An Intrusion Detection System for IoT Using Deep Learning and Optimization Techniques | Chapter 2 | Contemporary Perspective on Science, Technology and Research Vol. 4

Internet of Things (IoT) has got more attention in the research field of computer science. The extreme increase in the IoT appliances across various factors, such as smart industries and health care appears with extensive security hazards. It is not only restricted to the attacks on confidentiality but also broadens to the attacks on performance and availability of the network. Hence, an intrusion detection mechanism is mandatory for identifying the attacks on IoT to offer effectual protection and security. Even though various intrusion detection methods are developed, achieving higher classification performance still results a challenging task. Therefore, an effective intrusion detection method is developed using the proposed Competitive Swarm Henry Optimization (CSHO)-based Deep Maxout network to find intruders in the IoT environment.

The process of detection strategy is carried out with the information captured by nodes distributed in network. Routing plays an essential role in transferring data from IoT devices to the base station (BS) to accomplish the task of intrusion discovery. It is the common approach used for increasing the energy efficiency in network communication. Here, the selection of optimal routing path is made using the FGGWO. The data received at BS undergoes the features selection phase and intrusion detection phase for detecting the intruders in IoT network. Here, deep learning classifier is used to identify network intruders.

The feature selection is mainly used to increase the computational efficiency and learning performance. The feature selection phase plays an essential factor in reducing dimension of data and to prevent overfitting. The input data Di is fed to feature selection module for selecting features using the Tversky index. After the selection of unique features, the mechanism of intrusion discovery is done with Deep Maxout. The training of Deep maxout network is done by CSHO algorithm. The working principle of HGSO depends on Henry's law. It has the facility to balance the exploration and exploitation phase by reproducing the gas huddling behavior.

The proposed solution offers security as a service and provides evidence in terms of scalability. However, the proposed method offers better results using the metrics, like energy, F-measure, precision, and recall as 0.1610, 0.9001, 0.9052, and 0.8993, respectively.

Author(s) Details:

Mythili Boopathi,
School of Information Technology, Vellore Institute of Technology, Katpadi, Vellore 632014, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V4/article/view/13135

Examining the Algorithms used for Routing and Assigning Wavelength in WDM Networks | Chapter 11 | Research Highlights in Mathematics and Computer Science Vol. 8

 In contemporary's communication, the telecommunications area is the most disputing, offering the highest amount of frequency range. In network communication, optical fiber plays a very important role.   Many guests have appeared with the best and brightest prototypes and test rigs to form optical WDM electronics. For a specific link request, a awareness and a path must be allocated in the WDM network. The Routing and Wavelength Assignment (RWA) question is referred to as the assignment of a intuitiveness and a path to a link request accompanying accessible resources. The purpose concerning this article search out examine the algorithms for any WDM network so that develop efficient awareness assignment clobbering protocols. As researchers, we determine to propose a comprehensive article review in order to expand a new algorithm for efficient distribution and appropriate wavelength usage, in addition to the routing of dossier packets from source to destination so that overcome all failures. We use simulations and evaluate visual fiber ideas methods before allocating, in addition to various losses established dynamic awareness allocation with minimum and maximum principles.

Author(s) Details:

M. Hamsaveni,
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Savita Choudhary,
Department of Computer Science and Engineering, Sir M Visveswaraya Institute of Technology, Bangalore, Karnataka, India.

Please see the link here: https://stm.bookpi.org/RHMCS-V8/article/view/10214

Advanced Routing Methods for Efficient Energy Harvesting in Mobile Adhoc Networks| Book Publisher International

 By utilising the routing metrics based on the routing table, the methods outlined are employed to decrease the amount of energy consumed. Here, the energy efficiency is calculated using the packet forwarding method. Because energy arrivals are unpredictable, the existing algorithms must be modified to route information data through the path of maximum energy nodes or through the fewest number of end transmission packets for communication systems that harvest energy. Networked systems allow for the regulation of transmission energy to maximise throughput, which internally explains the improvement in finite battery capacity storage. This is done by utilising knowledge of channel statuses and the temporal profile of energy arrivals. For dependable transmissions in MANET applications, comparative analysis of route minimization for the shortest efficient energy paths is analysed.

Author(s) Details:

N. Shyam Sunder Sagar,
GITAM Institute of Technology and Management, GITAM University, Hyderabad Campus, India.

Please see the link here: https://stm.bookpi.org/ARMEEHMAN/issue/view/817

A Study on Optimization of Truck-Drone Parcel Delivery Using Metaheuristics | Chapter 12 | Recent Advances in Mathematical Research and Computer Science Vol. 5

 The TSP-D, a version of the travelling salesman problem in drone-based delivery systems, is the subject of this study. The TSP-D is a combinatorial optimization problem in which a truck and a drone work together to deliver packages to clients in the shortest time possible. Because determining the best answer is NP-hard, the number of problems that can be solved optimally is restricted. As a result, metaheuristics are employed to tackle the issue. Metaheuristics are adaptive and intelligent algorithms that have been proven to work in a variety of situations. This paper provides a hybrid meta-heuristic solution to the TSP-D issue, where the answer is based on the Concorde TSP solver's optimal TSP solution. The greedy, randomised adaptive search technique (GRASP) with two local search alternatives is then used to find the delivery routes for the truck and the drone. The application of self-adaptive selection when searching the neighbourhood in GRASP is the work's key contribution. The suggested method was evaluated on 200 cases from the publicly available "Instances of TSP with Drone" benchmark with various parameters. The results were compared to cutting-edge algorithms. In terms of tour duration, nonparametric statistical tests revealed that the proposed strategy is equivalent to rival algorithms (p = 0:074). When the drone and truck are travelling at the same speed ((alpha) = 1), the proposed method performs better or similarly.

Author(S) Details

Sarab AlMuhaideb
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Taghreed Alhussan
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Sara Alamri
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Yara Altwaijry
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Lujain Aljarbou
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Haifa Alrayes
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

View Book:- https://stm.bookpi.org/RAMRCS-V5/article/view/4977