Existing Security Methods Research Articles

The Paradox of Power: Securing User Data in the Cloud's Shadow

Whilst the blessing of the cloud, which provides for adaptability and easy data sharing as well as storage, comes with some security doubts. We place a major responsibility on our backs of protecting the data provided by our users from any evil attempts to sneak in and also risks of being accidentally exposed and the ones that exist through shared infrastructure. This paper traverses this complex terrain by noting the need for targeted security solutions which are aimed towards dealing with the compounded nature of the fast-growing problem. By exploring such threats as data breaches, the possible encryption breaks and the nature of server sharing we will analyze the safety of cloud-based services. We position that the existing security methods, though very necessary, are not exclusive in responding to the ones posed by the new threats. This means we will therefore be rather flexible in our design of the data security in the cloud. By the way, we will employ innovative strategies, maybe, by replacing a word for words such as homomorphic encryption, zero-knowledge proofs, and federated learning, presenting how it holds promise for private and confidential assets. Furthermore, we investigate the exploding influence of blockchain technology, regarding the wages it might deserve in providing manipulated data authentication and creating trust. The work in this paper creates a path towards a future when database users' virtual information in the cloud is safe and certified. Through proposing a wide-ranged approach, which equates realized ideas with well-established security frameworks, we shall lead a cloud infrastructure where stability and power would prevail. Keywords – Cloud Computing, Security Issues, Security Challenges

An Artificial Intelligence-based Proactive Network Forensic Framework

is at an all-time high in the modern period, and the majority of the population uses the Internet for all types of communication. It is great to be able to improvise like this. As a result of this trend, hackers have become increasingly focused on attacking the system/network in numerous ways. When a hacker commits a digital crime, it is examined in a reactive manner, which aids in the identification of the perpetrators. However, in the modern period, it is not expected to wait for an attack to occur. The user anticipates being able to predict a cyberattack before it causes damage to the system. This can be accomplished with the assistance of the proactive forensic framework presented in this study. The proposed system combines a reactive and proactive framework. The proactive part will use machine learning-based classification algorithms to forecast the attack. Once the assault has been predicted, the reactive element of the proposed framework is used to investigate who is attempting to initiate the attack. The suggested system further emphasizes integrity and confidentiality by proposing an encryption method that encrypts the proactive module's report before decrypting it in the reactive module. The suggested elliptical curve cryptography-based security model was compared to several existing security methods in this paper.A comparison of multiple machine learning-based categorization algorithms is also performed in order to determine which is the most suitable for the proposed Network Forensic Framework. Accuracy, recall, precision, and F1 value are the performance metrics used to evaluate the various machine learning-based algorithms. According to the analysis, the suggested Network Forensic Framework is best implemented using the Extreme Gradient Boosting (XGB) technique.

Deepfake detection using deep learning methods: A systematic and comprehensive review

AbstractDeep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention.This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory

A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs; however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for DBN and LSTM accuracy reaching 99.75%, a sensitivity of 99.79%, and a recall of 99.79%.

Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model

Web applications’ popularity has raised attention in various service domains, which increased the concern about cyber-attacks. One of these most serious and frequent web application attacks is a Cross-site scripting attack (XSS). It causes grievous harm to victims. Existing security methods against XSS fail due to the evolving nature of XSS attacks. One evolving aspect of XSS attacks is feature drift which changes the feature relevancy and causes degradation in the performance. Unfortunately, dynamic awareness of drift occurrence is missing. Thus, this study attempts to fill the gap by proposing a feature drift-aware algorithm for detecting the evolved XSS attacks. The proposed approach is a dynamic feature selection based on a deep Q-network multi-agent feature selection (DQN-MAFS) framework. Each agent is associated with one feature and is responsible for selecting or deselecting its feature. DQN-MAFS provides a sub-model for reward distribution over agents, which is named as fair agent reward distribution based dynamic feature selection FARD-DFS. This framework is capable of supporting real-time, dynamic updates and adjustment of embedded knowledge as long as new labelled data arrives. DQN-MAFS has been evaluated using four real XSS attack datasets with various feature length sizes. The evaluation process was conducted and compared with state-of-the-art works. The obtained results show the superiority of our FARD-DFS over the benchmarks in terms of the majority of metrics. The improvement percentages of the mean accuracy and F1-measure ranged from 1.01% to 12.1% and from 0.55% to 6.88%, respectively, in comparison with the benchmarks. This approach can be deployed as an autonomous detection system without the need for any offline retraining process of the model to detect the evolved XSS attack.

Inspection of the Most Suitable Approach and Information Projection Method for Interactions in the Night Flight of a Projector-Mounted Drone

Security is considered heavy labor work owing to night shifts and long working hours. In recent years, the number of security guards has been increasing, and the labor shortage for security guards has become a problem in Japan. To overcome these problems, robots have been used for security purposes. However, most are unable to interact with users or guards at night. In this study, a drone called aerial ubiquitous display (AUD) is proposed to improve the problems of existing security methods using robots and to provide night security. The AUD enables human-drone interaction and night security. Moreover, when an AUD interacts with humans, the drone must come close to the human and project information on the ground. Therefore, this study investigated the optimal parameters for a projector-equipped drone to approach a human at night. In addition, by comparing these results with those of the day approach, we verified whether there would be a change in perception between daytime and nighttime. Furthermore, an experiment was conducted to investigate the types of projections that are most likely to capture a user’s attention.

Protecting Data Mobility in Cloud Networks Using Metadata Security

At present, health care applications, government services, and banking applications use big data with cloud storage to process and implement data. Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data. Sometimes, data may have highly sensitive information, leading users to consider using big data and cloud processing regardless of whether they are secured are not. Threats to sensitive data in cloud systems produce high risks, and existing security methods do not provide enough security to sensitive user data in cloud and big data environments. At present, several security solutions support cloud systems. Some of them include Hadoop Distributed File System (HDFS) baseline Kerberos security, socket layer-based HDFS security, and hybrid security systems, which have time complexity in providing security interactions. Thus, mobile data security algorithms are necessary in cloud environments to avoid time risks in providing security. In our study, we propose a data mobility and security (DMoS) algorithm to provide security of data mobility in cloud environments. By analyzing metadata, data are classified as secured and open data based on their importance. Secured data are sensitive user data, whereas open data are open to the public. On the basis of data classification, secured data are applied to the DMoS algorithm to achieve high security in HDFS. The proposed approach is compared with the time complexity of three existing algorithms, and results are evaluated.

Unknown Payload Anomaly Detection Based on Format and Field Semantics Inference in Cyber-Physical Infrastructure Systems

A cyber-physical infrastructure system (CPIS) is a system that controls and manages critical infrastructure such as smart manufacturing, water treatment facilities, power generation, and distribution facilities. Although these CPISs focus on the security of air-gapped network environments, strict isolation from the outside network is difficult to achieve, leading to various attacks. CPISs also comprise various devices and proprietary communication protocols that are used exclusively for each domain and site. Therefore, experts have to adopt a customized strategy to enhance security in CPIS networks after analyzing each domain, device, and protocol in advance. These methods require a significant amount of time, cost, and manpower; consequently, they are difficult to apply existing security methods in the real field. As a solution, a method is proposed herein that includes the following: 1) inferencing the CPIS protocol format and field semantics based on the characteristics of CPIS networks and protocols; 2) multilevel anomaly detection based on the meaning and values of each inferred field. The proposed method does not require knowledge of each site and protocol. In addition, the inference method can be used to analyze the payload field, including the state and measurement value, as well as the header field. Finally, we validate the proposed technique using an open-source CPIS network dataset including response injection, command injection, denial-of-service, and reconnaissance attacks. In addition, in the aspect of detection efficiency, the proposed technique exhibits comparable performance to that of existing knowledge-based anomaly detection methods.

Security for smart cities

Smart cities are evolving globally and many governments have invested large sums of monies to develop smart cities. This development is not a result of an overnight decision but rather, smart cities have evolved through a period of time, directly from earlier work on the digital city to ubiquitous city, green city, connected city, sustainable city, eco-city etc. The present age sees the arrival of very high-speed wireless 5G connectivity, fast GPU multi-core-based servers, big data, cloud computing, artificial intelligence, and data analytics. Many of these new technologies have supported the development and realisation of smart cities. In this study, the authors present an outline of security for smart cities and provide a deeper understanding of what we meant by securing smart cities. They discuss the applicability of existing security methods of authentication, access control, encryption, firewalls, and their appropriateness to defending a smart city. Specifically, we cover the security of data, internet, water supply, electricity supply, city brain, and other critical city services and present the possible malicious attacks on a smart city and consequences. Finally, they discuss security best practices for smart cities.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Risk Factors And Security Issues In Various Cloud Storage Operations

Presently a days, cloud computing is a rising and method for registering in software engineering. Cloud computing is an arrangement of assets and administrations that are offered by the system or web. Distributed computing broadens different figuring methods like framework registering, appropriated processing. Today distributed computing is utilized as a part of both mechanical, research and scholastic fields. Cloud encourages its clients by giving virtual assets through web. As the field of distributed computing is spreading the new procedures are producing for cloud security. This expansion in distributed computing condition likewise expands security challenges for cloud designers. Customers or Users of cloud spare their information in the cloud subsequently the absence of security in cloud can lose the client's trust. In this paper we will discuss about on cloud database and information mining security issues in different viewpoints like multi-occupancy, flexibility, unwavering quality, accessibility on different divisions like modern and research regions, and furthermore examine existing security methods and methodologies for a safe cloud condition through enormous information ideas. What's more, this paper additionally study different parts of mechanical, training and research areas. This paper will empower scientists and experts to think about various security dangers, models and apparatuses proposed in existing distributed storage.

МЕТОДИ ЗАБЕЗПЕЧЕННЯ БЕЗПЕКИ РОЗУМНОГО БУДИНКУ

This study focuses on the problem of Internet of Things (IoT) security, namely: an overview of threats to the information security of a smart home and the analysis of existing security methods for its devices. The Internet of Things is experiencing an exponential growth in research and industry. At the same time, security threats to information programs and databases are increasing. As it is known, the foundation of Internet of Things security consists of four parts: communication security, device security, device monitoring and network interaction control. The sources of threats and vulnerabilities of IoT information security are accordingly investigated. Conventional security and privacy approaches are generally not applicable to IoT, mainly because of its decentralized topology and limited resources of most of its devices. The analysis of scientific sources has allowed us to explore different approaches to secure a smart home: an architectural method with three modules to protect the confidentiality of smart home data analysis; network-level constraint method; Blockchain technology. It is determined that the proposed blockchain smart home platform enables the security of information, taking into account the basic security goals - confidentiality, integrity and accessibility. Blockchain can be used to track sensor data measurements and prevent duplication of any other malicious data, for authentication and secure data transmission. This research will help design and develop new methods and technologies for smart home security.

An Efficient Optimal Key Based Chaos Function for Medical Image Security

With the rapid development in the field of medical image encryption, researchers analyzed a number of encryption algorithms based on chaotic systems. But there is a difficulty lies, i.e., small key space and weak security in 1-D chaotic cryptosystems. To overcome this difficulty, an alternative security model was proposed in this paper. This paper investigated highly secure medical images with a couple of subkeys, initially where a couple of subkeys are given by utilizing chaotic logistic and tent maps. As per the chaotic (C-function) process, the security was investigated like diffusion as well as confusion. Based on the initial conditions, different random numbers were generated for each map from chaotic maps. Adaptive grasshopper optimization algorithm with PSNR and correlation coefficient fitness function was proposed to choose the optimal secret and public key of the system among the random numbers. The reason behind choosing adaptive process is to enhance high-security investigation of the current proposed model compared to the existing methods. At last, the proposed strategy results were compared with existing security methods and literary works, but found to be high performing.

Anonymous-address-resolution model

Address-resolution protocol (ARP) is an important protocol of data link layers that aims to obtain the corresponding relationship between Internet Protocol (IP) and Media Access Control (MAC) addresses. Traditional ARPs (address-resolution and neighbor-discovery protocols) do not consider the existence of malicious nodes, which reveals destination addresses in the resolution process. Thus, these traditional protocols allow malicious nodes to easily carry out attacks, such as man-in-the-middle attack and denial-of-service attack. To overcome these weaknesses, we propose an anonymous-address-resolution (AS-AR) protocol. AS-AR does not publicize the destination address in the address-resolution process and hides the IP and MAC addresses of the source node. The malicious node cannot obtain the addresses of the destination and the node which initiates the address resolution; thus, it cannot attack. Analyses and experiments show that AS-AR has a higher security level than existing security methods, such as secure-neighbor discovery.

A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review

Social media has been increasingly utilized as an area of sharing and gathering of information. Data mining is the process of analyzing data from different context and summarizes them into useful information. It allows the users to analyze the data, categorize them and identifies the relationship inferred in them. Text mining often referred to as text data mining can be used to derive information from text. Text analysis can be used in information retrieval, information extraction, pattern recognition, frequency distribution and data mining techniques. An application of this is to scan a set of documents in natural language for predictive classification purposes. Recent researches shows that the number of crimes are increasing through social media that may cause tremendous loss to organizations. Existing security methods are weak in cyber crime forensics and predictions. The contribution of this paper is to mine cybercriminal network which can reveal both implicit and explicit meanings among cybercriminal based on their conversation messages.

Genetic Algorithm and Probabilistic Neural Networks for Fingerprint Identification

Existing security methods rely on knowledge based on approaches like password or token based on approaches like access cards. Such method is not very secure, biometrics such as fingerprint, face and voice offer means of personal identification and provide increased security because they rely on who we are. In this paper, algorithm fingerprint identification is introduced. The proposed algorithm has used 196 fingerprint image back to the twenty-eight individual 140 from them has been used for training and 56 image has been used for testing .Discrete Cosine Transform has been used to extract distinctive features from fingerprint image and genetic algorithm has been used as feature selection technique .Genetic algorithm has helped to produce GA filter in order to select subset of features out of DCT. When testing the proposed system by using Probabilistic Neural Network has found the identification rate reaching to 91%. This rate has emboldened on attempted using more one filter of genetic algorithm , the result that reached to 98% as identification rate with more reduction in number features.

Usability Evaluation of Dialogue Designs for Voiceprint Authentication in Automated Telephone Banking

This paper describes an empirical investigation of the usability of different dialogue designs for voiceprint authentication in automated telephone banking. Three strategies for voice authentication were evaluated in an experiment with 120 telephone banking end-users: 1-Factor (voiceprint authentication based on customers' utterances of their account number and sort code); 1-Factor with Challenge (1-Factor plus a randomly generated digit string); and 2-Factor (1-Factor plus secret information known only to the caller). The research suggests the 2-Factor approach is the most effective strategy in this context: results from a Likert questionnaire show it to be highly usable and it is rated highest in terms of both security and overall quality. Participants welcome the option to use voiceprint technology but the majority would prefer it to augment rather than replace existing security methods.

Imbalanced Beamforming by a Multi-Antenna Source for Secure Utilization of an Untrusted Relay

We investigate a relay network where a multiantenna source can potentially utilize an unauthenticated (untrusted) relay to augment its direct transmission of a confidential message to the destination. Since the relay is untrusted, it is desirable to protect the confidential data from it while simultaneously making use of it to increase the reliability of the transmission. We present a low-complexity scheme denoted as imbalanced beamforming based on linear beamforming and constellation mapping that ensures perfect physical-layer security even while utilizing the untrusted relay. Furthermore, the security of the scheme holds even if the relay adopts the conventional decodeand- forward protocol, unlike prior work. Simulation results show that the proposed imbalanced signaling maintains a constant BER of 0.5 at the eavesdropper at any SNR and number of source antennas, while maintaining or improving the detection performance of the destination compared to not utilizing the relay or existing security methods.

An Effective Control Report Based Security Countermeasure against the Joint Attacks of False Report Injection Attack and Selective Forwarding Attack

Sensor networks are vulnerable to many attacks because the sensor networks operate in open environments. It is easy to incur one or more attacks such as a selective forwarding attack, a false report injection attack. It is hard to defend the sensor network from the multiple attacks through existing security methods. Thus, we suggest an energy-efficient security method in order to detect the multiple attacks. This paper presents a security method to detect the false report injection attack and the selective forwarding attack in the sensor network using a new message type. The message type is a filtering message. The filtering message prevents from generating and forwarding false alert messages. We evaluated performance of our proposed method through a simulation in comparison with an application of SEF (statistical enroute filtering scheme) and CHEMAS (Check point-based Multi-hop Acknowledgement Scheme). The simulation results represent that the proposed method is 10% more energy-efficient than the application when the number of false reports is great while retaining the detection performance.

Security in a Client/Server Environment

During the many years in which I have been involved in building client/server systems, I have often been asked if there is any real difference between the security of client/server systems and that of traditional, mainframe-based systems. The simple though seldom heard answer is yes—adoption of such a new architectural paradigm has a direct impact on security. It does not significantly affect existing security objectives; in most cases, security objectives remain constant. However, a new systems architecture does require that existing security methods and tools be either changed or replaced with new security techniques and tools. In short, what has to be done remains constant; how it is done changes depending on the new systems architecture. This column addresses a number of the unique security issues that are raised by adoption of client/server technologies.