Component Of Network Security Research Articles

Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.

Enhancing Firewall Packet Classification through Artificial Neural Networks and Synthetic Minority Over-Sampling Technique: An Innovative Approach with Evaluative Comparison

Firewall packet classification is a critical component of network security, demanding precise and reliable methods to ensure optimal functionality. This study introduces an advanced approach that combines Artificial Neural Networks (ANNs) with various data balancing techniques, including the Synthetic Minority Over-sampling Technique (SMOTE), ADASYN, and BorderlineSMOTE, to enhance the classification of firewall packets into four distinct classes: ‘allow’, ‘deny’, ‘drop’, and ‘reset-both’. Initial experiments without data balancing revealed that while the ANN model achieved perfect precision, recall, and F1-Scores for the ‘allow’, ‘deny’, and ‘drop’ classes, it struggled to accurately classify the ‘reset-both’ class. To address this, we applied SMOTE, ADASYN, and BorderlineSMOTE to mitigate class imbalance, which led to significant improvements in overall classification performance. Among the techniques, the ANN combined with BorderlineSMOTE demonstrated superior efficacy, achieving a 97% overall accuracy and consistently high performance across all classes, particularly in the accurate classification of minority classes. In contrast, while SMOTE and ADASYN also improved the model’s performance, the results with BorderlineSMOTE were notably more balanced and reliable. This study provides a comparative analysis with existing machine learning models, highlighting the effectiveness of the proposed approach in firewall packet classification. The synthesized results validate the potential of integrating ANNs with advanced data balancing techniques to enhance the robustness and reliability of network security systems. The findings underscore the importance of addressing class imbalance in machine learning models, particularly in security-critical applications, and offer valuable insights for the design and improvement of future network security infrastructures.

Asynchronous Record Alignment of Network Flows for Incident Detection and Reconstruction

In today's interconnected digital landscape, the distribution of cyber threats presents a significant challenge to cyber security. Moreover, as of 2016, the amount of data in the world exceeds one zettabyte. Because of this, evidence-based network flow analytics is a critical component of modern network management and security. Problems such as anomalies in the network flow, cyber security incidents, alert generation, data pre-processing, network monitoring, network flow complexity, and data flow patterns become difficult to detect in massive network data flows. These specific problems can be addressed using Packet capture (PCAP). PCAP analysis is a standard network forensics process and investigation for assessing network behaviour and identifying anomalies. This work presents a method for analysing network flows for probable alignment of asynchronously recorded communications in heterogeneous networks. Using a proposed method for alignment, we can identify the relevant recordings aligned over two data streams for faster and more conclusive incident analysis. We use synthetic network incident scenarios for research experiments, detailing the generation of cyber event data and impact on cloud network traffic, followed by in-depth PCAP analysis. The automated cyber-attacks are simulated within a network infrastructure generating network flows in a PCAP format. Simulated cyber-attacks range from standard port scans, service scans, and specific scenarios like SQL injection, phishing, DoS or DDoS. We define analysis objectives and criteria for the in-depth PCAP analysis and alignment. The evidence gathered showcases valuable information about network data flow and its behaviour.

Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis.

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization's network security. This is because IDSs serve as the organization's first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs' performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model's performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF's capabilities in intrusion detection and network security solutions.

Implementation of Efficient Intrusion Detection of Imbalanced Network Traffic using Deep Learning

A crucial component of network security is intrusion detection, but this task can be difficult when dealing with statistics on the distribution of network traffic, where benign cases exceed malicious ones by a large margin. The goal of this project is to create a powerful intrusion detection system using a recurrent neural network (RNN) to address this problem. RNNs are excellent at identifying temporal dependencies in sequential data, which makes them useful for examining network traffic patterns. The suggested methodology calls for preprocessing the data, balancing the dataset, feature extraction, choosing an RNN model, training, assessing, adjusting, and monitoring in real time. The network traffic data is cleaned and formatted during the preprocessing stage, and balancing algorithms make sure the RNN is trained on a representative dataset. It then extracts pertinent aspects, including behavioral patterns and packet header data. Effective intrusion detection requires the use of an appropriate RNN architecture, such as LSTM or GRU. The model is subsequently trained, assessed using a range of criteria, and refined through iterations. The optimized RNN model is then implemented for real-time intrusion detection, monitoring network traffic continuously and producing alerts for probable intrusions. This research seeks to improve the precision and efficacy of network security systems, maintaining the integrity of crucial information in the linked world of today, by utilizing RNNs for intrusion detection in imbalanced network trafficking. Keywords IDS, Imbalanced Network Traffic, Deep Learning, Recurrent Neural Network.

A Federated Learning and Blockchain-Enabled Sustainable Energy Trade at the Edge: A Framework for Industry 4.0

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.

Robust Design and Implementation of a Network IntrusionDetection System

A Network Intrusion Detection System (NIDS) is an essential component of network security that helps to identify and responds to potential security branches and attacks within a network infrastructure. Computer security has become a challenge and various tools and mechanisms have been developed in order to guarantee a safety level up to the requirements of modern life. Information security is of great concern these days due to the activities of hackers and malicious users on the Internet. Securing informationhas become a critical issue and is of growing concern as computer systems worldwide become increasingly vulnerable to the rapid increase in the volume of information being transmitted across networks and over the Internet. It describes various approaches of detecting the attacks and preventing the attacks with different techniques such as pattern matching, protocol decoding, defining rules and signatures etc. This research work introduced a technique which provided a robust intrusion detection system against attackers. The framework consists of a Network Intrusion Detection System (NIDS) for detection of traffic to and from a given network, a Host based Intrusion Detection System (HIDS) and a line for possible Intrusion Prevention Systems (IPS). The technique was developed by modeling network using a network simulation software; since a real life implementation is very costly. The result showed that the technique provided an intrusion detection system that can be used to monitor user’s activities on the Internet and other networks with relatively minimal error.

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.

Detecting Cyber Attacks by Applying MachineLearning Techniques

Because of the growth in cloud services, the growing number of web applications customers, and modifications to network infrastructure that connects devices running several operating systems, cyber security is facing new challenges. For this reason, network security components, sensors and insurance conspiracies must also be developed to meet the needs and concerns of the customers in order to combat new threats. In this post, we focus on combating application layer cyber assaults, which are rankedas the most dangerous threats and the most important test for network and cyber security. Most of this essay focuses on machine learning as a method to cope with model normal use and to detect cyber threats. Chart-based division technique and dynamic programming were used in order to obtain examples in the form of Perl Compatible Regular Expressions (PCRE) ordinary expressions. Information is gathered through HTTP requests made by the client to a web worker, which is used in the model. Using the CSIC 2010 HTTP Dataset, we've been able to demonstrate the effectiveness of our approach.

Network Penetration Intrusion Prediction Based on Attention Seq2seq Model

Intrusion detection is a critical component of network security. However, intrusion detection cannot play a very good role in the face of APT and 0 day. It needs to combine intrusion prevention, deception defense, and other technologies to ensure network security. Intrusion prediction is an important part of intrusion prevention and deception defense. Only by predicting the next possible attack can we prevent the corresponding intrusion or cheat adversary more efficiently. However, the current research on intrusion prediction has not received much attention. Most of the existing intrusion prediction research focuses on the prediction of security situation, specific security events, system calls, etc., having limitation in applicability and sequence dependency. In order to supplement this part of research, this paper reports the prediction of network penetration intrusion sequence for the first time. By introducing the ATT&CK framework, this paper builds a dictionary for the penetration intrusion types and builds three different seq2seq models. The experiment runs on the public and generated sequence data based on real APT events and adversary groups resulting that the model can predict future penetration intrusion sequence with an accuracy of up to 0.90.

Intrusion Detection System Model for IoT Networks Using Ensemble Learning

The capacity to identify breaches and malicious activity inside the Internet of Things (IoT) networks is important for network infrastructure resilience as the dependence on IoT devices and services grows. Intrusion detection systems (IDS) are basic components of network security. IDSs monitor and analyze the activity of a system in a network to identify intrusions. Existing intrusion detection systems (IDS) gather and utilize large amounts of data with irrelevant, unnecessary, and unsuitable characteristics, resulting in long detection times and low accuracy. In this paper, we present an IDS model based on a Random Forest (RF) classifier. NSL-KDD dataset is used to test the performance of the model and the satisfying performance is obtained in terms of accuracy, detection rate, and false alarm rate. The proposed model has attained an average accuracy of 99.3% and 98% for binary classification and multiclass classification, respectively. To demonstrate the efficacy of the suggested model, its accuracy was compared with some existing approaches that utilize other models such as AIDS, ELM and PCA, MapReduce-based hybrid architecture, and DRNN.

Enabling Trustworthiness in Sustainable Energy Infrastructure Through Blockchain and AI-Assisted Solutions

Network trustworthiness is a critical component of network security, as it builds on positive inter-actions, guarantees, transparency, and accountability. And with the growth of smart city services and applications, trustworthiness is becoming more important. Most current network trustworthiness solutions are insufficient, particularly for critical infrastructures where end devices are vulnerable and easily hacked. In terms of the energy sector, blockchain technology transforms all currencies into digital modes, thereby allowing one person to manage and exchange energy with others. This has drawn the attention of experts in many fields as a safe, low-cost platform to track billions of transactions in a distributed energy economy. Security and trust issues are still relatively new in the current centralized energy management scheme. With blockchain technology, a decentralized energy infrastructure enables parties to establish micro- grid trading energy transactions and apply artificial intelligence (AI). Using AI in energy systems enables machines to learn various parameters, such as predicted required amounts, excess amounts, and trusted partners. In this article, we envision a cooperative and distributed framework based on cutting-edge computing, communication, and intelligence capabilities such as AI and blockchain in the energy sector to enable secure energy trading, remote monitoring, and trustworthiness. The proposed framework can also enable secure energy trading at the edge devices and among multiple devices. There are also discussions on difficulties, issues, and design principles, as well as spotlights on some of the more popular solutions.

Exploiting the Outcome of Outlier Detection for Novel Attack Pattern Recognition on Streaming Data

Future-oriented networking infrastructures are characterized by highly dynamic Streaming Data (SD) whose volume, speed and number of dimensions increased significantly over the past couple of years, energized by trends such as Software-Defined Networking or Artificial Intelligence. As an essential core component of network security, Intrusion Detection Systems (IDS) help to uncover malicious activity. In particular, consecutively applied alert correlation methods can aid in mining attack patterns based on the alerts generated by IDS. However, most of the existing methods lack the functionality to deal with SD data affected by the phenomenon called concept drift and are mainly designed to operate on the output from signature-based IDS. Although unsupervised Outlier Detection (OD) methods have the ability to detect yet unknown attacks, most of the alert correlation methods cannot handle the outcome of such anomaly-based IDS. In this paper, we introduce a novel framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR, which is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterizes and represents the potential attack scenarios with respect to their communication relations, their manifestation in the data’s features and their temporal behavior. Beyond the recognition of known attacks, comparing derived signatures, they can be leveraged to find similarities between yet unknown and novel attack patterns. The evaluation, which is split into two parts, takes advantage of attack scenarios from the widely-used and popular CICIDS2017 and CSE-CIC-IDS2018 datasets. Firstly, the streaming alert correlation capability is evaluated on CICIDS2017 and compared to a state-of-the-art offline algorithm, called Graph-based Alert Correlation (GAC), which has the potential to deal with the outcome of anomaly-based IDS. Secondly, the three types of signatures are computed from attack scenarios in the datasets and compared to each other. The discussion of results, on the one hand, shows that SOAAPR can compete with GAC in terms of alert correlation capability leveraging four different metrics and outperforms it significantly in terms of processing time by an average factor of 70 in 11 attack scenarios. On the other hand, in most cases, all three types of signatures seem to reliably characterize attack scenarios such that similar ones are grouped together, with up to 99.05% similarity between the FTP and SSH Patator attack.

Efficient Security Solutions for IoT Devices

The Internet of Things (IoT) is a technological innovation that has revolutionized society. The IoT will forever change the way we use simple things that do very little things to smart, fully capable things. IoT devices can process and automate everyday household and workplace tasks through simple sensors. Yet despite the benefits of these devices, they are vulnerable to violations such as privacy issues and security breaches. This paper aims to provide a clearer understanding of the IoT and current threats to it by explaining why IoT devices are susceptible to attack. Moreover, the technologies used in the IoT are examined, as well as the different communication layers of the IoT and their functioning. The findings reveal that IoT devices are prone to many software and hardware vulnerabilities, not to mention the challenges that come with IoT. Solutions to these challenges are proposed, notably through the use of anomaly-based intrusion detection systems, which are critical components of network security. Using machine learning (ML) to detect potential attacks is recommended. Many proposed anomaly-based detection systems use different ML algorithms and techniques. However, there is no standard benchmark to compare these in terms of power consumption. A benchmark that measures both accuracy and power consumption to calculate and evaluate each algorithm’s implementation is proposed.

RETRACTED ARTICLE: Efficient data sensing with group key management for intelligent automation system by one-way key derivation in wireless networks

Over the last century, Wireless Sensor Network (WSN) has given major advances in several distinguishable fields. In addition, it has also evolved many features such as high device heterogeneity, high scale and supporting multiple applications. In WSN, Key management is employed (involves) as a significant component of network security, especially in multi-cast based applications and services. Group key management has a vital role in multi-cast secure communication with huge number of group members. Group key must always be updated dynamically to all groups in the network and keys to be redistributed currently to active members only. Consequently, a secret key called session key for a group of members is stored in key trees that are shared efficiently between them, in order to achieve secure group communication. The key trees are used to encrypt with other keys and data are transmitted to ensure security. Logical key hierarchy (LKH) is a hierarchical structure of multiple keys to provide a scalable and secrecy for group communication. This research work proposes a new hierarchical group key management (HGKM) using multiple logical key trees for dynamic groups in order to enhance the Quality of Service of the network. Among the multiple logical trees, primary tree can be selected by tree selection algorithm. In WSN, there are high numbers of members joining or leaving the group at any time, resulting with rekeying process becoming too large. This leads to degraded efficiency of tree-based key management system. Further, communication overhead of rekeying process can be mitigated by one-way key derivation method which integrates with multiple logical key trees. In this method, tree server does not need to be encrypted. Rather, it involves in sending a new key to members who are capable of derive their own keys in same way the server does. This results with a need for less number of encrypted keys for each rekeying process within the group tree. The fixed interval time is called batch time, which is applied in each rekeying process after nodes join or leave the group. The proposed approach can improve the bandwidth utilization effectively. The proposed HGKM method is implemented in Network Simulator (NS-2) environment and obtained results are compared with existing two methods such as LKH and multiple logical tree key management (MLT-KM). QOS of proposed approach is evaluated in terms of Bandwidth Efficiency.

Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

Abstract Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

Intrusion Prediction With System-Call Sequence-to-Sequence Model

The advanced development of the Internet facilitates efficient information exchange while also been exploited by adversaries. Intrusion detection system is an important defense component of network security that has always been widely studied in security research. However, the research of intrusion prediction, which is more critical for network security, received less attention. General research methods regarding prediction are analyzing short term of system-calls to predict forthcoming abnormal behaviors. However, those approaches have poor accuracy due to their limited sequence dependency. To solve this problem, in this paper, we take advantage of the remarkable performance of recurrent neural networks in dealing with long sequential problems, introducing the sequence-to-sequence model into our intrusion prediction work. By semantically modeling system-calls, we build a robust system-call sequence-to-sequence prediction model. Our prediction model predicts a sequence of system-calls that will be executed in the future, which will enable the monitoring of system state and the prediction of attack behavior. The experiments show that the prediction method proposed in this paper achieved well prediction performance on ADFA-LD intrusion detection test data set. Moreover, the predicted sequence, combined with the known invoked traces of system call, significantly improves the performance of intrusion detection verified on various classifiers.

Systematic Literature Review on Usability of Firewall Configuration

Firewalls are network security components that handle incoming and outgoing network traffic based on a set of rules. The process of correctly configuring a firewall is complicated and prone to error, and it worsens as the network complexity grows. A poorly configured firewall may result in major security threats; in the case of a network firewall, an organization’s security could be endangered, and in the case of a personal firewall, an individual computer’s security is threatened. A major reason for poorly configured firewalls, as pointed out in the literature, is usability issues. Our aim is to identify existing solutions that help professional and non-professional users to create and manage firewall configuration files, and to analyze the proposals in respect of usability. A systematic literature review with a focus on the usability of firewall configuration is presented in the article. Its main goal is to explore what has already been done in this field. In the primary selection procedure, 1,202 articles were retrieved and then screened. The secondary selection led us to 35 articles carefully chosen for further investigation, of which 14 articles were selected and summarized. As main contributions, we propose a taxonomy of existing solutions as well as a synthesis and in-depth discussion about the state of the art in firewall usability. Among the main findings, we perceived that there is a lack (or even an absence) of usability evaluation or user studies to validate the proposed models. Although all articles are related to the topic of usability, none of them clearly defines it, and only a few actually employ usability design principles and/or guidelines.

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

As Internet access widens, IDS (Intrusion Detection System) is becoming a very important component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles massive amounts of data traffc that contain redundant and irrelevant features, which impact the performance of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques, and support vector machine models are employed in some such methods. Good feature selection leads to better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.

Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

Intrusion detection system (IDS) is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR). In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking) and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection.