Computer Security Problems Research Articles

ПРИМЕНЕНИЕ КОМПЬЮТЕРНЫХ СЕТЕВЫХ ТЕХНОЛОГИЙ В ИНФОРМАТИЗАЦИИ ПРЕДПРИЯТИЯ

Having accepted information management and enterprise innovations as the main object of analysis, the basis is the integration of computer network technologies to create a diversified enterprise information management system and integrate it into day-to-day business management, financial control, internal control and management of electronic files of the enterprise. At the same time, this article analyzes computer viruses, hacker intrusions, security and other problems that exist when using computer network technologies, emphasizing that in the process of implementing the construction of informatization, enterprises should step up efforts to control the security of information technologies and conduct reasonable software updates and innovations in the staff team in order to provide a good guarantee of information development enterprises.

Ensuring data confidentiality and integrity in edge computing environments : A security and privacy perspective

This study examines, from a security and privacy standpoint, the significant obstacles to maintaining data integrity and confidentiality in edge computing contexts. Data security at the edge becomes critical as more and more edge devices proliferate and generate large volumes of sensitive data. The review of the literature looks at edge computing security problems that currently exist and discusses fixes to protect data integrity and confidentiality. The article explores various vulnerabilities, emphasizing the necessity for strong security systems, including data exposure risks and integrity concerns. It examines secure communication protocols, access control methods, and encryption techniques as crucial risk-reduction instruments. Anonymization and differential privacy are two privacy-preserving techniques that are investigated to find a middle ground between user privacy and data value. The article provides real-world insights through case studies and actual implementations, and it lays out metrics and evaluation criteria for determining how successful security measures are. Finally, the report highlights the need of addressing data security in changing edge computing settings and offers future directions and research difficulties.

Covering Arrays ML HPO for Static Malware Detection

Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection.

Cloud Computing Security Problems and Solution Methods

Cloud Computing Security Problems and Solution Methods

Adoption of Cloud Enabling Cyber-Security Model in Organizations

Objectives: To carry out a systematic review on Cloud Computing (CC) security problems in order to suggest a cyber-security model for a secure CC environment. Methods: ResearchGate, PLOS One, MDPI, Wiley, Springer, Scopus, IEEE Xplore, IOP Conf. Series, and TechScience Press are used as sources in this research. Cloud security, risks to CC, issues with the current models, and the proposed model are among the significant keywords included in this article. Analysis and evaluation of the cloud computing security models have been conducted on the basis of factors like the technology used, authorization, security level, malicious insiders, VPN, encryption, etc. Findings: After doing comprehensive study of the previous models, it was analyzed that the client service provider’s (CSP) are focusing on their own security but not of client’s security. One problem is that there is no real set of standards to measure cloud security. Encryption key should be known to client only. Also, clients are not aware of security policies of CSPs. Furthermore, to overcome some issues certification programs should be there for CSPs so that clients are able to judge the CSPs according to their needs. Lastly, periodical awareness programs should be conducted for both CSP and client employees. Novelty: In this research a security model is proposed in which it is tried to implement solutions to various threats like hacking, unauthorized access of data, data encryption and authentication. It has proposed to develop a Cyber Security Model (CSM) for improvement of security features of cloud when it is used by organizations. In this study features like Single Sign-On for users to get proper authorization; key supervision is used for encryption of data and VPN controller is implemented to guard data from hackers have been added. Keywords: Cloud Computing; CyberSecurity; Cloud Security Challenges; Data Protection; Security Model

Approach to Predict Software Vulnerability Based on Multiple-Level N-gram Feature Extraction and Heterogeneous Ensemble Learning

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.

Computer Security Issues and Legal System Based on Cloud Computing.

To effectively improve the security and accuracy of computer information storage, a computer security problem and legal system based on cloud computing are proposed. Firstly, this article details the evolution of cloud computing, its characteristics, architecture, and application status of cloud computing in detail. Second, we discussed security strategies to ensure the confidentiality and integrity of cloud computing information, focuses on the data encryption technology of cloud data security, and designs and implements the data backup and recovery system based on the cloud platform. The core layers of the system are the system layer and data operation layer. The system uses multithreading technology based on epoll and thread pool to improve the efficiency of data transmission. At the same time, the basic visual page is realized, and users can use the page to create a convenient operating system. Finally, the system is built in the laboratory environment and tested as a whole. The test results show that through the performance comparison with the current commonly used systems, it is found that the system in this paper has a certain improvement in data transmission rate, but the utilization rate of node CPU is as high as 40%, which leads to certain requirements for node CPU performance. Therefore, the system meets the functional requirements proposed in the design. Compared to the existing system, its performance has been found to meet the actual requirements of use, proving that the system is accessible and efficient.

Game theory approach for analysing attack graphs

Many real problems involve the simultaneous security of computer networks and systems as it penetrates all areas of our daily lives. The purpose of attack graph analysis is to be able to protect computer systems and networks against attacks related to them. In this paper, we have proposed a new approach to analysing attack graphs based on game theory in order to reduce network vulnerabilities. This approach consists of turning a computer security problem into a two-player game and extracting the best strategies for each of the both of them. The purpose of this work is to help administrator to take a good decision to better secure network by using game theory methods.

"An Overview on the Study of Data Encryption and Decryption in Cloud Computing"

While big data technologies in cloud computing are rapidly growing in popularity, privacy issues have increased dramatically. Encrypting data in real-time is one of the greatest important issues throughout data acquisition and transfer. In order to reach an acceptable performance level, many modern applications forego data encryptions, which is unsuitable with privacy issues. In light of cloud computing concepts and features, this article examines various cloud computing systems and analyzes the cloud computing security problem and solution. Data privacy but instead service availability are major security problems in cloud computing. A single security solution will not address the cloud computing security challenge; to defend the whole cloud computing system, a mix of old and new technologies and procedures must be employed in harmony. The author demonstrates the use of encryption and decryption algorithms in terms of data confidentiality, great computational efficiency, and cloud-based system effectiveness. On top of this architecture, they may enable dynamic block-level actions on data encryption blocks for insertion, removal, including update, which we believe will be future work for enhancement.

Технология идентификации ключевых особенностей в последовательностях API-вызовов вредоносных программ

Today, almost everyone is faced with computer security problems in one or another way. Antivirus programs are used to control threats to the security of malicious software. Conventional methods for detecting malware are no longer effective enough; nowadays, neural networks and behavioral analysis technology have begun to be used for these purposes. Analyzing the behavior of programs is a difficult task, since there is no clear sequence of actions to accurately identify a program as malicious. In addition, such programs use measures to resist such detection, for example, noise masking the sequence of their work with meaningless actions. There is also the problem of uniquely identifying the class of malware due to the fact that malware can use similar methods, while being assigned to different classes. In this paper, it is proposed to use NLP methods, such as word embedding, and LDA in relation to the problems of analyzing malware API calls sequences in order to reveal the presence of semantic dependencies and assess the effectiveness of the application of these methods. The results obtained indicate the possibility of identifying the key features of malware behavior, which in the future will significantly improve the technology for detecting and identifying such programs.

Deep Learning Approaches for Intrusion Detection

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

ОПЫТ ПОДГОТОВКИ КАДРОВ ДЛЯ ОБЕСПЕЧЕНИЯ КОМПЬЮТЕРНОЙ БЕЗОПАСНОСТИ ТРАНСПОРТНОЙ ОТРАСЛИ

In the context of the pandemic, the role of computer security specialists in solving the problems of uninterrupted operation of transport enterprises has increased many times. The article is devoted to summing up and analyzing the experience accumulated in the RUT (MIIT) for 20 years of training specialists in the specialty “Computer Security”, determining the prospects for the development of the educational program. The article presents a methodology for using freely distributed software created by the authors of the article in the learning process in cases where it is impossible or unsafe on real objects. The main advantage of this technique is that it takes into account the experience gained by specialists in solving real cases, allows you to reproduce real system vulnerabilities without harming real systems for training purposes, makes it possible to diagnose these vulnerabilities, exploit them, choose and compare different ways to remove them from the system. The article contains an analysis of publications devoted to the experience of training personnel in the specialty “Computer Security” in different countries using various tools. The article provides an overview of the educational program and formulates the principles that form the basis of training at our university, demonstrates examples of the use of digital doubles in the learning process, focuses on the formation of interdisciplinary con nections, skills of applying theoretical knowledge to solve practical problems and analyze the results obtained. The review of the subjects of the discipline “Methods of risk management analysis”, statistical analysis of the results of the implementation of the methodology developed by the authors in practice, and their graphical interpretation is carried out. Examples of the application of risk theory to solving computer security problems are given. The article contains examples of students ‘ participation in research activities, the creation of methodological support for the relevant educational program, and various events outside the university.

The use of virtual reality interfaces in the field of information security

Technologies of virtual and augmented reality are gaining popularity. Virtual reality is used in many areas, including beginning to be used in the field of information and computer security. In addition, virtual reality interfaces are also susceptible to attacks. However, there are still few works on research in this area. This article provides an overview of existing solutions to computer security problems using virtual and augmented reality interfaces, as well as an overview and classification of the identified threats for these interfaces themselves.

Efficient machine learning for attack detection

Abstract Detecting and fending off attacks on computer systems is an enduring problem in computer security. In light of a plethora of different threats and the growing automation used by attackers, we are in urgent need of more advanced methods for attack detection. Manually crafting detection rules is by no means feasible at scale, and automatically generated signatures often lack context, such that they fall short in detecting slight variations of known threats. In the thesis “Efficient Machine Learning for Attack Detection” [35], we address the necessity of advanced attack detection. For the effective application of machine learning in this domain, a periodic retraining over time is crucial. We show that with the right data representation, efficient algorithms for mining substring statistics, and implementations based on probabilistic data structures, training the underlying model for establishing an higher degree of automation for defenses can be achieved in linear time.

Using deep learning to solve computer security challenges: a survey

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.

Improving accuracy of HPC-based malware classification for embedded platforms using gradient descent optimization

Malware detection is still one of the difficult problems in computer security because of the daily occurrences of newer varieties of malware programs. There have been enormous efforts in developing a generalized solution to this critical security aspect, but a little has been done considering the security of resource constraint embedded devices. In this paper, we attempt to develop a lightweight malware detection tool explicitly designed for embedded platforms using micro-architectural side-channel information obtained through Hardware Performance Counters (HPCs) and high-level programs representing Operating System (OS) resources. The methodology uses statistical hypothesis testing, in the form of t-test, to develop a metric, called $$\lambda $$ , which indicates a conceptual boundary between the programs which are allowed to run on a given embedded platform, with the codes that are suspected as malwares. The metric is computed based on the observations obtained from carefully chosen features, which are tuples of high-level programs representing OS resources along with low-level HPCs. An ideal $$\lambda $$ -value for a malicious program is 1, as opposed to 0 for a benign application. However, in reality, the efficacy of $$\lambda $$ to classify a program as malware or benign largely depends on the proper assignment of weights to the tuples. We employ a gradient-descent-based learning mechanism to determine optimal choices for these weights. We present detailed experimental results on an embedded Linux running on an ARM processor which validates that the proposed lightweight side-channel-based learning mechanism improves the classification accuracy significantly compared to an ad-hoc selection of weights leading to significantly low false positives and false negatives in all our test cases.

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools.

Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources.

Comparison and Performance Evaluation of Prime Number Generation Using Sieves Algorithm

People are used prime numbers in secret codes to secure the message and data. Prime numbers are essential in computer security problem to perform certain operations over the Internet. It can be used for encryption. The larger the prime number used, the intricate it is to decipher the code. So finding the “massive” prime number is a current problem. One of the most widely used applications of prime number in computing is the RSA encryption system. A prime sieve is a fast type of algorithm for finding prime numbers. Sieve of Eratosthenes, Sieve of Sundaram and Sieve of Atkins are the algorithms that can be used to generate prime numbers. This research paper is to study and testing to find out, which algorithm is better used for large prime numbers in terms of time complexity. The prime sieve algorithm is coded in java language and the results are summarized.

Dispersion of the number of failures in restoration processes

Optimal organization of the restoration process is of significant importance in the operation of technical, information and computer systems, since failures occurring during their operation lead to substantial negative consequences. In this paper, a formula for the variance of the number of failures is obtained for the general restoration process, which depends on the restoration functions (average number of failures) of the simple and general restoration processes. Also obtained are the formulas for the variances of the number of failures and restorations during the alternating restoration process, when along with the element’s time to failure, for example, the restoration time is taken into account. For an exponential distribution with a simple and general restoration process, formulas are written for the variance of the number of failures, as well as the Chebyshev inequality and the formula for the coefficient of variation of the number of failures for a simple restoration process. The paper presents an algorithm for obtaining dispersion in the form of series for the operation time distribution laws common to the dependability theory. The developed mathematics are intended for the definition and solution of various optimization problems of information and computer security, as well as in the operation of technical and information systems, software and formware information protection facilities affected by random failures, threats of attacks and security threats.

Heterogeneous integration for artificial intelligence: Challenges and opportunities

The recent progress in artificial intelligence (AI) and machine learning (ML) has enabled computing platforms to solve highly complex difficult problems in computer vision, robotics, finance, security, and science. The algorithmic progress in AI/ML have motivated new research in hardware accelerators. The dedicated accelerators promise high energy efficiency compared to software solutions using CPU. However, as AI/ML models become complex, the increasing memory demands and, hence, high energy/time cost of communication between logic and memory possess a major challenge to energy efficiency. We review the potential of heterogeneous integration in addressing the preceding challenge and present different approaches to leverage heterogeneous integration for energy-efficient AI platforms. First, we discuss packaging technologies for efficient chip-to-chip communication. Second, we present near-memory-processing architecture for AI accelerations that leverages 3D die-stacking. Third, processing-in-memory architectures using heterogeneous integration of CMOS and embedded non-volatile memory are presented. Finally, the article presents case studies that integrate preceding concepts to advance AI/ML hardware platform for different application domains.