Input Message Research Articles

Design and Implementation of Innovative Statistically Secured Hash Algorithm using Decentralized Hash Function

Information technology is in high demand today due to the expanding global use of the internet for information sharing. Every user wants to secure communication over a public network and to confirm the accuracy of their data. Hash algorithms are used to translate the fixed-length hash code from the input message, which is used to verify the integrity of the input message or any information exchanged through the web. Numerous hash algorithms are proposed by researchers but many algorithms use high consumption time during hash function processing time and they are using centralized hash function processing. Therefore we are designing an Innovative hash algorithm to generate a 96-bit hash code using a decentralized hash function.

USING A DICTIONARY OF PARONYMS FOR PERSONAL NAMES COMPONENTS TO IDENTIFY PERSONAL DATA

The use of fuzzy search methods when identifying input messages inevitably leads to the appearance of quite a lot of information noise in the output data. It is proposed to solve the current problem of cutting off information noise in the output data by analyzing the results of a fuzzy search using a dictionary of paronyms for nominal components. In relation to nominal components, paronyms are those nominal components that, on the one hand, satisfy the criteria of similarity of an information retrieval system, but, in fact, differ in meaning (they relate to completely different individuals). For the first time, the problem of reducing information noise in the output message of information retrieval systems was solved using an information filter, the structure-forming element of which is a dictionary of paronyms of nominal components. When compiling a dictionary of paronyms, a mechanism was used to categorize paronyms of nominal components depending on the number of distorted characters. The proposed approach to reducing information entropy depending on the category and number of paronyms of nominal components in family-name groups can be applied not only for information search by family-name groups, but also in a broad sense – for text data frequently used in queries, which are subject to various distortions.

Lyapunov based stochastic stability of a quantum decision system for human–machine interaction

In psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and violation of the sure thing principle. We consider human–machine interaction involving a quantum decision maker (human) and a controller (machine). Given a sequence of human decisions over time, how can the controller dynamically provide input messages to adapt these decisions so as to converge to a specific decision? We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically. Our methodology yields a useful mathematical framework for human-sensor decision making. The stochastic Lyapunov results are also of independent interest as they generalize recent results in the literature.

Mouse Data Attack Technique Using Machine Learning in Image-Based User Authentication: Based on a Defense Technique Using the WM_INPUT Message

Recently, as the non-face-to-face society persists due to the coronavirus (COVID-19), the Internet usage rate continues to increase, and input devices, such as keyboards and mice, are mainly used to authenticate users in non-face-to-face environments. Due to the nature of the non-face-to-face environment, important personal data are processed, and since these personal data include authentication information, it is very important to protect them. As such, personal information, including authentication information, is entered mainly from the keyboard, and attackers use attack tools, such as keyloggers, to steal keyboard data in order to grab sensitive user information. Therefore, to prevent disclosure of sensitive keyboard input, various image-based user authentication technologies have emerged that allow sensitive information, such as authentication information, to be entered via mouse. To address mouse data stealing vulnerabilities via GetCursorPos() function or WM_INPUT message, which are representative mouse data attack techniques, a mouse data defense technique has emerged that prevents attackers from classifying real mouse data and fake mouse data by the defender generating fake mouse data. In this paper, we propose a mouse data attack technique using machine learning against a mouse data defense technique using the WM_INPUT message. The proposed technique uses machine learning models to classify fake mouse data and real mouse data in a scenario where the mouse data defense technique, utilizing the WM_INPUT message in image-based user authentication, is deployed. This approach is verified through experiments designed to assess its effectiveness in preventing the theft of real mouse data, which constitute the user’s authentication information. For verification purposes, a mouse data attack system was configured, and datasets for machine learning were established by collecting mouse data from the configured attack system. To enhance the performance of machine learning classification, evaluations were conducted based on data organized according to various machine learning models, datasets, features, and generation cycles. The results, highlighting the highest performance in terms of features and datasets were derived. If the mouse data attack technique proposed in this paper is used, attackers can potentially steal the user’s authentication information from various websites or services, including software, systems, and servers that rely on authentication information. It is anticipated that attackers may exploit the stolen authentication information for additional damages, such as voice phishing. In the future, we plan to conduct research on defense techniques aimed at securely protecting mouse data, even if the mouse data attack technique proposed in this paper is attempted.

Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions

Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce overly optimistic results, rendering current solution evaluations inadequate for real-world environments. This paper proposes a framework based on the simulation of real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The simulation module uses techniques and queueing theory to replicate a real-world message stream. The processing module refines the input messages for optimal classification. The classification module categorises the generated message stream using existing solutions. The evaluation module evaluates the performance of the classification solutions by measuring accuracy, precision and recall. The framework can model different behaviours from different sources, such as different spammers with different attack strategies, press media or social network sources. Each profile generates a message stream that is combined into the main stream for greater realism. A spam detection case study is developed that demonstrates the implementation of the proposed framework and identifies latency and message body obfuscation as critical classification quality parameters.

Methods of Correcting Errors in Messages Encoded by Fibonacci Matrices

The main problems of detection and available methods of correcting errors in encoded messages with Fibonacci matrices, which make it possible to find and correct one, two and three errors in the same or different lines of the code word, are analyzed. It has been found that even in the last decade, many scientists have published a significant number of various publications, each of which to one degree or another substantiates the expediency of using Fibonacci matrices for (de)coding data. It has been established that the elements of a codeword obtained by multiplying a message block by a Fibonacci matrix have many useful properties, which are the basis for the method for detecting and correcting errors in them. The statement is given, according to which the ratio of the corresponding elements of the code word is close to the golden ratio, which is important for the existing methods of correcting potential errors. This property of the elements makes it possible to identify the presence of double and triple false elements by checking whether their ratios belong to a fixed interval. It is found that the false affiliation indicates that there are two errors in different lines of the codeword, which require solving the corresponding Diophantine equations, the suitability of the solution of which must satisfy certain conditions for error correction. It was found that in order to correct two errors in one line of the code word, a condition was introduced according to which the set of blocks of the input message should contain only minimal matrices, which makes it possible to take the smallest solutions of the Diophantine equation, the suitability of which is specified by test ratios. It was found that in order to correct three errors in a codeword, it is necessary to check whether the relations of its corresponding elements belong to a fixed interval and to solve a nonlinear Diophantine equation, the implementation of which is extremely difficult. The proposed approach boils down to trial and error, according to which you first need to find the exact location of the erroneous elements, and only then correct them according to the appropriate methods.

Black Box Testing in Community Service Systems Using Boundary Value Analysis and Cause Effect Graph Methods (Case Study of Jombang District Community and Village Empowerment Service)

The community service information system at the Jombang Regency Community and Village Empowerment Service is an accurate and interactive information medium for the community and has been integrated with the village fund management system. So it is necessary to maintain the quality of the system so that it runs according to the expected functionality and runs optimally. Testing focuses on system functionality and performance using Black Box Testing with Bodary Value Analysis and Cause Effect Graph methods. Based on the test results, the use of Cause and Effect Graphs in public service information systems is more dominant for testing data entry features, and features in the form of buttons. Meanwhile, Boundary Value Analysis is more dominant in testing features that have a range such as name or title input, caption input, and complaint message input and obtained an effectiveness value of 77.86%, which is in the quite effective category and still needs to be done. improved and developed so that the system can be successful. achieve maximum effectiveness values ​​so that functionality runs more optimally.

Multimixer-128: Universal Keyed Hashing Based on Integer Multiplication

In this paper we introduce a new keyed hash function based on 32-bit integer multiplication that we call Multimixer-128. In our approach, we follow the key-then-hash parallel paradigm. So, we first add a variable length input message to a secret key and split the result into blocks. A fixed length public function based on integer multiplication is then applied on each block and their results are added to form the digest. We prove an upper bound of 2−127 for the universality of Multimixer-128 by means of the differential probability and image probability of the underlying public function.There are vector instructions for fast 32-bit integer multiplication on many CPUs and in such platforms, Multimixer-128 is very efficient. We compare our implementation of Multimixer-128 with NH hash function family that offers similar levels of security and with two fastest NIST LWC candidates. To the best of our knowledge, NH hash function is the fastest keyed hash function on software and Multimixer-128 outperforms NH while providing same levels of security.

Quantum hash function based on controlled alternate lively quantum walks

Quantum hash function is an important area of interest in the field of quantum cryptography. Quantum hash function based on controlled alternate quantum walk is a mainstream branch of quantum hash functions by virtue of high efficiency and flexibility. In recent development of this kind of schemes, evolution operators determined by an input message depend on not only coin operators, but also direction-determine transforms, which usually are hard to extend. Moreover, the existing works ignore the fact that improper choice of initial parameters may cause some periodic quantum walks, and further collisions. In this paper, we propose a new quantum hash function scheme based on controlled alternate lively quantum walks with variable hash size and provide the selection criteria for coin operators. Specifically, each bit of an input message determines the magnitude of an additional long-range hop for the lively quantum walks. Statistical analysis results show excellent performance in the aspect of collision resistance, message sensitivity, diffusion and confusion property, and uniform distribution property. Our study demonstrates that a fixed coin operator, along with different shift operators, can effectively work on the design of a quantum hash function based on controlled alternate quantum walks, and shed new light on this field of quantum cryptography.

Optimization-based pseudo random key generation for fast encryption scheme

Data encryption is being widely employed to secure data in order to provide confidentiality, authenticity and data privacy. In proposed method a novel method for (encryption, decryption) message through optimization using genetic algorithm (GA) is presented which aims to maintain the security of the message via increasing the complexity of the key used in the encryption. The proposed method is named stream cipher randomization using GA (SCRGA). It is characterized by its secrecy by hiding the statistical properties for the input message. The GA increases the key diffusion in order to eliminate the likelihood of using the statistical analysis and cryptanalysis techniques to obtain the original text. A key (k) will be supplied as input to pseudorandom bit generator and then it produces a random 8-bit output, The SCRGA evaluates the candidate keys and it would select the one that achieves the best value of the fitness function by maximization three criteria considered in matching the input message and the key. The test results were based on the comparison of the proposed method with two other state-of-the-art methods, that the SCRGA outperformed the other two methods in terms of the execution time (encryption, decryption) and encryption rounds key size.

Message flow analysis with complex causal links for distributed ROS 2 systems

Distributed robotic systems rely heavily on the publish–subscribe communication paradigm and middleware frameworks that support it, such as the Robot Operating System (ROS), to efficiently implement modular computation graphs. The ROS 2 executor, a high-level task scheduler which handles ROS 2 messages, is a performance bottleneck. We extend ros2_tracing, a framework with instrumentation and tools for real-time tracing of ROS 2, with the analysis and visualization of the flow of messages across distributed ROS 2 systems. Our method detects one-to-many and many-to-many causal links between input and output messages, including indirect causal links through simple user-level annotations. We validate our method on both synthetic and real robotic systems, and demonstrate its low runtime overhead. Moreover, the underlying intermediate execution representation database can be further leveraged to extract additional metrics and high-level results. This can provide valuable timing and scheduling information to further study and improve the ROS 2 executor as well as optimize any ROS 2 system. The source code is available at: github.com/christophebedard/ros2-message-flow-analysis.

AN EXPLORATORY STUDY OF HOW EMOTION TONE PRESENTED IN A MESSAGE INFLUENCES ARTIFICIAL INTELLIGENCE (AI) POWERED RECOMMENDATION SYSTEM

This innovative study aims to explore how emotion tone presented in a message influences the judgement of Artificial Intelligence (AI) bots. We established a scenario by using vacation rental introduction as input message to conduct experiments to explore the influences. Our findings suggest that AI bots demonstrate preferences on the input message presented in positive tones rather than input message with negative tones. Our pioneering study can serve as a crucial starting point for future studies, in particular opening up fresh avenues for future research endeavours and engenders discussions and debates concerning the development of recommendation system.

IoTInfer: Automated Blackbox Fuzz Testing of IoT Network Protocols Guided by Finite State Machine Inference

The popularity of Internet of Things (IoT) devices calls for effective yet efficient methods to assess the security and resilience of IoT devices. In this work, we explore a new heuristic based on finite state machine (FSM) inference to guide generation of test cases for blackbox fuzzing tests of IoT network protocol implementations. Our method, which is called IoTInfer, balances exploration and exploitation by continuously monitoring how likely mutation of an input message leads to counterexamples conflicting with the prediction by the current FSM. IoTInfer also applies clustering techniques to coarsen the FSM inferred when there are limited computational resources provisioned for fuzzing tests. We implement IoTInfer for both Bluetooth and Telnet protocols, which are widely used by existing IoT devices. Our experimental results with a variety of IoT devices reveal that IoTInfer is efficient at generating meaningful test cases, some of which can expose previously unknown vulnerabilities or implementation deviations from protocol specifications. We also compare IoTInfer with two other state-of-the-art blackbox IoT device fuzzing tools and find that IoTInfer is better at eliciting different types of responses from the fuzzing targets.

Implementation of the data encryption using caesar cipher and vernam cipher methods based on CrypTool2

Information has become precious and essential for all fields, so it is crucial to carry out information security. The principle of information security is to protect and safeguard information with the aim that the information is not entitled to be read, modified, or deleted by anyone who does not have rights to it. The purpose of our research is to analyze how the caesar cipher and vernam cipher methods are jointly used in the cryptographic process and are expected to produce a high level of data encryption so that it can increase the security of data or messages. The research applies the combination of the caesar cipher and vernam cipher methods to encrypt text data or messages. Using the secret key value will convert the input message into an encrypted message that is difficult to crack and cannot be decrypted again. The input text and the encrypted data have no resemblance to maintain the confidentiality of the information or data contents.

STEGANOGRAPHY CAPACITY ANALYSIS OF TEXT IN TEXT WITH SENTENCE STRUCTURE IN INDONESIAN

Data security issues become very important when the computer has been used as a communication tool on a global network (internet). One method that is quite popular for securing data from irresponsible parties is text-based steganography, where confidential data or information is hidden or inserted into other text media so as not to arouse suspicion from other parties. Based on the input data calculated on the number of words, it has a significant influence on the results of the stegotext. This can be seen from the comparison of the length of the input message which is calculated in the number of words with the length of the output message (stegoteks) which is influenced by the length of the style (pattern) of the sentence structure used. For input data with a word length of 1, the average capacity of the stegotext is 8 to 9 words or 12.4%, for input word length 2, the average capacity of the stegotext is 22 words or 8.85% and the input word length is 4 then the capacity of the stegotext is between 30 to 33 words or equivalent to 12.91%. Keywords :Text Steganography; Sentence Pattern; Payload Balancing Capacity

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

Quantum-dot cellular automata based design of multifunction binary right shifter circuit

This article explores the basic design methodology of a 4-bit binary right shifter circuit and its implementation based on a QCA nano-device. The right shifter circuit can shift a single bit of an input message. The proposed shifter has been reorganized to function as a logical, arithmetic, and circular right shifter. Single-layer wire intersection has been utilized to give the most straightforward execution of the plans. These designs are helpful in floating-point handling frameworks, incredibly exceptionally valuable for the mantissa multiplication procedure. The design correctness is confirmed by contrasting hypothetical qualities and relating simulation results. The shifter consumes 435 QCA cells, 0.89µm2 area with a latency of 1.75, and a circuit cost of 264.06. The design is compared with other shifters in terms of design complexities. The design has 0.628eV energy consumption at tunneling energy level 1.0Ek (kink energy). But it consumes only 0.190eV energy to perform multifunction shifting operations.

Emergency and Non-Emergency Vehicle Classification using Machine Learning

A highly populated country like India faces too much traffic jam. Sometimes emergency vehicles like ambulances, fire engine get stuck in the traffic causing threat to life in many cases. It is important to give priority to this vehicle and help to clear its path. But it is difficult or sometimes impossible for traffic police to handle this. So, differentiating a vehicle into an emergency and non-emergency category can be an important component intraffic monitoring as well as self-drive car systems can easy understand the emergency vehicle as reaching on time to their destination is critical for these services. In case of automatic vehicles, they need an alert input message like this to clear the path to emergency vehicles. on other hand, this is useful for all types of vehicle to get sudden alert to clear thepath to emergency vehicles. For this reason, we need an automated system that will be ableto detect an emergency vehicle in heavy traffic road, let the controller know or automatically navigate other vehicles to clear its path. In this work, we have proposed an automated system to detect emergency vehicle using the Machine Learning. KEYWORDS: Intelligent Transport System (ITS), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Decision Tree, Recurrent Neural Network (RNN), space invariant artificial neuralnetworks (SIANN)

A Cognitive Deception Model for Generating Fake Documents to Curb Data Exfiltration in Networks During Cyber-Attacks

The exponential increase in the compromise of sensitive and intellectual properties alludes to the huge price the global community must pay for the digital revolution we are currently experiencing. This irrefutable reality is a major reason why cybersecurity defences continue to be a pressing and timely area of research. Traditional countermeasures of cyber defence using boundary controllers and filters such as intrusion detection, access controls, firewalls and so on, have proven ineffective. Such measures fail to account for the attacker’s inherent advantage of being increasingly techno-savvy, as well as their persistence in attempting to compromise the security of not only high-value targets, but also the vast pool of oblivious users of technology. The use of decoys and deception is one of the emerging solutions for cyber defence. Leveraging decoys and deception for security pre-date the advent of the digital revolution as centuries have witnessed the military using human decoys to deceive and successfully defeat their adversaries during wars. However, its benefits for reducing cyberattacks in these digital times have not been thoroughly investigated. One of its use requires that fake text documents are positioned in the repository of critical documents in order to mislead and catch hackers attempting to exfiltrate sensitive documents. Current methods of generating fake text documents involve using symbols, junk documents, randomly generated texts. Such approaches fail to capture the empirical and linguistic properties of language, resulting in messages that do not scale well, are not realistic, fail in the context of syntax and are semantically void. Consequently, failing to convince the attackers to believe they are the original messages. This paper presents a Cognitive Deception Model (CDM) based on a neural model which takes an input message and generates syntactically cohesive and semantically coherent independent looking but plausible and convincing decoy messages to cognitively burden and deceive the adversaries. The experimental results used to validate the models, as well as the comparison with state-of-the-art tools, show that it outperforms existing systems.

A Novel Hybrid Encryption Method Based on Honey Encryption and Advanced DNA Encoding Scheme in Key Generation

Nowadays, increased information capacity and transmission processes make information security a difficult problem. As a result, most researchers employ encryption and decryption algorithms to enhance information security domains. As it progresses, new encryption methods are being used for information security. In this paper, a hybrid encryption algorithm that combines the honey encryption algorithm and an advanced DNA encoding scheme in key generation is presented. Deoxyribonucleic Acid (DNA) achieves maximal protection and powerful security with high capacity and low modification rate, it is currently being investigated as a potential carrier for information security. Honey Encryption (HE) is an important encryption method for security systems and can strongly prevent brute force attacks. However, the traditional honeyword encryption has a message space limitation problem in the message distribution process. Therefore, we use an improved honey encryption algorithm in our proposed system. By combining the benefits of the DNA-based encoding algorithm with the improved Honey encryption algorithm, a new hybrid method is created in the proposed system. In this paper, five different lookup tables are created in the DNA encoding scheme in key generation. The improved Honey encryption algorithm based on the DNA encoding scheme in key generation is discussed in detail. The passwords are generated as the keys by using the DNA methods based on five different lookup tables, and the disease names are the input messages that are encoded by using the honey encryption process. This hybrid method can reduce the storage overhead problem in the DNA method by applying the five different lookup tables and can reduce time complexity in the existing honey encryption process.