Cyber Security and Digital Forensics

In the past few years, there has been a tremendous increase in cyberattacks and cybercrimes. Technology is changing at a very fast pace, thus inviting more advanced cyberattacks. Any event that is triggered on the system is recorded in the form of logs in log files. It may be any warning, any alert, and information, and all the things get stored in the logs. Therefore, from the security point of view, analyzing the logs plays a crucial role in the forensic investigation or for analytics purposes also. This paper highlights a systematic approach for analyzing the log files using a string-matching algorithm and regular expressions. Thus, it helps in log analysis, management, and analytics for future reference. Analyzing logs in a systematic way is always crucial in digital forensics, and it will help in the smooth conduction of forensic investigations.

In this modern era, security has gotten to be the foremost broadly concerned in each domain as recently approaching malware postures a danger to the systems. So our fundamental concern is to identify and anticipate a malware assault on the system. As the polymorphic worm postures, an enormous challenge to identify as they have more than one occurrence and exceptionally expansive endeavors is required to detain every occurrence and to generate signatures. This work proposes malicious attack detection and prevention using honeypot technology. We have proposed a double-honeynet framework, which can distinguish and avoid modern worms. We apply system call analysis to detect malware which mostly focuses on the polymorphic and metamorphic virus rather than utilizing a signature-based approach.

With the advent of fog computing, various e-governance services get influenced as fog provides new service delivery models and new ways to interact with the citizens. Uttarakhand, as a state of 86% hilly region and 65% of forest area, got geographical conditions that are not so favored for cloud-enabled technologies, because cloud needs regular and high bandwidth Internet connectivity. Fog computing can be a key player, in terms of providing e-Governance service. E-Health is the major service provided under the e-governance platform. With the application of Internet-enabled and IoT-based e-Healthcare systems in the state like Uttarakhand, there can be a drastic improvement in health services. E-Health services require real-time processing, low latency, high consistency, and high data rate, and these all parameters are fulfilled by fog computing. There are many kinds of research that describe how fog can be used in e-Healthcare systems. In this research paper, we discuss fog computing in the context of Uttarakhand. Our main concerns are security issues and challenges faced by fog computing while using the E-Healthcare system in hilly areas of Uttarakhand. In studying those challenges and security issues, the technologies related to fog computing are also discussed.

Android devices remain vulnerable to an increasing number of unidentified Android malware that has greatly compromised the efficacy of traditional security measures. Most classifiers are programmed to use a training process to learn from the data itself, since full expert insight to evaluate classifier parameters is difficult or impossible. This paper proposes a methodology which is a hybrid of machine learning and swarm intelligence. This methodology combines the successful exploration algorithm called the particle swarm optimization (PSO) with the extreme learning machine (ELM) classifier. ELM is a single-hidden layer feedforward neural network (FFNN) consisting of large number of hidden layer neurons, which has proved to be an excellent classifier. In this research, the optimum values of input weights and biases for the ELM classifier are determined using PSO, and it further improves the classifier’s accuracy. The dataset consists of 1700 benign and 418 malicious Android applications from which over 15,000 features of different types were extracted, and feature selection techniques were applied. The aforementioned dataset was used to experiment our proposed model, and significant results were achieved.

An optical asymmetric cryptosystem built on chaotic zone plate phase mask (CZPPM) has been proposed. Here the pixels of an image are shuffled by employing Arnold transform $$\left( {{\text{AT}}^{\omega } } \right)$$ AT ω and is then modulated with the CZPPM featuring in the gyrator Transform domain (GT). This increases randomness and adds chaotic parameters that make the system highly secure. The proposed system strengthens the security of the cryptosystem and does not permit the attacker to retrieve the initial image without the expertise of keys. The robustness of the projected cryptosystem has been investigated and validated based on an extra degree of freedom by simulating on MATLAB 9.9.0 (R2020b), and investigational outcomes have been shown to emphasize the efficacy of the algorithm.

Digital watermarking is a technique to hide and transmit the data in such a manner that attackers cannot perceive it. The watermarks can be used for authentication, and the creator/owner of the digital data can claim the rights, in case of any dispute. The reversible digital watermarking ensures the reusability of the cover media. In this research paper, we are suggesting a more robust method of watermarking using the combination of LWT-SVD. The digest of the message is generated using MD5 algorithm, and a quantum representation is used as the trap door. The results are measured for the existing quantum technique, LSB quantum watermarking, and the proposed algorithm. We have compared the results for MSE, SSIM, and PSNR in the analysis section. The improvements can be seen in the results persistently.

A fault is any type of bug or error or failure that occurs in the system when any hardware is failed and requires replacement or strained to reboot or software fails to give the result. System failure occurs when the fault in system is not revealed and corrected in time. Software fault tolerance is the major research area in software development industry. Software fault tolerance enables a system to operate efficiently if any fault exists in the system and makes 011.0210 s a system capable to protect against any type of accidental or malicious destruction of the information in system. Many techniques of fault tolerance and recovery have been proposed in work of literatures in order to ensure efficient working of software. The main objective of this paper is to propose fault-tolerant framework based on N-version and recovery block mechanism for automatic test case generation. Simulation results show that proposed framework improves the reliability in terms of elapsed time, classification accuracy, and mean square error.

Computer-aided system is a subject of great importance and extensive requirement. Nowadays, deep learning and machine learning have gained quite a knack in people’s eye and widely used among them. Gone are the occasions when the products were utilized for complex count issues or graphical portrayal alone. And Chatbots are proven revolutionary in our day-to-day lives where they are present in health, career, insurance and customer care support. In this paper, we have built up a Health-Bot using RNN network and Keras classifier. During such a pandemic period when there is an enormous crowd present in hospitals, people can get themselves checked at their homes with this interactive language system. Neural network adds more exactness to our work and reactions. And we further implemented our model on StreamLit which is an open-source framework for machine learning and deep learning.

In this modern era, all the organizations seem to be shifting their data and services to cloud because of the increase in information leaks and data thefts. It has become easier for intruders to break into the organization’s data stored locally. As cloud provides a significant hike in security levels, it is slowly turning into the future of data storage. It has become crucial to acknowledge the issues regarding cloud security. Cloud security is the security of all the services provided by the cloud—storage, servers, networking, and databases. In this paper, the issues related to the same are covered along with the mitigation proposed to deal with those problems. The key to the overall security of the cloud is identity access management (IAM). IAM is inherently more secure than a simple username and password combinations because of the profile of information IAM collects. It can make access to data and networks a much more convenient process.

It is no longer a story that the criminal act is now the order of the day in Nigeria with the way mobile phones and network applications are being used. A lot of Nigerian citizens have in one way or the other fallen prey to this crime through online fraud, hacking into the person’s account, retrieving vital information, and the likes. It is, therefore, necessary to detect some of these crimes, reveal them, and proffer possible solutions on how to avoid them, and possibly, how to solve such problems. To do this, the most recent papers were reviewed to create awareness of such criminal act detection and how to curb such activities to create a friendly environment for the usage of mobile phones and network applications without any panic. It is also evident that with the stated methods in this paper, such criminal acts would have been greatly mitigated.

Mobile Ad hoc networks (MANETs) are wireless/infrastructure-less and resource-constraint, having collection of nodes with high mobility feature (Ramanathan and Redi in IEEE Commun Magaz 40(5) 2002). It is a challenge to have efficient intrusion detection system (IDS) for such wireless and mobile architecture of systems. Researchers have presented in their research that the fuzzy logic-based intrusion detection systems are more adoptable to MANET’s application because behavior of any mobile node may be visualized in fuzziness characteristics. It is required to design robust IDS system which can sustain and can work efficiently in MANET environments. The work presents the selection of suitable protocol features and fuzzy rules generation which exhibits substantial role for precision of the fuzzy logic-based intrusion detection system (FIDS). Here, set of fuzzy rules have been proposed to protect network against blackhole attack. These set of rules are created using three AODV critical attribute which are rate of RREQ, RREP and Sequence number value. The proposed FIDS, thereafter, evaluated using ns2 simulator and are found efficient to detect and isolate the attacker node from the network. The deployment of FIDS has resulted in increase of throughput of the network.

Phishing is a form of online fraudsters try to mimic genuine websites and get the user’s sensitive and personal information. Such malicious users post phishing links and advertisements which may transmit malwares and viruses. Not only individual users are affected by such fraudsters but also several organizations and corporate those depend on internet for services and sales. Such malicious users adopt social engineering skills such as sending emails or by online pop-up advertisements. A number of solutions for detecting phishing websites are mentioned such as non-technical methods, method of black listing and white listing and machine learning techniques. In this paper, data mining techniques in machine learning have been applied to detect phishing websites. One of the data mining techniques is a classification which seems to have a high potential in detecting phishing websites. Here, bagging, C4.5 (J48) and random forest classifiers are tested on the phishing dataset. The dataset is taken from UCI repository which has 1353 instances. The C4.5 classification model has the highest accuracy rate of 90.8%.

Farmers face well-known challenges of crop protection from insect pests, diseases, and weeds along with protection from contrary weather conditions like hail or frost. However, they face another important challenge of protecting crops from wild animals that may cause severe damage to their grown crops by feeding on plant parts or trampling over the crops. As most of farmers stay away from their fields, continuous monitoring of fields is not possible due to distance as well as costs involved to appoint manpower for this purpose. Present technologies have made it possible to provide low cost, easy to install, and user friendly solution to such problems. This paper aims to develop and install IoT-based security system for agriculture fields capable to detect and communicate presence of wild animals using PIR sensor and GSM module. It generates SMS alerts on the farmer’s mobile phone whenever some animal crosses specific area. It helps farmers to take timely action for crop protection. The security system is deployed in real environment to validate its applicability and possible future extensions.

Transactional data is set-valued data which is generated from retail store, healthcare, etc. The data needs to be published to extract useful information from the data. The data contain some sensitive information about the individual, and if leaked, then it will cause serious implications to the privacy of an individual. Therefore, it is required to protect the user’s privacy on the published data while ensuring the data should remain useful for analysis purpose. The paper proposes efficient clustering method using ant-colony-based clustering algorithm to bring similar transactions in same equivalence class/cluster. Finally, we can achieve privacy with minimal information loss. The approach has been tested on INFORMS dataset and compared with the Disassociation. The result shows that the more information is preserved as compared to Disassociation approach.

With the advancement of innovation, these days respectability of digitized information has been addressed from various perspectives. With the assistance of innovation, the integrity level of advanced video can be disturbed from various perspectives. Video tempering performs with two different ways: one is frame level and other is changing the sequence of frames to hide or highlight the specific aim of the original video. In some cases, the original information of video frames is altered and afterward pasted at some other area of the same video. The proposed framework completed two stages of calculation to distinguish the doctored frames. Feature extraction is executed as the initial steps of the proposed framework. Correlation of separated features will assist with distinguishing the altered frames. In the subsequent advance, each frame will be compared with one before and after frames with the assistance of extricated highlights to check the measure of changes in the tempered frames all through the video stream. To check the uprightness of a video, include correlation and RANSAC strategies are utilized and it shows huge outcomes distinctive when checked with the video which altered and not altered. With the utilization of this strategy, we can ensure the originality of the advanced video when it assumes a significant part as proof or verification in specific conditions.

Messaging systems are a crucial part of many distributed systems. Various implementations of open-source message brokers, message queues, and messaging protocols are in wide use to facilitate highly available and reliable messaging. We evaluated the architectures of two popular open-source message brokers used in the cloud: Apache Pulsar and NATS, studying qualitative differences like broker distribution, client architecture, messaging features, etc., and benchmark the two message brokers in realistic deployments.

Geographic information systems contain data of varying degrees of secrecy. Access to data is regulated by different documents. The data is loaded and processed in real time and booked in several hundred layers. On the other hand, geographic information systems are integrated into almost all existing information systems and tend to evolve into spatial data infrastructure. This circumstance imposes strict requirements on ensuring the integrity, confidentiality, and availability of data, makes it extremely difficult to make a decision on granting access of subjects to data, on the allocation of appropriate access rights. The article analyzes the problems of providing access to geographic information systems and suggests approaches to their solution.

This paper presents the utilization of the novel rear mounted triple phase masking procedure to increase the security of traditional double random phase encoding (DRPE) scheme with a variation of hybrid transform. Two chaotic random phase masks and a deterministic phase mask are used in the scheme to enhance its security as opposed to traditional DRPE which uses random phase masks. From the previous studies on cryptanalysis of DRPE, it is clear that the second lens used in it proves to be irrational which leads to cryptanalytic attacks on DRPE. The enhanced framework discards the invalidation of the second lens and therefore reinforces DRPE security. The hybrid transform is a combination of fractional Hartley transform and Gyrator transform that improves the quality of the cryptosystem. The carried out simulations exhibit the efficacy of the designed symmetric cryptosystem.

As internet of things (IoT) is extending internet connectivity beyond standard devices; the secure data transmission between IoT devices becomes more challenging. However, most of the traditional upper layer security schemes are computationally complex and increase the latency. Moreover, the security provided at upper layer is software implemented, and its strength depends on complexity of the encryption algorithm. This is a bottleneck situation for low-power IoT applications. As IoT uses many remote sensors which operate on battery power; IEEE 802.15.4 standard is gaining attention because of low-power consumption. An 802.15.4 protocol defines medium access control (MAC) and physical layer (PHY) specifications, and is designed to allow low-power, low-cost short range communication. Basic encryption and authentication in 802.15.4 are provided by link layer. Hence, considerable attention is required to study 802.15.4 specifications that can be used to provide alternative methods of security. So this paper is motivated to study the importance of 802.15.4 PHY. Firstly, we review the different physical layer security (PLS) schemes. Secondly, we present the concept of physical layer encryption (PLE) and further we analyze and compare the implementation of different PLE schemes in wireless standards. Later, we will try to give insights of 802.15.4 security standards and bring out the drawbacks of AES based link-layer security. Finally, we present the design and implementation aspects of 802.15.4 transceiver hardware architecture by considering performance and security.

Robotic process automation (RPA) is an emerging field in any industry that takes care of automation of the monotonous jobs. This can help to optimize the resources utilization for the organization, save the cost, time and improve the accuracy and quality of the jobs performed. However, lack of security in the implementation and the management of these RPAs shall affect the business in an adverse way. In the current scenario, privacy has also taken important role, and allowing bots (RPA) to have an access to these privacy applications can lead to regulatory compliance issues and invite heavy penalty to the company, and at certain times, it may result in business shutdown. Hence, in this paper, we explored various security risks associated with the bots automation and provided a proposal to build a holistic security framework for the RPA environment.

The Internet of Things (IoT) connects every device possessing some element of computer technology or a digital interface. These devices constitute a global interconnected network that bridges the gap between the physical and virtual worlds. Today, there are two major applications for IoT—Consumer Internet of Things (CIoT), concerned with interactions between consumers and IoT devices, and industrial Internet of Things (IIoT), focussed on the utilisation of IoT for designing industrial systems. With the proliferation of IoT devices for myriad applications, it is becoming increasingly important to investigate and understand the factors essential to securing them against external threats. These factors directly influence the design, functionality and the standards and regulations for IoT devices. This paper defines the trust and resilience of IoT systems and provides unambiguous definitions for key factors (security, privacy, safety, recoverability, reliability and scalability) that directly influence the trust and resilience of IoT systems. Based on the results of a survey conducted amongst IoT consumers and experts, this paper ranks each of these factors in the order of their importance in determining the trust and resilience of CIoT and IIoT systems. These rankings are generated using the analytic hierarchy process (AHP) and a pairwise analysis of the collected data.

MANET is an autonomous collection of mobile devices. They need the features of infrastructure-less network, flexibility, random mobility, and they do not require any base station or centralized device for the communication process. Rather than this, each device in MANET acts as a client and server. So it becomes a hot research topic among researchers. Communication between nodes is completed by intermediate nodes. Sometimes the intermediate nodes act as malicious nodes by implementing any abnormal function. So we would like to guard the traditional nodes. Therefore, we examine some routing attacks, and how they drastically affect the MANET communication process.

Steganography is the oldest technique that is been used from century, steganography purpose has not changed, i.e., all these techniques aim at hiding data or protecting data. With the help of steganalysis, the media can be analyzed to check for the presence of any secret information. Nowadays, attackers are making the use of advanced steganography approaches to conceal the secret information and communicate in a stealth manner. In this paper, the authors have discussed about the novel approach to hide malicious payload into image metadata. Therefore, metadata is a data that describes about the image rights and its administration. Hacker generally uses this metadata to perform various malicious attacks such embedding malicious script inside the image metadata and many more.

Phishing has become one of the most common activities observed over the Internet quite often. To investigate the methods through which phishing can not only be detected but can also be controlled, a lot of researchers have contributed and have opened gates for the industry. This paper illustrates the types of phishing attacks and ways to optimize the anti-phishing architecture. The highlights of this paper are listing down the ways to detect phishing activities over web services. The analyzed techniques are compared on the basis of suitable comparative parameters listed in reputed articles.

Providing network security with an open source has been a huge concern today. Data transmitted over the network shall not be assumed to be stable. There are numerous attacks such as phishing, spoofing, and sniffing. This paper provides a summary of the tools available to fight against these network attacks and threats. There are several resources in the open source that are built to use to handle these threats. Resources including OpenSSH, OpenSSL, NMap, digital certificate, and IP tables were discussed in this article.

Nowadays the most important issue we face during data transmission and exchanging of data is security of data. The cryptographic algorithms play very significant role to secure data. By using different algorithms, it improves security of data by making data unreadable, only the authenticate users can read data after decryption using keys. All algorithms perform same work, but consume different volume of computing properties such as time of CPU, memory utilization, throughput time, encryption and decryption time, simulation etc. Also different algorithms use various size of keys, size of block and cipher type. So we need to compare the cryptographic algorithms to choose the best one. In this paper, we have done the evaluation of both block cipher (AES, DES, 3DES, Blowfish) as well as stream cipher (SALSA20) cryptographic algorithms is shown by taking different size of audio files. This comparison of different algorithms has been conducted to evaluate parameters such as encryption/decryption time, throughput time, memory utilization, scalability and ratio. Simulation results are given to demonstrate the efficiency of each. This research contributes to identify state of the art cryptographic technique.

The evolution and era of the latest programs and services, collectively with the enlargement of encrypted communications, make it difficult for site visitors within a safety enterprise. Virtual private networks (VPNs) are an instance of encrypted communique provider that is becoming famous, as a way for bypassing censorship in addition to gaining access to offerings which are geographically locked. This paper reviews the layout of an IP security, VPN. The Cisco Packet lines platform is used for the simulation, evaluation and verification. It uses a virtual connection to carry the records packets from a non-public network to remote places.

Artificial intelligence techniques have been intensively used for android malware detection and analysis in the last past few years. The proposed methodologies do not suffice the requirement while characteristics of malwares are changing so rapidly and evolving new complex malwares. Therefore, it is a very complex task to classify and identify these malwares. This paper presents an organized and comprehensive survey for the detection techniques of android malware(s) in chronological order. These detection and analysis techniques are elaborated in two core categories: statics and dynamic analysis and hybrid analysis with machine learning or artificial intelligence. The core contributions of this paper are: (1) explaining a methodical, chronicle and organized summary of the existing techniques of android malware detection, (2) exploring the major elements and challenges in the detection methods and (3) explaining the importance of artificial intelligence for android malware detection. The detection approaches are explained in a manner that new approaches are compared with the old ones based on their features. The advantages and disadvantages of each approach are discussed. This study facilitates researchers and academics to have a wide-ranging conception of the field of android malware detection and provides a platform to enhance the fundamental knowledge to implement the new idea and subsequent improvement further in existing techniques.

Currently, the blockchain technology is a boom in creating a secure and distributed environment for many real-life applications. Here, the focus is to introduce a car hiring system so that the car owners will get the fare without any significant loss and consumers can hire the car with variety of preferences. The proposed system is designed with blockchain technology; anyone can join the system by entering user’s information and is added to the specified block, computing hash for that required block by applying proof of work and storing hash of previous block in it. Thus, the system works on the complexity of blockchain with proof of work. The proposed model is implemented in Python and aims at removal of organizational role from car hiring system using blockchain technology.

An IoT software product’s reliability is the probability of the product working “correctly” under or over a given time. New opportunities are the result of expansion in the fast-paced Internet of Things (IoT) space. IoT technologies on the collected datasets improve disease progression technology, disease prediction, patient self-management and clinical intervention. To propose, the IoT with cipher block chaining in the traditional cryptographic operation mode will be used for cryptographic processing. Developing models for the supervised learning classification and security of imbalanced datasets is challenging, especially in the medical field. However, most real-time IoT datasets present most traditional machine learning algorithms challenging unbalanced datasets. Proposed a new framework for the Correlation Blockchain Matrix Factorization Classifier (CBMFC) related to comprehensive medical records. CBMFC uses a multiple class label machine learning that represents an independent population model based on disease meta functions such as profile age, group, or cognitive function keys. The Pairwise Coupling Multi-Class Classifier (PCMC) is used to prove the model’s correctness. This produces more comprehensive data in various machine learning environments, such as predictive classification, similar to real data performance. For the results of security analysis confirmation, the proposed IoT application model’s effectiveness can withstand various attacks, such as selected cryptographic attacks. In this proposed CBMFC system, classification accuracy, precision, recall, execution time and security matrix are used to evaluate performance.

Blockchain is emerging as a functional technology for remodeling current technologies and also for creating new applications which in practical was not possible earlier. In this paper, we are building a self-sovereign identity management system using blockchain (BSelSovID). The application will serve as an interface to all the entities involved, viz. user, authority, and verifier. The use of blockchain is to dodge the centralized identity manager. The user gets digital identity after getting authority’s verifiable claim and the identity is stored on interplanetary file system (IPFS) and the content address of these is stored on the blockchain. This makes it easy for the user to interact with different services without having identity for each one of them. The security of user data is ensured by encrypting the data and giving user the complete control over his data stored in the IPFS. The blockchain cannot extract the user’s data but can only request the user to provide some data attribute or some verification as the verifier demands. Thus, the system provides security and privacy to users’ data as well as user has control over his data.

Blockchain and Internet of Things (IoT) are important constituents of Internet-enabled era of information technology. Both technologies are distributed, autonomous, and decentralized systems. IoT devices require the strengthening of its security features, and security is an intrinsic aspect of blockchain due to cryptographic mechanisms. On the other hand, blockchain needs contribution from the distributed nodes and IoT includes within its architecture. So, blockchain can aid in the settlement of major security requirements in IoT. Blockchain features such as decentralization, immutability and transparency (DIT), auditability, and data encryption help to solve various IoT architectural problems. The main goal of water supply sector is to provide a solution to get shielded, authentic, and cost-effective water supply through well-regulated arrangements. It is very hard to achieve these goals. This paper introduces an algorithm for implementing a smart water management system that identifies and quantifies the water requirement by an individual consumer within a given locus and also identifies leaks (if any) in the plumbing system. The system proposed monitors both water quality and water scarcity aspects within the supplied vicinity. The smart water management system is collateral to a decentralized system implemented using smart tanks that uses the Internet of Things (IoT) for implementation and blockchain technology for providing a more rooted mechanism.

In today’s world, cryptocurrency has seen a boom in users’ number, and the numbers are increasing day by day. There are multiple cryptocurrencies, so there must be a platform to provide an exchange of cryptocurrencies. These days, many platforms provide users with the service, but they lack speed and are limited to some cryptocurrencies. Thus, we have proposed and developed a system that will increase the transactions rate by performing it off-chain to increase the transactions’ speed and perform exchange between any cryptocurrencies. The proposed system combines the best practices of both the decentralised and centralised exchange platforms.

Non-fungible tokens are one of the most important future application domains for smart contracts. Ethereum is the pioneer of a blockchain-based decentralized computing platform that has ultimately standardized these types of tokens into a well-defined interface, now known as ERC-721. Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permissionless blockchains. This paper aims to analyze blockchain and cryptocurrencies’ technical underpinnings, specifically non-fungible tokens or “crypto-collectibles,” with the help of a blockchain-based image matching game. While outlining the theoretical implications and use cases of NFTs, this paper also gives a glimpse into their possible use in the domain of human user verification to prevent misuse of public data by automated scripts. This demonstrates the interaction of the ERC-721 token with the Ethereum-based decentralized application. Further, we aim to reach a definitive conclusion on the benefits and challenges of NFTs and thus reach a solution that would be beneficial to both researchers and practitioners.

Blockchain is taken to be a system of ledger which handles data and manages their transactions with the help of their time stamps through cryptographic hashing and serves in a decentralized technique over the computer networks. Despite the fact that blockchain concept is used on a large scale for cryptocurrency, this paper explores some potential applications of the blockchain concept in the healthcare industry as well. It discusses the use of blockchain technology in the healthcare sector and how it can help in overcoming the present challenges like interoperability, security, and cost of maintenance in the traditional healthcare system. This paper highlights the applications of blockchain in detail and why it is hard to implement in the healthcare sector.

Wireless sensor network (WSN) is a promising technology for monitoring the physical world. The energy limitation of WSNs makes it an energy saver technology. A number of diverse routing protocols can be used to enhance the network lifetime of WSNs. There exist two types of clustering-based approaches, namely homogeneous and heterogeneous. In the former case, all nodes have the same technical characteristics (bandwidth, processor, initial energy, etc.). In contrast, in the latter case, nodes have different technical characteristics, i.e., some of the nodes have higher ability than others in terms of the parameters listed above. In this paper, we analyze the ADV-LEACH1 algorithm for the homogenous WSNs. The mathematical modeling and simulation results achieved by MATLAB-2017b show a comparative analysis of heterogeneous and homogeneous WSNs in terms of the energy consumption, alive/dead nodes, and network lifetime. The ADV-LEACH1 with heterogeneous network performs better than that with the homogenous network because of advanced node presence in the network with higher energy level.

Life being the most valuable assets is lost myriads of time due to dreadful vehicular accidents. However, a real-time drowsiness detector used on a car can significantly obviate these accidents and save valuable lives worldwide. The main reason being inattention of driver mainly referred to as driver’s drowsiness. The driver drowsiness monitoring system works with a high frequency detection system using an incoming video stream of a driver focusing on the natural visual changes in driver such as the steady closure of eyes and slow changing facial expression using artificial intelligence and scientific drowsiness detection measure. The proposed work focuses on the various monitoring systems used in the drowsiness detection as well as the process of the detection system. The driver drowsiness detector attached to crucial predictive analysis system is proposed to be utilized. This research paper focuses on the analysis of various drowsiness system so that better predictive analysis could be done. Machine learning is a very important paradigm for predictive analysis, so this paper focuses on the various machine learning techniques and their efficiency for detection of drowsiness in various systems.

Twitter is a popular platform for people to express their feelings on any subject or topic irrespective of place and time in the world. The view expressed by the Twitter community gives enormous information about them and the trend going on. To identify these trends and patterns, many data science techniques are used. However, using appropriate clustering technique always remains a challenge for researchers. The research concentrates on using hard clustering approaches like K-means and soft clustering approaches like Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI). The proposed methodology uses K-means on numerical attributes, LDA, and LSI on textual attributes. The experiments are done using different Twitter datasets and tested on Sabarimala temple tweet dataset. The first time experimental study shows the promising correlation of the K-means cluster sentiments with topic sentiments. This helps in understanding the stance of the topics formed. The paper concludes by highlighting the relevance between the results of K-means clusters with the topics formed using LDA and LSI techniques.

Wireless communication application is not possible without using the antenna. Nowadays, microstrip patch antenna (MPA) is using broadly due to its major advantages than others. Most of the electronics device is using MPA. There are various shapes, and pattern is proposed by antenna researchers. Microstrip patch antenna provides better performance in wireless communication applications. This paper proposed a novel design of microstrip patch antenna array for 5G Wi-Fi, wi-max applications. The CST microwave studio software is used to make a proposed antenna design and simulation. The resonant frequency of this antenna is 6.9 GHz. Overall bandwidth achieved by proposed antenna is 903 GHz. The large bandwidth is required for 5G communication applications.

The vehicle tracking system is the fundamental part in our everyday life. Yet, the vehicle tracking system regularly utilized GSM/GPS procedure. Presently what the issue with is, we should need a web association with doing this kind of vehicle following, but a portion of the cases and in no organization regions this sort of vehicle following won’t work. Along these lines, in this paper, we investigate the demonstration of GPS/GSM tracking system and the chance of LoRa-based vehicle tracking system. Results uncovered that all measurements measured with in re-enacted experiments were comparable to the results of a genuine examination; notwithstanding, we thought that by looking for a more realistic relationship with real inquiries, the model could be strengthened.

Excessive enrichment of nutrients can cause any water body to undergo eutrophication, thus damaging the water body and sometimes making it completely unusable and futile. Further, anthropogenic activities lead to cultural/accelerated eutrophication that becomes native to several species of algae including cyanobacteria and cyanotoxins causing algal blooms, organic pollution caused due to organic and inorganic waste, etc. Monitoring of the river and lakes for such phenomena using satellite images has become increasingly popular to understand the trends and patterns in order to further protect the environment. The Mithi River in Mumbai that travels 17.84 km from the Vihar Lake to the Mahim Bay has been studied in this paper using multispectral satellite images and remote sensing to understand the different processes that take place due to human and biological factors which degrade the river. Chlorophyll-a (vegetation index), chemical oxygen demand (COD) and biological oxygen demand (BOD) are the parameters that we have taken into consideration to measure the water quality index at eleven different predefined points (Table 1). Sentinel 2A multispectral images have been used to study the parameters along the stretch of the river by applying cloud masks to eliminate the cloud and the cloud shadow effects on the image collection.

Detection of human presence can be used for intelligently switching on and off devices. This will save a lot of electricity as well as help in building an intuitive experience for smart homeowners. In the future, technologies like artificial intelligence may also benefit from this information by taking smart decisions and providing more contextual responses. One can find many research proposals and commercial products for efficiently counting humans. However, these systems are very costly, extremely difficult to install, and many a time obstructs people. It is the reason why they have still not made it to residential homes yet. In this paper, we have discussed some of the most cutting-edge technologies that can be used for sensing humans. We analyze them based on six factors and determine how economic and easy to deploy they are. Some of the solutions deliver promising results and have the potential of becoming a mass-market product.

In recent years, source of revenue for companies across all industries is subscription products. Hence, it is crucial to know the user area of interest so that customer churn from the company is minimized. Machine learning is gaining scope day by day. Its application has also found a way to predict and analyse the churn rate. The model is build using logistic regression and support vector machine (SVM) algorithms. Objective of the model is to predict which user is likely to cancel product subscription. Accuracy obtained in the model using logistic regression was found to be 61.2 and using SVM was found to be 60.7%.

Global warming and the corresponding climatic changes have affected the world adversely. Climatic changes encompass the soaring temperatures, extremity in weather phenomenon, disrupting habitats, rising water levels in seas, and plenty of other impacts. As these changes emerge, the humans attempt to reduce the carbon emissions. This research paper aims to study the changing temperatures as a result of the industrial activities and greenhouse effect over the last 5 decades. The analysis utilizes data analytic tools to analyze the percentage at which the decades are affected as we moved into technologically advanced era. After studying the effects, the research paper also aims to predict the changes in the mean temperatures for the next decade using the time series prediction model with the help of machine learning algorithms. The dataset includes monthly temperatures for about 150 countries for a period of 58 Years, and machine learning algorithms aim to predict the rise and fall of temperatures for the next decade successfully.

In recent years, a great deal of attention has been given to deep learning in the field of network and information security. Any intrusion and anomaly in the network can significantly impact many areas, such as security of the private and social data, national security, social and financial concerns, etc. Therefore, network and information security are a broad research domain for which researchers are actively utilizing the functionally improved, emerging deep learning technique and report the improved result. In this review paper, we have analysed several deep learning methods in the area of network anomaly, intrusion detection, network traffic analysis and its classification. We have presented a comprehensive review of widely known deep learning approaches. And then, we conclude with open research challenges and unresolved issue for further study. This review paper provides an overall background for the researchers interested in network anomaly and intrusion detection based on deep learning methods.

Deforestation, which has contributed to adverse effects on the natural environment, is one of the challenges to reducing biodiversity and global climate change. Thus, early detection of deforestation is of utmost importance. Inspired by the above situation, this work provides an examination of the automated deforestation detection method. Change detection is used to figure out whether or not the changes occurred using remote sensing images at two different times. This work proposes an idea of effective method for detecting relevant changes in the equivalent scene between two temporally different images. This research analyzes image data from a remote sensing satellite called Landsat-8 in order to track changes in forest cover over a period of time. The findings of such a study will lead to taking steps to conserve the environment.

A high-speed field-programmable gate array (FPGA) implementation architecture is purposed to implement fast learning with error (LWE) fully homomorphic encryption. Currently, there are many security issues with conventional cryptosystems. In addition, encrypting and decrypting a large volume of data consume enormous computing time which makes the conventional cryptosystems ineffective. In this work, a novel fully homomorphic encryption algorithm, LWE, has been analyzed using linear algebraic equations. The same has been simulated in Python. In addition, to map the LWE scheme in the FPGA, digital circuits are conceptualized to implement mathematical operations such as modulo adder, multiplier, and noise generator.

Communication by using an energy-saving approach is a vital requirement of an energy-constrained underwater sensor network. The underwater acoustic communication technique ordinarily governs the usage of clout. The network’s capability to communicate the collected facts is affected due to minimal renewing capacity in case of the underwater wireless sensor network. The proposed work describes the reliable resources available in underwater for the sensors. The proposed scheme provides the mechanism for energy harvesting from tidal energy into electrical energy to charge the Li-ion cells used in sensors. To cover the maximum area of the ocean sensor, the data aggregators are deployed at three different levels (i.e. bottom, middle and top levels). The sensors deployed in oceans at the top level are static, whereas they are mobile at middle and bottom levels.

Wireless sensor network (WSN) finds vast real-world applications in the field of energy control, security, health care, defense, and environment monitoring. WSNs are subdued by limited power with a specific battery backup. Due to the large distance between sensor nodes and sink, more consumption of power takes place in the sensors. Limited energy of sensor nodes is a major drawback to empower a large network coverage area. Therefore, the battery life and location of cluster heads play an important role in increasing the efficiency and lifetime of sensor nodes for long-term operation in WSNs. While there are many algorithms leading to the optimization of performance using convergence, comparison of such algorithms and their advantages and challenges is addressed. Different types of attacks and security goals are described for high-level security and privacy in WSNs. This paper tabulates a systematic survey of the evolutionary algorithms of WSNs based on nature. This paper also intends to reflect on the security challenges of WSN and proposes effective techniques to address them.

In a cloud environment, the workload that has to be maintained using visualization is limited by the available hardware resources of virtual machines (VMs). So utilization of VMs becomes significant to do more work with lesser infrastructure. Thus, in recent times, major thrust was shown by researchers in the field of task allocation algorithms on VMs. There are many techniques discussed in the literature, which uses different allocation methods, which can improve the performance by changing the working of cloud environment. In this research work, analysis, implementation and performance comparison of the existing allocation techniques have been performed using CloudSim. So performance tuning is being done analytically and practically for the task allocation algorithm. VMs and cloudlets are configured for experimental purposes and parameter results are obtained. Parameters recorded are execution time, makespan, utilization ratio and power consumption. A new algorithm is proposed for task allocation algorithm (Tiwari et al in Int J Adv Intell Syst Comput, 2016 (Tiwari and Kumar in Telecommun. Syst. 62:149–165, 2016)). These parameters are calculated for FCFS, SJF, Hungarian and the proposed algorithm. Then, result analysis is done and majorly got a speedup in utilization ratio of the proposed algorithm w.r.t. to FCFS as 53.20%, 18.08% w.r.t. to SJF and 10.52% w.r.t. to Hungarian. For power consumption, the algorithm has shown a significant decrease in power consumption from 37.21, 16.52 and 10.52% w.r.t. to FCFS, SJF and Hungarian algorithms.