Biblio

To verify the integrity and confidentiality of data communicated through the web is a very big issue worldwide because every person wants very fast computing and secure electronic data communication via the web. The authentication of electronic data is done by hashing algorithms. Presently researchers are using one-time padding to convert variable-length input messages into a block of fixed length and also using constant initial values that are constant for any input message. So this reason we are proposing the autonomous initial value proposed secure hash algorithm-256 (AIVPSHA256) and we are enhancing the performance of the hash function by designing and compuiting its experimental results in python 3.9.5 programming language.

Cyber Attack is the most challenging issue all over the world. Nowadays, Cyber-attacks are increasing on digital systems and organizations. Innovation and utilization of new digital technology, infrastructure, connectivity, and dependency on digital strategies are transforming day by day. The cyber threat scope has extended significantly. Currently, attackers are becoming more sophisticated, well-organized, and professional in generating malware programs in Python, C Programming, C++ Programming, Java, SQL, PHP, JavaScript, Ruby etc. Accurate attack modeling techniques provide cyber-attack planning, which can be applied quickly during a different ongoing cyber-attack. This paper aims to create a new cyber-attack model that will extend the existing model, which provides a better understanding of the network’s vulnerabilities.Moreover, It helps protect the company or private network infrastructure from future cyber-attacks. The final goal is to handle cyber-attacks efficacious manner using attack modeling techniques. Nowadays, many organizations, companies, authorities, industries, and individuals have faced cybercrime. To execute attacks using our model where honeypot, the firewall, DMZ and any other security are available in any environment.

Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS & a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user's consent.

Pauses in typing are generally considered to indicate cognitive processing and so are of interest in educational contexts. While much prior work has looked at typing behavior of Computer Science students, this paper presents results of a study specifically on the pausing behavior of students in Introductory Computer Programming. We investigate the frequency of pauses of different lengths, what last actions students take before pausing, and whether there is a correlation between pause length and performance in the course. We find evidence that frequency of pauses of all lengths is negatively correlated with performance, and that, while some keystrokes initiate pauses consistently across pause lengths, other keystrokes more commonly initiate short or long pauses. Clustering analysis discovers two groups of students, one that takes relatively fewer mid-to-long pauses and performs better on exams than the other.

The purpose of this article is to consider one of the options for automating the process of collecting information from open sources when conducting penetration testing in an organization's information security audit using the capabilities of the Python programming language. Possible primary vectors for collecting information about the organization, personnel, software, and hardware are shown. The basic principles of operation of the software product are presented in a visual form, which allows automated analysis of information from open sources about the object under study.

Risk management is an essential part of software security. Assemblyline is a software security tool developed by the Canadian Centre for Cyber Security (CCCS) for malware detection and analysis. In this paper, we examined the performance of Assemblyline for assessing the risk of executable files. We developed and examined use-cases where Assemblyline is included as part of a security safety net assessing vulnerabilities that would lead to risk. Finally, we considered Assemblyline’s utility in a continuous integration / delivery context using our test results.

The main goal of the system is to make it easier to standardize the style of program code written in C++. Based on the results of the review of existing static analyzers, in addition to the main requirements, requirements for the structure of stylistic rules were identified. Based on the results obtained, a system for static analysis of the C++ language has been developed, consisting of a set of modules. The system is implemented using the Python 3.7 programming language. HTML and CSS markup languages were used to generate html reports. To ensure that rules can be stored in the database, the MongoDB database management system and the pymongo driver module were used.

Internet Exchange Points (IXPs) are critical components of the Internet infrastructure that affect its performance, evolution, security and economy. In this work, we introduce a technique to improve the well-known TraIXroute tool with its ability to identify IXPs. TraIXroute is a tool written in python3. It always encounters problems during its installation by network administrators and researchers. This problem remains unchanged in the field of internet ixp measurement tools. Our paper aims to make a critical analysis of TraIXroute tool which has some malfunctions. Furthermore, our main objective is to implement an improved tool for detecting ixps on the traceroute path with ipv4 and ipv6. The tool will have options for Geolocation of ixps as well as ASs. Our tool is written in C\# (C sharp) and python which are object oriented programming languages.

Preventing the cyberattacks has been a concern for any organization. In this research, the authors propose a novel method to detect cyberattacks by monitoring and analyzing the network traffic. It was observed that the various log files that are created in the server does not contain all the relevant traces to detect a cyberattack. Hence, the HTTP traffic to the web server was analyzed to detect any potential cyberattacks. To validate the research, a web server was simulated using the Opensource Damn Vulnerable Web Application (DVWA) and the cyberattacks were simulated as per the OWASP standards. A python program was scripted that captured the network traffic to the DVWA server. This traffic was analyzed in real-time by reading the various HTTP parameters viz., URLs, Get / Post methods and the dependencies. The results were found to be encouraging as all the simulated attacks in real-time could be successfully detected. This work can be used as a template by various organizations to prevent any insider threat by monitoring the internal HTTP traffic.

The extensive utilization of network by smart devices, computers and servers makes it vulnerable to malicious activities where intruders and attackers tends to violate system security policies and authenticity to slither essential information. Honeypots are designed to create a virtual trap against hackers. The trap is to attract intruders and gather information about attackers and attack features. Honeypots mimics as a computer application, billing systems, webpages and client server-based applications to understand attackers behavior by gathering attack features and common foot prints used by hackers to forge information. In this papers, authors analyse amazon web services honeypot (AWSH) data to determine geo-spatial distribution of targeted attacks originated from different locations. The categorization of attacks is made on the basis of internet protocols and frequency of attack occurrences worldwide.

An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance.The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows:First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.

Digital signatures are increasingly used today. It replaces wet signature with the development of technology. Elliptic curve digital signature algorithm (ECDSA) is used in many applications thanks to its security and efficiency. However, some mathematical operations such as inversion operation in modulation slow down the speed of this algorithm. In this study, we propose a more efficient and secure ECDSA. In the proposed method, the inversion operation in modulation of signature generation and signature verification phases is removed. Thus, the efficiency and speed of the ECDSA have been increased without reducing its security. The proposed method is implemented in Python programming language using P-521 elliptic curve and SHA-512 algorithm.

Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://youtu.be/1hy\_ugf-iek. Documentation, tool, and source code can be found here: https://rishitha957.github.io/APIScanner.

On the current website, there are many undeniable conditions and there is the existence of new plot holes. If data link is normally extracted on each of the websites, it becomes difficult to evaluate each vulnerability, with tolls such as XS S, SQLI, and other such existing tools for vulnerability assessment. Integrated testing criteria for vulnerabilities are met. In addition, the response should be automated and systematic. The primary value of vulnerability Buffer will be made of predefined and self-formatted code written in python, and the software is automated to send reports to their respective users. The vulnerabilities are tried to be classified as accessible. OWASP is the main resource for developing and validating web security processes.

Cybersecurity education is a critical component of today's computer science and IT curriculum. To provide for a highly effective cybersecurity education, we propose using machine-learning techniques to identify common learning modalities of cybersecurity students in order to optimize how cybersecurity core topics, threats, tools and techniques are taught. We test various hypothesis, e.g. that students of selected VARK learning styles will outperform their peers. The results indicate that for the class assignments in our study preference of read/write and kinesthetic modalities yielded the best results. This further indicates that specific learning instruments can be tailored for students based on their individual VARK learning styles.

Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.

Cloud is the perfect way to hold our data every day. Yet the confidentiality of our data is a big concern in the handling of cloud data. Data integrity, authentication and confidentiality are basic security threats in the cloud. Cryptography techniques and Third Party Auditor (TPA) are very useful to impose the integrity and confidentiality of data. In this paper, a system is proposed Enhancing data protection that is housed in cloud computing. The suggested solution uses the RSA algorithm and the AES algorithm to encrypt user data. The hybridization of these two algorithms allows better data protection before it is stored in the cloud. Secure hash algorithm 512 is used to compute the Hash Message Authentication Code (HMAC). A stable audit program is also introduced for Third Party Auditor (TPA) use. The suggested algorithm is applied in python programming and tested in a simple sample format. It is checked that the proposed algorithm functions well to guarantee greater data protection.

This paper investigates the use of BeagleBone Black Wireless single-board Linux computers with Long Range (LoRa) transceivers to send and receive information in a mesh network while one of the transmitting/receiving nodes is acting as a relay in the system. An experiment is conducted to examine how long each LoRa node needed to learn the transmission intervals of any other transmitting nodes on the network and to synchronize with the other nodes prior to transmission. The spread factor, bandwidth, and coding rate are all varied for a total of 18 different combinations. A link to the Python code used on the BeagleBone Black is provided at the end of this paper. The best parameter combinations for each individual node and for the system as a whole is investigated. Additional experiments and applications of this technology are explored in the conclusions.

In the coming future, Software-defined networking (SDN) will become a technology more responsive, fully automated, and highly secure. SDN is a way to manage networks by separate the control plane from the forwarding plane, by using software to manage network functions through a centralized control point. A distributed denial-of-service (DDoS) attack is the most popular malicious attempt to disrupt normal traffic of a targeted server, service, or network. The problem of the paper is the DDoS attack inside the SDN environment and how could use SDN specifications through the advantage of Open vSwitch programmability feature to stop the attack. This paper presents DDoS attack detection and mitigation in the SDN data-plane by applying a written SDN application in python language, based on the malicious traffic abnormal behavior to reduce the interference with normal traffic. The evaluation results reveal detection and mitigation time between 100 to 150 sec. The work also sheds light on the programming relevance with the open daylight controller over an abstracted view of the network infrastructure.

The PyPI ecosystem has indexed millions of Python libraries to allow developers to automatically download and install dependencies of their projects based on the specified version constraints. Despite the convenience brought by automation, version constraints in Python projects can easily conflict, resulting in build failures. We refer to such conflicts as Dependency Conflict (DC) issues. Although DC issues are common in Python projects, developers lack tool support to gain a comprehensive knowledge for diagnosing the root causes of these issues. In this paper, we conducted an empirical study on 235 real-world DC issues. We studied the manifestation patterns and fixing strategies of these issues and found several key factors that can lead to DC issues and their regressions. Based on our findings, we designed and implemented Watchman, a technique to continuously monitor dependency conflicts for the PyPI ecosystem. In our evaluation, Watchman analyzed PyPI snapshots between 11 Jul 2019 and 16 Aug 2019, and found 117 potential DC issues. We reported these issues to the developers of the corresponding projects. So far, 63 issues have been confirmed, 38 of which have been quickly fixed by applying our suggested patches.

This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.

Partitioning security components of IoT devices to enable the use of Trusted Execution Environments adds resilience against side-channel attacks. Devices are hardened against extraction of sensitive information, but at the same time additional effort must be spent for the integration of the TEE and software partitioning. To perform partitioning, the developer typically inserts Remote Procedure Calls into the software. Existing RPC-based solutions require the developer to write Interface Definition Language files to generate RPC stubs. In this work, we present an RPC-based framework that supports software partitioning via a graphical user interface. The framework extracts required information about the interfaces from source-code header files to eliminate the need for IDL files. With this approach the TEE integration time is reduced and reuse of existing libraries is supported. We evaluate a Proof-of-Concept by partitioning a TLS library for IoT devices and compare our approach to other RPC-based solutions.

Software vulnerabilities often remain hidden until an attacker exploits the weak/insecure code. Therefore, testing the software from a vulnerability discovery perspective becomes challenging for developers if they do not inspect their code thoroughly (which is time-consuming). We propose that vulnerability prediction using certain software metrics can support the testing process by identifying vulnerable code-components (e.g., functions, classes, etc.). Once a code-component is predicted as vulnerable, the developers can focus their testing efforts on it, thereby avoiding the time/effort required for testing the entire application. The current paper presents a study that compares how software metrics perform as vulnerability predictors for software projects developed in two different languages (Java vs Python). The goal of this research is to analyze the vulnerability prediction performance of software metrics for different programming languages. We designed and conducted experiments on security vulnerabilities reported for three Java projects (Apache Tomcat 6, Tomcat 7, Apache CXF) and two Python projects (Django and Keystone). In this paper, we focus on a specific type of code component: Functions. We apply Machine Learning models for predicting vulnerable functions. Overall results show that software metrics-based vulnerability prediction is more useful for Java projects than Python projects (i.e., software metrics when used as features were able to predict Java vulnerable functions with a higher recall and precision compared to Python vulnerable functions prediction).

The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.