Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

GIS equipment is an important component of power system, and mechanical failure often occurs in the process of equipment operation. In order to realize GIS equipment mechanical fault intelligent detection, this paper presents a mechanical fault diagnosis model for GIS equipment based on cross-validation parameter optimization support vector machine (CV-SVM). Firstly, vibration experiment of isolating switch was carried out based on true 110 kV GIS vibration simulation experiment platform. Vibration signals were sampled under three conditions: normal, plum finger angle change fault, plum finger abrasion fault. Then, the c and G parameters of SVM are optimized by cross validation method and grid search method. A CV-SVM model for mechanical fault diagnosis was established. Finally, training and verification are carried out by using the training set and test set models in different states. The results show that the optimization of cross-validation parameters can effectively improve the accuracy of SVM classification model. It can realize the accurate identification of GIS equipment mechanical fault. This method has higher diagnostic efficiency and performance stability than traditional machine learning. This study can provide reference for on-line monitoring and intelligent fault diagnosis analysis of GIS equipment mechanical vibration.

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

A persistent and serious danger to the Internet is a denial of service attack on a large scale (DDoS) attack using machine learning. Because they originate at the low layers, new Infections that use genuine hypertext transfer protocol requests to overload target resources are more untraceable than application layer-based cyberattacks. Using network flow traces to construct an access matrix, this research presents a method for detecting distributed denial of service attack machine learning assaults. Independent component analysis decreases the number of attributes utilized in detection because it is multidimensional. Independent component analysis can be used to translate features into high dimensions and then locate feature subsets. Furthermore, during the training and testing phase of the updated source support vector machine for classification, their performance it is possible to keep track of the detection rate and false alarms. Modified source support vector machine is popular for pattern classification because it produces good results when compared to other approaches, and it outperforms other methods in testing even when given less information about the dataset. To increase classification rate, modified source support Vector machine is used, which is optimized using BAT and the modified Cuckoo Search method. When compared to standard classifiers, the acquired findings indicate better performance.

Speech emotion popularity is one of the quite promising and thrilling issues in the area of human computer interaction. It has been studied and analysed over several decades. It’s miles the technique of classifying or identifying emotions embedded inside the speech signal.Current challenges related to the speech emotion recognition when a single estimator is used is difficult to build and train using HMM and neural networks,Low detection accuracy,High computational power and time.In this work we executed emotion category on corpora — the berlin emodb, and the ryerson audio-visible database of emotional speech and track (Ravdess). A mixture of spectral capabilities was extracted from them which changed into further processed and reduced to the specified function set. When compared to single estimators, ensemble learning has been shown to provide superior overall performance. We endorse a bagged ensemble model which consist of support vector machines with a gaussian kernel as a possible set of rules for the hassle handy. Inside the paper, ensemble studying algorithms constitute a dominant and state-of-the-art approach for acquiring maximum overall performance.

The major aim of the study is to predict the type of crime that is going to happen based on the crime hotspot detected for the given crime data with engineered spatial features. crime dataset is filtered to have the following 2 crime categories: crime against society, crime against person. Crime hotspots are detected by using the Novel Hierarchical density based Spatial Clustering of Application with Noise (HDBSCAN) Algorithm with the number of clusters optimized using silhouette score. The sample data consists of 501 crime incidents. Future types of crime for the given location are predicted by using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms (N=5). The accuracy of crime prediction using Support Vector Machine classification algorithm is 94.01% and Convolutional Neural Network algorithm is 79.98% with the significance p-value of 0.033. The Support Vector Machine algorithm is significantly better in accuracy for prediction of type of crime than Convolutional Neural Network (CNN).

Being a part of today’s technical world, we are connected through a vast network. More we are addicted to these modernization techniques we need security. There must be reliability in a network security system so that it is capable of doing perfect monitoring of the whole network of an organization so that any unauthorized users or intruders wouldn’t be able to halt our security breaches. Firewalls are there for securing our internal network from unauthorized outsiders but still some time possibility of attacks is there as according to a survey 60% of attacks were internal to the network. So, the internal system needs the same higher level of security just like external. So, understanding the value of security measures with accuracy, efficiency, and speed we got to focus on implementing and comparing an improved intrusion detection system. A comprehensive literature review has been done and found that some feature selection techniques with standard scaling combined with Machine Learning Techniques can give better results over normal existing ML Techniques. In this survey paper with the help of the Uni-variate Feature selection method, the selection of 14 essential features out of 41 is performed which are used in comparative analysis. We implemented and compared both binary class classification and multi-class classification-based Intrusion Detection Systems (IDS) for two Supervised Machine Learning Techniques Support Vector Machine and Classification and Regression Techniques.

This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases.

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS.

Critical infrastructure is a key area in cybersecurity. In the U.S., it was front and center in 1997 with the report from the President’s Commission on Critical Infrastructure Protection (PCCIP), and now affects countries worldwide. Critical Infrastructure Protection must address all types of cybersecurity threats - insider threat, ransomware, supply chain risk management issues, and so on. Unsurprisingly, in the past 25 years, the risks and incidents have increased rather than decreased and appear in the news daily. As an important component of critical infrastructure protection, secure supply chain risk management must be integrated into development projects. Both areas have important implications for security requirements engineering.

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products.

Modern software development frequently uses third-party packages, raising the concern of supply chain security attacks. Many attackers target popular package managers, like npm, and their users with supply chain attacks. In 2021 there was a 650% year-on-year growth in security attacks by exploiting Open Source Software's supply chain. Proactive approaches are needed to predict package vulnerability to high-risk supply chain attacks. The goal of this work is to help software developers and security specialists in measuring npm supply chain weak link signals to prevent future supply chain attacks by empirically studying npm package metadata. In this paper, we analyzed the metadata of 1.63 million JavaScript npm packages. We propose six signals of security weaknesses in a software supply chain, such as the presence of install scripts, maintainer accounts associated with an expired email domain, and inactive packages with inactive maintainers. One of our case studies identified 11 malicious packages from the install scripts signal. We also found 2,818 maintainer email addresses associated with expired domains, allowing an attacker to hijack 8,494 packages by taking over the npm accounts. We obtained feedback on our weak link signals through a survey responded to by 470 npm package developers. The majority of the developers supported three out of our six proposed weak link signals. The developers also indicated that they would want to be notified about weak links signals before using third-party packages. Additionally, we discussed eight new signals suggested by package developers.

Counterfeit drugs are an immense threat for the pharmaceutical industry worldwide due to limitations of supply chain. Our proposed solution can overcome many challenges as it will trace and track the drugs while in transit, give transparency along with robust security and will ensure legitimacy across the supply chain. It provides a reliable certification process as well. Fabric architecture is permissioned and private. Hyperledger is a preferred framework over Ethereum because it makes use of features like modular design, high efficiency, quality code and open-source which makes it more suitable for B2B applications with no requirement of cryptocurrency in Hyperledger Fabric. QR generation and scanning are provided as a functionality in the application instead of bar code for its easy accessibility to make it more secure and reliable. The objective of our solution is to provide substantial solutions to the supply chain stakeholders in record maintenance, drug transit monitoring and vendor side verification.

We have several issues in most current supply chain management systems. Consumers want to spend money on environmentally friendly products, but they are seldomly informed of the environmental contributions of the suppliers. Meanwhile, each supplier seeks to recover the costs for the environmental contributions to re-invest them into further contributions. Instead, in most current supply chains, the reward for each supplier is not clearly defined and fairly distributed. To address these issues, we propose a supply-chain contribution management platform for fair reward distribution called ‘Advanced Ledger.’ This platform records suppliers' environ-mental contribution trails, receives rewards from consumers in exchange for trail-backed fungible tokens, and fairly distributes the rewards to each supplier based on the contribution trails. In this paper, we overview the architecture of Advanced Ledger and 11 technical features, including decentralized autonomous organization (DAO) based contribution verification, contribution concealment, negative-valued tokens, fair reward distribution, atomic rewarding, and layer-2 rewarding. We then study the requirements and candidates of the smart contract platforms for implementing Advanced Ledger. Finally, we introduce a use case called ‘ESG token’ built on the Advanced Ledger architecture.

Ensuring sustainable sourcing of crude materials and production of goods is a pressing problem in consideration of the growing world population and rapid climate change. Supply-chain traceability systems based on distributed ledgers can help to enforce sustainability policies like production limits. We propose two mutually independent distributed-ledger-based protocols that enable public verifiability of policy compliance. They are designed for different supply-chain scenarios and use different privacy-enhancing technologies in order to protect confidential supply-chain data: secret sharing and homomorphic encryption. The protocols can be added to existing supply-chain traceability solutions with minor effort. They ensure confidentiality of transaction details and offer public verifiability of producers' compliance, enabling institutions and even end consumers to evaluate sustainability of supply chains. Through extensive theoretical and empirical evaluation, we show that both protocols perform verification for lifelike supply-chain scenarios in perfectly practical time.

Blockchain has emerged as a leading technological innovation because of its indisputable safety and services in a distributed setup. Applications of blockchain are rising covering varied fields such as financial transactions, supply chains, maintenance of land records, etc. Supply chain management is a potential area that can immensely benefit from blockchain technology (BCT) along with smart contracts, making supply chain operations more reliable, safer, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, and safety-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for scalability, cooperation, and coordination in distributed culture. MAS consists of a collection of automated agents who can perform a specific task intelligently in a distributed environment. In this work, an attempt has been made to develop a framework for implementing a multi-agent system for a large-scale product manufacturing supply chain with blockchain technology wherein the agents communicate with each other to monitor and organize supply chain operations. This framework eliminates many of the weaknesses of supply chain management systems. The overall goal is to enhance the performance of SCM in terms of transparency, traceability, trustworthiness, and resilience by using MAS and BCT.

Blockchain has the potential to enhance supply chain management systems by providing stronger assurance in transparency and traceability of traded commodities. However, blockchain does not overcome the inherent issues of data trust in IoT enabled supply chains. Recent proposals attempt to tackle these issues by incorporating generic trust and reputation management methods, which do not entirely address the complex challenges of supply chain operations and suffers from significant drawbacks. In this paper, we propose DeTRM, a decentralised trust and reputation management solution for supply chains, which considers complex supply chain operations, such as splitting or merging of product lots, to provide a coherent trust management solution. We resolve data trust by correlating empirical data from adjacent sensor nodes, using which the authenticity of data can be assessed. We design a consortium blockchain, where smart contracts play a significant role in quantifying trustworthiness as a numerical score from different perspectives. A proof-of-concept implementation in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively small overheads compared to the baseline.

Food supply chain is a complex but necessary food production arrangement needed by the global community to maintain sustainability and food security. For the past few years, entities being a part of the food processing system have usually taken food supply chain for granted, they forget that just one disturbance in the chain can lead to poisoning, scarcity, or increased prices. This continually affects the vulnerable among society, including impoverished individuals and small restaurants/grocers. The food supply chain has been expanded across the globe involving many more entities, making the supply chain longer and more problematic making the traditional logistics pattern unable to match the expectations of customers. Food supply chains involve many challenges like lack of traceability and communication, supply of fraudulent food products and failure in monitoring warehouses. Therefore there is a need for a system that ensures authentic information about the product, a reliable trading mechanism. In this paper, we have proposed a comprehensive solution to make the supply chain consumer centric by using Blockchain. Blockchain technology in the food industry applies in a mindful and holistic manner to verify and certify the quality of food products by presenting authentic information about the products from the initial stages. The problem formulation, simulation and performance analysis are also discussed in this research work.

The Open Web Application Security Project (OWASP) (a non-profit foundation that works to improve computer security) considered, in 2021, injection as one of the biggest risks in web applications. SQL injection despite being a vulnerability easily avoided has a great insurgency in web applications, and its impact is quite nefarious. To identify and exploit vulnerabilities in a system, algorithms based on Swarm Intelligence (SI) can be used. This article proposes and describes a new approach that uses SI and attack vectors to identify Structured Query Language (SQL) Injection vulnerabilities. The results obtained show the efficiency of the proposed approach.

The development of new types of technology actualizes the issues of ensuring their information security. The aim of the work is to increase the security of the collective decision-making process in swarm robotic systems from negative impacts by identifying malicious robots. It is proposed to use confidence in choosing an alternative when reaching a consensus as a criterion for identifying malicious robots - a malicious robot, having a special behavior strategy, does not fully take into account the signs of the external environment and information from other robots, which means that such a robot will change its mind with characteristic features for each malicious strategy, and its degree of confidence will be different from the usual voting robot. The modeling performed and the obtained experimental data on three types of malicious behavioral strategies demonstrate the possibility of using the degree of confidence to identify malicious robots. The advantages of the approach are taking into account a large number of alternatives and universality, which lies in the fact that the method is based on the mechanisms of collective decision-making, which proceed in the same way on various hardware platforms of swarm robotic systems. The proposed method can serve as a basis for the development of more complex security mechanisms in swarm robotic systems.

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets - UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration.

Advanced metamorphic malware and ransomware use techniques like obfuscation to alter their internal structure with every attack. Therefore, any signature extracted from such attack, and used to bolster endpoint defense, cannot avert subsequent attacks. Therefore, if even a single such malware intrudes even a single device of an IoT network, it will continue to infect the entire network. Scenarios where an entire network is targeted by a coordinated swarm of such malware is not beyond imagination. Therefore, the IoT era also requires Industry-4.0 grade AI-based solutions against such advanced attacks. But AI-based solutions need a large repository of data extracted from similar attacks to learn robust representations. Whereas, developing a metamorphic malware is a very complex task and requires extreme human ingenuity. Hence, there does not exist abundant metamorphic malware to train AI-based defensive solutions. Also, there is currently no system that could generate enough functionality preserving metamorphic variants of multiple malware to train AI-based defensive systems. Therefore, to this end, we design and develop a novel system, named X-Swarm. X-Swarm uses deep policy-based adversarial reinforcement learning to generate swarm of metamorphic instances of any malware by obfuscating them at the opcode level and ensuring that they could evade even capable, adversarial-attack immune endpoint defense systems.

Physical layer security is an emerging security area to tackle wireless security communications issues and complement conventional encryption-based techniques. Thus, we propose a novel scheme based on swarm intelligence optimization technique and a deep neural network (DNN) for maximizing the secrecy energy efficiency (SEE) in a cooperative relaying underlay cognitive radio- and non-orthogonal multiple access (NOMA) system with a non-linear energy harvesting user which is exposed to multiple eavesdroppers. Satisfactorily, simulation results show that the proposed particle swarm optimization (PSO)-DNN framework achieves close performance to that of the optimal solutions, with a meaningful reduction in computation complexity.

Swarm robotics is a network based multi-device system designed to achieve shared objectives in a synchronized way. This system is widely used in industries like farming, manufacturing, and defense applications. In recent implementations, swarm robotics is integrated with Blockchain based networks to enhance communication, security, and decentralized decision-making capabilities. As most of the current blockchain applications are based on complex consensus algorithms, every individual robot in the swarm network requires high computing power to run these complex algorithms. Thus, it is a challenging task to achieve consensus between the robots in the network. This paper will discuss the details of designing an effective consensus algorithm that meets the requirements of swarm robotics network.

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.