Network Security Professionals Research Articles

ENHANCED NETWORK SECURITY: A DATA MINING APPROACH TO INTRUSION DETECTION

In the ever-evolving landscape of network security, the role of Intrusion Detection Systems (IDSs) is critical. These systems serve as the guardians of digital networks, defending against a relentless tide of cyber threats. However, the efficacy of IDSs in accurately detecting and preventing these threats remains a critical concern. The emergence of new attack vectors and the growing sophistication of cyberattacks underscore the need for innovative approaches to intrusion detection. Nonetheless, there is an information vacuum about the efficacy of different data mining approaches in intrusion detection. This study investigates the deployment of seven data mining models, which include Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and two types of Recurrent Neural Networks (RNN) on five (5) IDS benchmark datasets. Based on the undertaken experiments, it is evident that the RNN is the best classifier as it obtained 100% accuracy for all datasets. Due to its strength in modelling time-dependent and sequential data problems, RNN has become powerful in predicting future attacks. As our digital world evolves, so must our cyber defences; hence, this study strives to equip network security professionals with the knowledge and tools needed to fortify their networks, ensuring resilience against the ever-adaptive landscape of cyber threats.

Strengthening Wireless Network Security: Supervised Machine Learning-Based Intrusion Detection for Enhanced Threat Mitigation

With the wireless networks deemed vital to our laced and unetched world, it has become fundamental to safeguard these networks against the various cyber threats that are on the rise. As a research proposal I show concern about the application of a Supervised Machine Learning-Based Intrusion Detection system, making use of Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. The survey begins with the introduction of an intrusive inter-dataset technology that merges two catalytic datasets into a single efficient model that can address both model security and data sensitivity concerns. The evaluation of several machine learning algorithms within the proposed framework is a core component of the analysis. The accuracy measure as well as others such as recall, precision, and F1 score, are applied to evaluate the algorithms' level in detecting intrusions from different datasets.The investigation shows that the intra-dataset routing noticeably improves the detecting modules' efficiency. Notably, the model of MLP algorithm has enhanced recall which in turn shows the enhancement of the positive instance identification. SVM shows increased precision, which accounts for the improved accuracy since correct names of positive cases are produced more often. LR finds it overall enhance the precisions thus attesting to its efficiency in correct deductions. The research documents the multifaceted nature of IDS, prompting the intervention of the machine learning algorithms with ddoswdelcome intrusion detection system. A study which provides a practical guide for network administrators and security professionals hinting on how to select algorithms to meet the distinct security needs in their enterprises. These findings contribute to the open discourse on wireless network security and serve as a base for future study of this engaging research topic in constantly developing field of intrusion detection.

DDoS attack detection and mitigation using deep neural network in SDN environment

In the contemporary digital landscape, the escalating threat landscape of cyber attacks, particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern for network security. This research introduces an innovative approach to DDoS detection leveraging a deep neural network (DNN) architecture rooted in deep learning (DL) principles. The proposed model exhibits a scalable and adaptable framework, enabling meticulous analysis of network traffic data to discern intricate patterns indicative of DDoS attacks. To validate the efficacy of our methodology, rigorous evaluations were conducted using authentic real-world traffic data. The results unequivocally establish the superiority of our DNN-based approach over traditional DDoS detection techniques. This research holds significant promise for bolstering network security, particularly within the dynamic landscape of software-defined network (SDN) environments. The study's findings contribute to the continual refinement and eventual deployment of advanced measures in fortifying digital infrastructure against the evolving threat landscape. Performance metrics, including detection accuracy and loss rates, further emphasize the effectiveness of our approach across different datasets. With detection accuracy rates of 99.98%, 100%, and 99.99% for the InSDN, CICIDS2018, and Kaggle DDoS datasets, respectively, coupled with low loss rates, our DNN-based model demonstrates robust capabilities in mitigating contemporary DDoS threats. This study not only presents a novel DDoS detection approach within SDN infrastructures but also offers insights into practical implications and challenges associated with deploying DNNs in real-world SDN environments. Network security professionals can benefit from the nuanced perspectives provided, contributing to the ongoing discourse on fortifying digital networks against evolving cyber threats.

Seguridad en la Infraestructura de Redes: Desafíos y Estrategias de Protección

Network infrastructure is essential in today's society, connecting people, businesses and services around the world. However, its growing relevance has led to an increase in threats and cyber-attacks directed towards it. The main objective of this researchwas to analyze emerging threats to network infrastructure and determine optimal strategies for its protection. Using a qualitative methodology, documentary analysis and specialized literature 184review were used to collect data. The results revealed an increasing sophistication of cyber-attacks targeting network infrastructure, highlighting the need to implement proactive and adaptive security measures. Several protection strategies were also identified, including the use of artificial intelligence and machine learning to detect and mitigate threats in real time. In addition, it was found that ongoing training and awareness of network security professionals is critical to address current and future challenges.

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Snout: A Middleware Platform for Software-Defined Radios

The plethora of Internet of Things (IoT) protocols and the upcoming availability of new spectrum bands for wirelessly connected devices have made software-defined radio (SDR) technology increasingly useful to interact with radio-based communication. While SDR-based tools have grown in popularity in recent years due to their flexibility and adaptability towards new protocols, SDR software interfaces remain highly complex and technical, and inherently require specialized skillsets in digital signal processing (DSP) to operate. To address this problem, we present Snout, an SDR middleware platform that encapsulates and abstracts much of the current complexity in SDR toolchains. This allows SDR developers to create wireless networking applications usable by a wide range of users. For instance, network security professionals can monitor the IoT landscape across multiple protocols without needing to interact with the underlying software-defined DSP. Snout implements interfaces with common network analysis tools to allow for integration with traditional network security solutions, facilitating use cases such as traffic analysis or rogue device detection. Its software architecture enables scalability in terms of protocols and processing by modularizing the signal processing pipeline. To demonstrate Snout’s capabilities, we show how it can encapsulate GNU Radio flowgraphs, facilitate simultaneous multi-protocol scanning, and convert existing SDR-based protocol implementations into fully contained applications. We further demonstrate how Snout can handle GNU Radio flowgraphs with other signal processing software simultaneously. Through extensive experiments, we demonstrate that Snout incurs limited CPU performance overhead below 4% and a memory footprint below 100MB, and handles large amounts of events with sub-millisecond latency.

Analytical study of effects of DDoS mitigation methods

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructure and services. Software Defined Networks (SDNs) have emerged as a promising technology to enhance network flexibility and adaptability. This paper explores various DDoS attack mitigation approaches within the context of SDNs, with a focus on their effectiveness, limitations, and key considerations. The study begins by providing an overview of DDoS attacks and their evolving complexities. It then delves into the fundamental principles of SDNs and their potential advantages in combating DDoS attacks. Various mitigation techniques are analyzed, including rate limiting, flow diversion, and traffic anomaly detection, and their applicability in an SDN environment is discussed Our research takes a multi-dimensional approach, evaluating the performance of mitigation methods in terms of attack detection, response time, false positives, and the impact on legitimate traffic. Additionally, the study considers the adaptability of these methods to emerging threats, such as IoT-based attacks and zero-day vulnerabilities. Furthermore, we discuss the trade-offs and challenges associated with the implementation of these methods, including their impact on user experience, resource utilization, and operational overhead. The results of this comparative analysis aim to provide valuable insights for organizations and network security professionals in selecting appropriate DDoS mitigation strategies. This paper provides insights into the evolving landscape of DDoS attack mitigation in Software Defined Networks, emphasizing the need for a proactive and dynamic defence strategy to protect network infrastructure and ensure uninterrupted service availability. This study serves as a valuable resource for mitigating the risks and consequences of DDoS attacks in today's interconnected and digitally-dependent world.

Evaluation criteria for network security solutions based on behaviour analytics

This research aims to develop a set of evaluation criteria to identify and select a reliable user entity behaviour analytics (UEBA) solution for enterprises. A quantitative method is used to validate a set of evaluation criteria. Data were obtained from 280 high-profile network security professionals and analysed. Researchers used confirmatory factor analysis to validate the measurement model for convergent validity and reliability of criteria constructs, including evaluating the priority of each criterion item by security professionals. Findings depict that UEBA criteria are a multidimensional construct comprising six factors: 1) general capabilities; 2) use cases; 3) log reports for analysis of user behaviour; 4) profiling models; 5) alert reports; 6) integration with other solutions. Although the study's limitations might come from the respondents if selected globally, the results might be somewhat different. The novelty of this research work lies in developing and validating an instrument, empirically tested and validated by network security professionals and used by them to choose a reliable solution for their companies and what features of network security solutions are a must as per the perception of high profile users.

Exploration of teaching reform of network security experiment course under the background of emerging engineering education

Given the practical needs of network security professionals against the background of emerging engineering and guided by principles of cultivating stronger innovation ability and frontier problem-solving ability, this paper, a case study of a network security experiment course, proposes to build an open training platform with multidisciplinary cooperation and multi-party participation in order to provide solutions to existing problems. It attempts to enhance school-enterprise cooperation and jointly determine the complex engineering problems of network security. Following the pattern of the post-role competency, typical engineering problems, and hierarchical experimental projects, this paper explores the teaching reform of a network security experiment course by realizing individualized and differentiated evaluation of the course's teaching effectiveness.

Categorizing TLS traffic based on JA3 pre-hash values

The JA3 algorithm for fingerprinting TLS client traffic has become a popular additional tool in the tool set of network security professionals. The pre-hash value of the JA3 fingerprint lists parameter values from the TLS handshake supported by the TLS client. In this paper we present two different machine learning methods for identifying the endpoint application from TLS traffic based on the JA3 pre-hash string. Both methods were able to identify applications from Mozilla in our sample set, but had more variation with other applications. The methods can be used for improving network security accuracy.

Citadel: Cyber threat intelligence assisted defense system for software-defined networks

Defending networks is becoming more challenging due to the growing number and variety of cyber threats. On the other hand, network security professionals have new technologies and tools at their disposal. This paper focuses on a few of these technologies and investigates new ways to take advantage of them. To this end, we present Citadel, a novel security system utilizing cyber threat intelligence (CTI) to construct automated defense solutions in software-defined networking (SDN) environments. Citadel also incorporates network function virtualization (NFV) and service function chaining (SFC) to achieve flexible, cost-efficient, and proactive network defense. We examine CTI data to extract common attacker models and design security services as virtual network functions chained together using SFC to counter these threats. The modular and extensible nature of Citadel makes it suitable for incremental deployment in networks. Besides, we propose a new CTI data model to use as an extension of the existing CTI models for better compatibility with automated network defense. Extensive evaluations demonstrate that our proposals are applicable and effectively facilitate the management of agile defense in SDN/NFV-enabled networks.

D-FAC: A novel ϕ-Divergence based distributed DDoS defense system

A Distributed Denial of Service (DDoS) attack is an austere menace to extensively used Internet-based services and applications. Despite the presence of enormous DDoS defense solutions, the in-time detection of DDoS attacks poses a stiff challenge to network security professionals. The problem turns further crucial when such attacks are amalgamated with behaviorally similar flash events (FEs) wherein a large number of legitimate users starts accessing a particular service concurrently leading to the denial of service. This paper proposes an anomaly based distributed defense system called D-FAC that not only detect different type of DDoS attacks with efficacy but also efficiently mitigate their impact. D-FAC computes the information distance between legitimate and anomalous network traffic flows using information theory-based ϕ-Divergence metric to detect different types of DDoS attacks and efficiently discriminate them from FEs. D-FAC distribute the computational and storage complexity of computing ϕ-Divergence detection metric to the nearest point of presence (PoP) routers. D-FAC has been validated in an emulation based DDoSTB testbed using real DDoS attack tools and traffic generators. The results clearly show that D-FAC has outperformed existing Entropy and divergence based DDoS defense systems on various detection metrics like detection accuracy, classification rate, FPR, precision and F-measure.

Information behavior of Information Security and Cryptography professionals

This paper presents a user study of information security and cryptography professionals, focusing on the use of information behavior models. The methodological procedure proposed in this paper uses as a base model the information behavior model proposed by Choo et al. (2000), which divides human information behavior into three processes: information need, seek and use. The exploratory, descriptive and quali-quantitative study was carried out with 50 professionals from the Brazilian Community of Information Security and Cryptography (Comsic) through questionnaires, interviews and documentary analysis. Four subgroups were identified: cryptographic algorithms and protocols research group, cryptographic hardware and firmware development group, network security professionals group, and information security managers group.

SECURITY ROUTING SIMULATION THE LOCAL AREA NETWORK OF ACADEMIC DEPARTMENTS USING A LINK-STATE ROUTING PROTOCOL - OSPF

In this paper a summarized simulation and providing security communication in the local area network of academic department using a link-state routing protocol - OSPF is made. Most of the professional system administrators and IT specialists have to use and apply static and dynamic methods of information routing. Thereby, each network system administrators, security professionals and network architects can use the free of charge software network program Cisco Packet Tracer in order to design and simulate various types of computer networks.

ROUTING INFORMATION SECURITY IN THE LOCAL AREA NETWORK OF ACADEMIC DEPARTMENTS USING AN ENHANCED DISTANCE VECTOR ROUTING PROTOCOL - EIGRP

In this paper a summarized simulation and providing security communication in the local area network of academic department using an enhanced distance vector routing protocol - EIGRP is made. Most of the professional system administrators and IT specialists have to use and apply static and dynamic methods of information routing. Thereby, each network system administrators, security professionals and network architects can use the free of charge software network program Cisco Packet Tracer in order to design and simulate various types of computer networks.

Detection of DDoS attacks and flash events using novel information theory metrics

Distributed Denial of Service (DDoS) is an austere menace to network security. The in-time detection of DDoS attacks poses a stiff challenge to network security professionals. In this paper, the authors initiatively propose using a novel set of information theory metrics called ϕ-Entropy and ϕ-Divergence metrics for detecting DDoS attacks and flash events. The proposed metrics are highly sensitive towards detecting meek variations in the network traffic and elicit more information distance between legitimate and attack traffic flows as compared to existing predominantly used Generalized Entropy (GE) and Generalized Information Divergence (GID) metrics. As part of this work, a generalized detection algorithm has been proposed which uses the entropy difference between traffic flows to detect different types of DDoS attacks and FEs. The proposed detection algorithm has been validated using various publically available datasets of MIT Lincoln, CAIDA, FIFA and synthetically generated DDoSTB dataset in terms of various detection system evaluation parameters.

The New Computer Hacker’s Quest and Contest with the Experienced Hackers: A Qualitative Study applying Pierre Bourdieu’s Field Theory

IntroductionThe considerable body of literature seeking to understand computer hackers' motivations and behaviors still needs further inquiry as hacking cyber crime grows in sophistication and geographic reach (Decary-Hetu & Dupont, 2013; Nikitina, 2012; Turgeman-Goldschmidt, 2008; Yar, 2005; Taylor, 1999; Jordan & Taylor, 1998). Needing further investigation is the area of new computer hackers or newbies. This study specifically examines newbie hackers' acquisitions of skills and knowledge in their interactions with experienced hackers in a public hacker forum. The framework this study uses is Field Theory by Pierre Bourdieu (Bourdieu, 1990; Bourdieu, 1985; Bourdieu, 1977) with an emphasis on hackers' skills and knowledge trading, the cultural and social capital of the field (Bourdieu, 1986; Bourdieu, 1984), between newbies and experienced hackers.Although social media sites such as Facebook and Twitter are used by hackers, online community forums are still utilized as spaces hackers gather online (Kubitschko, 2015). Newbies join to gain access to highly specialized skills, information-sharing, networking and support from experienced hackers (Turgeman-Goldschmidt, 2008; Meyer & Thomas, 1990) but may experience admonishment, mocking and insults for their lack of skills and types of questions they ask (Decary-Hetu & Dupont, 2013; Meyer & Thomas, 1990). Hence the field of the forum operates on power relations; that is, in Bourdieu's terms (1990), the capital of skills and knowledge is contested by newbies because they want what the experienced hacker possesses. This exploratory study focuses specifically on this aspect. Research on newbies has not adequately focused on describing and analyzing the relationships between newbies and experienced hackers in online community hacker forums.The investigation of hacker forums to discover their characteristics and activities for cyber security has previously occurred. For example, Imperva, a global computer network security company, conducted a qualitative content analysis of threads within a large hacker forum. Their report identified descriptive trends, such as increased Structured Query Language (SQL) injections discussions to cause Denial of Service (DoS) attacks to a noted increase in newbies joining the forums from previous years' analyses (Imperva, 2012). Although using a considerably larger sample size than this study, 250,000 members' threads were examined (Imperva, 2012), it is clear that hacker forums require further analysis to give computer network security professionals insights into hacker behaviors and identify trends in hacker culture.Newbie hackers desire to be a part of a hacking community even if their individual goals differ. Hackers' desire, as Nissenbaum (2004) describes, total and free access to computers and information, and mistrust centralized authority, have a disdain for obstacles erected against free access to computing and desire to be evaluated by their technical virtuosity and accomplishment. Hackers hack for reasons such as: conflicts with authorities and revenge motives (Chiesa, Ducci & Ciappi, 2009), beliefs that breaking into computer systems benefit society by showing how to increase computer security (Kao, Huang & Wang, 2009), achieving feelings of power due to low self-esteem (Fotinger & Ziegler, 2004), gaining entrance to a social group (Kilger, Stutzman & Arkin, 2004) and to satisfy an addition (Taylor, 1999). These also play a part in a desire newbies have to join a hacker community and work with experienced hackers to learn skills and knowledge to hack computer systems.Yet newbie hackers face a constant battle to be accepted by hackers from the beginning of their quests to obtain the capital they need to hack. For example, experienced hackers resent script kiddies and unskilled hackers who use hacking tools such as code and scripts developed by experienced hackers. In a display of power by experienced hackers, script kiddies are identified, shamed and called pestilence and other terms, and banned from hacker communities (Taylor, Fritsch, Liederback & Holt, 2011). …

A Model of a Pragmatic Secure Intrusion Detection System for Local Area Networks

Intrusion detection is very imperative in network systems due to outstanding vulnerabilities left unaddressed by current preventive network security measures such as firewalls and encryption software. The inefficiency, inaccuracy, high false alarm rates and lack of self-defensive mechanism of existing network security systems has continued to pose serious conc ern to network users, administrators and security professionals and thus needs urgent redress. There fore, the target of this paper is to develop a model of a pragmatic secure intrusion detection system for l ocal area networks using layered framework with conditional random fields that is capable of overcoming the apparent shortcomings of present intrusion detection systems. A critical analysis of existi ng IDSs was done using the structured system analysis and design methodology (SSADM) due to the sequential configuration of the proposed security system. Furthermore, a real-time response mechanism and a self-defensive mechanism for a network intrusion detection system (NIDS) was developed and implemented. The outcome of this study was a secured IDS that would proactively address potential security vulnerabilities by resisting and detecting attacks and security policy violations reliably and effic iently in local area networks, thus making it inevitable for use in our security conscious environment of the 21 st century.

Investigating the work practices of network security professionals

Purpose – The purpose of this paper is to investigate the work practices of network security professionals and to propose a new and robust work practices model of these professionals. Design/methodology/approach – The proposed work practices model is composed by combining the findings of ten notable empirical studies performed so far this century. The proposed model was then validated by an online survey of 125 network security professionals with a wide demographic spread. Findings – The empirical data collected from the survey of network security professionals strongly validate the proposed work practices model. The results also highlight interesting trends for different groups of network security professionals, with respect to performing different security-related activities. Research limitations/implications – Further studies could investigate more closely the links and dependencies between the different activities of the proposed work practices model and tools used by network security professionals to perform these activities. Practical implications – A robust work practices model of network security professionals could hugely assist tool developers in designing usable tools for network security management. Originality/value – This paper proposes a new work practices model of network security professionals, which is built by consolidating existing empirical evidence and validated by conducting a survey of network security professionals. The findings enhance the understanding of tool developers about the day-to-day activities of network security professionals, consequently assisting developers in designing better tools for network security management.

Correspondence: Debating the Chinese Cyber Threat

Correspondence: Debating the Chinese Cyber Threat