoid Packet Drop Attacks for Wireless Sensor Network

Because of the great characteristics of Wireless Sensor Networks like easier to use and less cost of deployment, they have attracted the researchers to conduct the investigations and received the importance in various civilian and military applications. A number of security attacks have been involved due to the lack of centralized management in these networks. The packet drop attack is one of the attacks and it has a compromised node which drops the malicious packets. In WSNs, different techniques have been implemented to identify the packet drop attack but none of them provides the feasibility to stop or isolate their occurrence in the future. In recent times, the reputation systems provide the way to identify the trustworthy nodes for data forwarding. But the lack of data classification in the reputation systems affects the false positive rate. In this paper, a novel CONFIDENT SCORE based BAYESIAN FILTER NODE MONITORING AGENT (CFS-BFNMA) mechanism is introduced to identify & avoid the packet drop nodes and also to monitor the node behaviours to improve the false positive rate. The final CFS of a node is estimated based on the node past and threshold CFS values. The node monitoring agents (BFNMA) constantly monitors the forwarding behaviour of the nodes and assigns CFS based on the successful forwards. The NMA saves the copy of the data packets in their buffers before forwarding to the neighbour nodes to compare them. Also, this BFNMA analyses the traffic pattern of every round of transmission to improve the false positive rate. By comparing with other conventional security algorithms, the proposed mechanism has been improved the network security & false positive rate drastically based on the simulation results.

An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but nonoverlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With nonoverlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized KMeans with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

Selective Video Encryption Using the Cross Coupling of One-dimensional Logistic Maps

H.264 videos have been the most shared type of video format in recent times and hence its security is a major issue. The techniques presented in the recent times incur complex computations. The major research objective is to design an efficient Chaotic Selective Video Encryption (CSVE) technique which can result in a better visual degradation of the encrypted video with less computational complexity. In the proposed work, in order to secure the H.264 videos, two one-dimensional logistic maps are cross coupled in the chaotic encryption technique which uses a lookup table for data conversion. The technique is evaluated using different performance metrics like Peak Signal to Noise Ratio (PSNR), entropy, statistical analysis etc along with the traditional logistic map. The work is compared with some recent techniques in terms of PSNR and was found out that the proposed technique has better encryption effect.

Prospect for Using Low-Element Adaptive Antenna Systems for Radio Monitoring Stations

This article is devoted to the analysis of prospect to apply multifunctional adaptive antenna systems for radio monitoring stations. The review of publications done demonstrates that current antennas that are developed and used in radio monitoring systems to control and measure the parameters of electromagnetic radiation should be applicable to conduct accurate measurements in wide frequency range under the condition of interferences. The analysis shows that modern adaptive antenna systems are mostly developed for radar and telecommunications applications. In this context we consider possible ways to solve the problem of adapting radio monitoring devices to a complex electromagnetic environment using antenna systems with primary processing of received signals . It was found that the developers of the antennas, which are based on adaptive interference suppression methods, focus basically on the development and implementation of adaptation processes, limiting themselves only to solving electromagnetic compatibility problems. In such approach, the functions of direction finding and measurement of radiation field parameters important exactly for radio monitoring systems are mostly ignored. Therefore, this research area opens up a wide field for identifying new possibilities for constructing multifunctional antenna systems. Focusing on this direction of research, we consider as an example the constraction of a simple two-element adaptive antenna system, which can be used to measure the parameters of the electromagnetic field in radio monitoring systems. The main relations for the error of determining the direction of arrival of the interference signal with a simple two-element antenna are investigated. The influence of the stability of the antenna array parameters and functional units of signal processing onto the errors is estimated.

The Mathematical Model for Research of the UAV Longitudinal Moving

The paper presents one of the perspective directions of the development to modern aviation, which is connected with designing and producing unmanned aerial vehicles (UAV) of various functionalities for applying in both military and civilian spheres. The syntheses of UAV control systems, regardless of their type and purpose presumes creation of adequate mathematical models, first of all adequate aerodynamic mathematical models. In the paper results that forms and justify the aerodynamic mathematical model and as well as the results of building a general mathematical model of the longitudinal movement of the perspective UAV are presented. Also factors that forms the mathematical model on given aerodynamic, geometric, mass and inertial data for a hypothetical perspective altitude long-range UAV are submitted. Assessment of the impact of these data on the dynamic, temporal, and logarithmic frequency response UAV also has been given in this paper.

Multi Duty Cycle Scheduled Routing in Wireless Sensor Network-lifetime Maximization

Cluster-based protocols are best for applications that require reliability and a continuous functioning environment with a sustainable lifetime of WSN. The dynamic nature of the sensor node makes energy conservation a challenging issue. Sensor node scheduled based on sensing error for energy conservation compromise the accuracy of prediction. The high data accuracy achieved using a single duty cycle controller at each node with compromised throughput and increased routing overhead. Duty Cycle Controller managing a great number of control messages at the network level leads to control packet interference with data packet transmission, increasing packet drop and minimizing throughput. Also, the single-duty cycle controller at the network level leads to increased control overhead. The proposed multilevel cluster-based approach focuses on the appropriate cluster design, selection of cluster head, and sensor nodes scheduling based on sensing error. The proposed method applies a multi-duty cycle controller at each cluster level, and control messages handled are related to nodes in a cluster. Thus has less interference and packet drop leading to maximum throughput than existing methods. The simulation results demonstrated that the proposed method with sensor nodes scheduled at individual cluster levels using a multi-duty cycle controller exhibited improved network lifetime, throughput, and reduced energy consumption compared with the state-of-the-art techniques.