Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET

Mobile Ad-Hoc Networks (MANETs) due to their reconfigurable nature are being integrated into new and futuristic knowledge such as Internet of Things (IoT), cloud, reconfigurable networks, etc. To attain such credibility of integration, the routing protocols associated with these mobile nodes have to connect, perform and facilitate routing that offers a high level of security and resistance to all possible threats and security issues that may emanate in the network. One of the solutions used to maintain network security is intrusion detection systems (IDSs). This article primarily emphasis on the network's susceptibility to a suction assault known as a black hole attack. The investigations about the employment of intelligent agents called Honeypot Agent-based detection scheme (HPAS) with Long-Short Term Memory (LSTM) in identifying such assaults. Hence, the proposed method is named HPAS-LSTM, where honeypots are roaming virtual software managers that create Route Request (RREQ) packets to attract and entrap black hole attackers. Extensive model results utilizing the ns-2 simulator are used to demonstrate the presence of the suggested detection technique. The simulation outcomes demonstrate that the suggested technique outperforms current black hole detection methods in terms of throughput (TH), packet loss rate (PLR), packet delivery ratio (PDR), and total network delay (TND).

Pragmatic Security-Aware Cross-Layer Design for Wireless Networks from Vampire Attacks

Wireless networks rely on ad hoc communication in an emergency, such as a search and rescue or military missions. WLAN, WiMAX, and Bluetooth are often used in Ad Hoc networks. Using a TCP/IP wireless network poses several challenges. Packet loss in 802.11 may be caused by noise or the network. TCP/IP connects non-adjacent layers of the network, resolving cross-layer communication technology for cross-layer communication. It regulates data transmission energy. This structure solves an issue in various ways. It is often used to improve data transfer. Currently, the OSI reference model's layers and functions are not explicitly connected. Only DCL can send multimedia data via wireless networks. The research employs CLD to improve wireless security—invasions of ad hoc networks (MANETs). The research helps secure wireless MANs (MANETs), Vampire Attack Defense (VAP) algorithms. A Secure Cross-Layer Design SCLD-AHN protocol is included. The paper contributes to controlling security attacks in wireless Mobile Ad Hoc Networks (MANET's). In MANETs effectiveness of Vampire Attack Defense (VAP) algorithms is evaluated and analyzed. It also proposes a Secure Cross-Layer Design for the ad hoc networks (SCLD-AHN) protocol.

Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlation-based filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category.

High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing

In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms of distance between the main server and the other server, VM capacity and memory size. Then the VMs with best fitness functions are selected as target VMs for placing the migrated VMs. The proposed algorithms are implemented in Cloudsim and performance results show that PEVM-GSO algorithm attains reduced power consumption and response delay with improved CPU utilization.

Towards the Construction of Reed-Muller Code Based Symmetric Key FHE

Homomorphic encryption (HE) schemes became popular cryptographic primitives and very useful in variety of security applications. Homomorphic encryption based on coding theory have the advantages of faster computations due to the structural properties of the codes used. Several schemes are supporting unlimited Mod2 addition operations in literature. The present paper introduces Reed-Muller (RM) code based Mod2 multiplication operation thereby making RM code based HE scheme fully homomorphic. The representation of the codeword with necessary adaption to support unlimited number of Mod2​ multiplication operations is presented along with the scheme first. The correctness proof of the homomorphic operations along with experimental evaluation is also presented to demonstrate the practical aspects of the proposal.

A Modified Jellyfish Search Optimizer with Opposition Based Learning and Biased Passive Swarm Motion

Jellyfish Search Optimizer (JSO) is one of the latest nature inspired optimization algorithms. This paper aims to improve the convergence speed of the algorithm. For the purpose, it identifies two modifications to form a proposed variant. First, it proposes improvement of initial population using Opposition based Learning (OBL). Then it introduces a probability-based replacement of passive swarm motion into moves biased towards the global best. OBL enables the algorithm to start with an improved set of population. Biased moves towards global best improve the exploitation capability of the algorithm. The proposed variant has been tested over 30 benchmark functions and the real world problem of 10-bar truss structure design optimization. The proposed variant has also been compared with other algorithms from the literature for the 10-bar truss structure design. The results show that the proposed variant provides fast convergence for benchmark functions and accuracy better than many algorithms for truss structure design.

Consumer Conformity, Social Ties and EWOM in Digital Marketing

Digital marketing is a marketing technique that is widely discussed in the latest literature. The complexity of digital marketing creates new variables in an effort to increase purchase intention, such as consumer conformity, social ties and EWOM (Electronic Word of Mouth). This study aims to analyze the relationship between consumer conformity, social ties, and EWOM in digital marketing on the Shopee, Lazada, and Bukalapak platforms. Through quantitative methods using Structural Equation Modeling (SEM) analysis using AMOS software on 200 respondents consisting of users of the Shopee, Lazada, and Bukalapak platforms. This study analyzes 5 hypotheses, which is then statistically proven to have a significance value of < 0.05, which means that there is a significant relationship between the variables, thus all hypotheses are supported. The results indicate that social ties and EWOM can have a significant effect on consumer conformity and are also able to directly affect purchase intention. This research also highlights that consumer conformity has a significant effect on purchase intention in digital marketing.

A SIFT Description Approach for Non-Uniform Illumination and Other Invariants

The new improvement keypoint description technique of image-based recognition for rotation, viewpoint and non-uniform illumination situations is presented. The technique is relatively simple based on two procedures, i.e., the keypoint detection and the keypoint description procedure. The keypoint detection procedure is based on the SIFT approach, Top-Hat filtering, morphological operations and average filtering approach. Where this keypoint detection procedure can segment the targets from uneven illumination particle images. While the keypoint description procedures are described and implemented using the Hu moment invariants. Where the central moments are being unchanged under image translations. The sensitivity, accuracy and precision rate of data sets were evaluated and compared. The data set are provided by color image database with variants uniform and non-uniform illumination, viewpoint and rotation changes. The evaluative results show that the approach is superior to the other SIFTs in terms of uniform illumination, non-uniform illumination and other situations. Additionally, the paper demonstrates the high sensitivity of 100%, high accuracy of 83.33% and high precision rate of 80.00%. Comparisons to other SIFT approaches are also included.