Real-time Network Environment Research Articles

Exploring the Potential of Deep Learning and Blockchain for Intrusion Detection Systems: A Comprehensive Review

Detecting intrusions in real-time within cloud networks presents a multifaceted challenge involving intricate processes such as feature representation, intrusion type classification and post-processing procedures. Distributed Intrusion Detection Systems (DIDSs) constitute a complex landscape characterized by diverse contextual nuances, distinct functional advantages and limitations specific to deployment scenarios. Despite these challenges, DIDS offers immense potential for addressing evolving intrusion detection challenges through tailored contextual adaptations and unique functional advantages. Moreover, exploring the limitations associated with different deployment contexts facilitates targeted improvements and refinements, unlocking new avenues for innovation in intrusion detection technologies. Notably, deep learning (DL) integrated with blockchain technology emerges as a superior approach in terms of security, while bioinspired models excel in Quality of Service (QoS). These models demonstrate higher accuracy across various network scenarios, underscoring their efficacy in intrusion detection. Additionally, edge-based models exhibit high accuracy and scalability with reduced delay, complexity and cost in real-time network environments. The fusion of these models holds promise for enhancing classification performance across diverse attack types, offering avenues for future research exploration. This text conducts a comprehensive comparison of performance metrics, including accuracy, response delay, computational complexity, scalability and deployment costs. The proposed Novel DIDS Rank (NDR) streamlines model selection by considering these metrics, enabling users to make well-informed decisions based on multiple performance aspects simultaneously. This unified ranking approach facilitates the identification of DIDS that achieves high accuracy and scalability while minimizing response delay, cost and complexity across varied deployment scenarios.

Network intrusion detection system using ML

In today's fast-paced technological landscape, advancements are happening at the speed of light. Every aspect of the surroundings is intricately linked to development and technology. AI, cloud computing, and the IoT reign supreme in the realm of technology. In the realm of cyberspace, everything is interconnected through computers and networking devices. With the increasing reliance on computers and the internet, ensuring safety becomes ever more critical. Safeguarding network architecture and the proper use of networking devices and tools play a vital role in cybersecurity. This study has developed a system called Network Intrusion Detection System using ML, which is suitable for home network environments. This paper has created an application that makes a significant impact on real-time network environments by providing security for a particular home network. Leveraging ML in networks has improved the results by providing accuracy and efficiency. The algorithm of Logistic Regression is used to demonstrate network behavior and classify network traffic as either in an "Attack" or "Benign" state. This helps in detecting suspicious activities across the network and can prevent them at a later stage.

Intelligent TCP Congestion Control Policy Optimization

Network congestion control is an important means to improve network throughput and reduce data transmission delay. To further optimize the network data transmission capability, this research suggests a proximal policy optimization-based intelligent TCP congestion management method, creates a proxy that can communicate with the real-time network environment, and abstracts the TCP congestion control mechanism into a partially observable Markov decision process. Changes in the real-time state of the network are fed back to the agent, and the agent makes action commands to control the size of the congestion window, which will produce a new network state, and the agent will immediately receive a feedback reward value. To guarantee that the actions taken are optimum, the agent’s goal is to obtain the highest feedback reward value. The state space of network characteristics should be designed so that agents can observe enough information to make appropriate decisions. The reward function is designed through a weighted algorithm that enables the agent to balance and optimize throughput and latency. The model parameters of the agent are updated by the proximal policy optimization algorithm, and the truncation function keeps the parameters within a certain range, reducing the possibility of oscillation during gradient descent and ensuring that the training process can converge quickly. Compared to the traditional CUBIC control method, the results show that the TCP-PPO2 policy reduces latency by 11.7–87.5%.

An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective

With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM.

A Machine Learning Approach for Intrusion Detection

Abstract: Computer networks and virtual machine security are very essential in today’s era. IDS monitors a network or system for malicious action and protects a computer network from unofficial access from users, including perhaps insiders. Various existing systems have already been developed to detect malicious activity on target machines; sometimes any external user creates some malicious behavior and gets unauthorized access to victim machines to such a behavior system considered as malicious activities or Intruder. Machine Learning (ML) algorithms are applied in IDS in order to identify and classify security threats. Numerous machine learning and soft computing techniques are designed to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data sets to detect the Intruder on the benchmark data set. In this paper, we proposed the identification of impostors using machine learning algorithms. Two different techniques have been proposed a signature with detection and anomaly-based detection. The experimental analysis demonstrates SVM, Naïve Bayes, and ANN algorithms with various data sets and demonstrates system performance in the real-time network environment.

ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows

Information-centric networking (ICN) is a promising solution that can meet the challenges of IP-based networks. ICN adopts the solution of separating name and address to solve the dual semantics problem of IP addresses, which has natural advantages in supporting mobility. These mobility support methods in ICN have realized route optimization to a certain extent. In fact, due to these methods not considering the real-time network environment, such as the bandwidth of the switching path and the characteristics of different mobile data flows, these approaches adopt the same routing optimization solution for all data flows generated by mobile users, resulting in a single switching path. The bandwidth of a single switching path may not meet the transmission requirements of mobile data flows, which will greatly affect the performance of mobile users. Therefore, how to efficiently allocate switching paths for multiple mobile data flows generated by mobile users is an urgent problem in ICN mobility support. In this paper, we first propose a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture and model the mobile data flow allocation problem as a late-binding node selection problem. We then propose a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. By simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT), we achieve late-binding node selection to minimize average packet delay. Finally, experimental results show that our proposal can achieve mobility support. Under the guarantee of a low-latency Name Resolution System (NRS), our method outperforms other methods in terms of average packet delay, packet loss rate, and handover delay.

Quality communication using varying parameters for wireless mobile ad hoc networks

Wireless Mobile Ad Hoc Networks (MANETs) present topologies where nodes act as both hosts and routes, necessitating efficient routing protocols to handle frequent route updates and transmission congestion. This paper proposes a novel routing algorithm, Fuzzy Controller Ad hoc On-demand Distance Vector (FC-AODV), to address these challenges. The FCAODV algorithm is designed to improve packet delivery ratio, throughput, end-to-end delay, packet loss, remaining energy, and routing overhead compared to the traditional AODV protocol. Through extensive simulations using NS 2, the proposed algorithm demonstrates superior performance across varying network scenarios. Results indicate an average increase of 12.4% in packet delivery ratio, 10.27% in throughput, and a decrease of 5.37% in end-to-end delay compared to AODV. Additionally, FC-AODV reduces packet loss by 5.57%, increases remaining energy by 6.4%, and decreases routing overhead by 6.37%. Future research directions include applying the FC-AODV concept to satellite networks, exploring the integration of shortest path algorithms, and evaluating FC-AODV in real-time network environments.

Entropy-based network traffic anomaly classification method resilient to deception

Entropy-based network traffic anomaly detection techniques are attractive due to their simplicity and applicability in a real-time network environment. Even though flow data provide only a basic set of information about network communications, they are suitable for efficient entropy-based anomaly detection techniques. However, a recent work reported a serious weakness of the general entropy-based anomaly detection related to its susceptibility to deception by adding spoofed data that camouflage the anomaly. Moreover, techniques for further classification of the anomalies mostly rely on machine learning, which involves additional complexity. We address these issues by providing two novel approaches. Firstly, we propose an efficient protection mechanism against entropy deception, which is based on the analysis of changes in different entropy types, namely Shannon, R?nyi, and Tsallis entropies, and monitoring the number of distinct elements in a feature distribution as a new detection metric. The proposed approach makes the entropy techniques more reliable. Secondly, we have extended the existing entropy-based anomaly detection approach with the anomaly classification method. Based on a multivariate analysis of the entropy changes of multiple features as well as aggregation by complex feature combinations, entropy-based anomaly classification rules were proposed and successfully verified through experiments. Experimental results are provided to validate the feasibility of the proposed approach for practical implementation of efficient anomaly detection and classification method in the general real-life network environment.

Hybrid Security and Energy Aware Routing for Wireless Ad hoc Networks

Wireless ad hoc networks are increased with respect to data transmission between different nodes via relay routing in real time network environment. Wireless ad hoc networks are the collection of different wireless nodes which communicate over common wireless medium. Relay configuration of particular node over relay routing is a complex task which improves the quality of service parameters based on basic standards in routing communication scenarios. Different security challenges appear in this scenario because of misbehaving nature of intermediate nodes in data transmission with respect to scalability and efficiency in wireless ad hoc network communication. So that in this paper we present and develop a Hybrid approach, which consists unital key distribution approach and dynamic source optimized routing scenario to improve scalability and efficiency for key sharing to all the nodes in wireless networks. Performance of proposed approach in terms of increasing quality of service (QOS) parameters in wireless ad hoc networks.

Link-Disjoint Multipath Routing for Network Traffic Overload Handling in Mobile Ad-hoc Networks

Mobile Ad-hoc Networks (MANETs) is the network of wireless mobile nodes with self-organizing, dynamic network connectivity without any pre-defined infrastructure for wireless communication. Error-less data broadcasting is the major role in MANET transmission. With the salient feature of dynamic topology and the distributed infrastructure, multipath routing is of great interests in MANETs. Multipath routing methods are dependable on adjacent nodes for finding the shortest path that several data packets are fallen to network traffic. Attention on the adjacent nodes can have the end-to-end delay while broadcasting the data packets into other nodes that finish up being fallen to congestion. It will also reduce network throughput and cause the network optimality problem. In this paper, we propose a new link-disjoint multipath routing method to solve the optimization problem in the real-time network environment. Hence, the proposed method is used to choose the shortest path from multiple paths in MANETs. Simulation results proved that the proposed method performs well in the dynamic network environment.

Usage of simulation for QA in real-time network environments

Usage of simulation for QA in real-time network environments

Abnormal Behavior Detection to Identify Infected Systems Using the APChain Algorithm and Behavioral Profiling

Recent cyber-attacks have used unknown malicious code or advanced attack techniques, such as zero-day attacks, making them extremely difficult to detect using traditional intrusion detection systems. Botnet attacks, for example, are a very sophisticated type of cyber-security threat. Malicious code or vulnerabilities are used to infect endpoints. Systems infected with this malicious code connect a communications channel to a command and control (C&C) server and receive commands to perform attacks on target servers. To effectively protect a corporate network’s resources against such threats, we must be able to detect infected systems before an attack occurs. In this paper, an attack pattern chain algorithm (APChain) is proposed to identify infected systems in real-time network environments, and a methodology for detecting abnormal behavior through network-based behavioral profiling is explained. APChain analyzes the attribute information of real-time network traffic, connects chains over time, and conducts behavioral profiling of different attack types to detect abnormal behavior. The dataset used in the experiment employed real-time traffic accumulated over a period of six months, and the proposed algorithm was developed into a prototype for the experiment. The C&C channel detection accuracy was measured at 0.996, the true positive rate at 1.0, and the false positive rate at 0.003. This study proposes a methodology that can overcome the limitations of conventional security mechanisms and suggests an approach to the detection of abnormal behavior in a real-time network environment.

Heuristic Optimization for Reliable Data Congestion Analytics in Crowdsourced eHealth Networks

Reliable data congestion analytics in crowdsourced eHealth networks becomes particularly important, especially in big data era, because of wide adaption of ubiquitous crowdsourced healthcare participants. Since a crowdsourced eHealth network has intermittent connectivity to its remote healthcare provider, researchers usually use some well-studied networks to model the novel network, but data congestion analytics is still a big problem in most intermittent connecting networks. In most cases, data congestion analytics may be realized by fixing the number of forwarded copies, but sometimes, it cannot suit the changing network environments well. This problem could be solved by modifying packet forwarding conditions dynamically through detecting real-time network environment. Based on this idea, in this paper, an optimized routing algorithm named RSW (reduced variable neighborhood search-based spray and wait) is proposed. In the algorithm, nodes will exchange and store each other's buffer status during their communication, based on which, current network environments will be evaluated and quantified as a real-time threshold. Then, spray and wait adapts the threshold for data congestion control. Simulation shows that the proposed algorithm increases data packet delivery probability, and optimize the overhead ratio dramatically, which can be up to ten times lower than that of standard algorithm.

Research and Implementation of Intrusion Detection Based Genetic Algorithm

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

A practical heterogeneous network optimisation algorithm based on reinforcement learning

Owing to the increasing and diversifying service requirements of wireless communications, wireless networks must coexist with heterogeneous radio systems. To realise the interconnection between different networks, it is important for the radio access network elements, such as the cellular network base stations (BSs) and the wireless local area network (WLAN) access points (APs) to be reconfigurable based on the real-time network environment. In this paper, we propose an efficient distributed reconfiguration algorithm for heterogeneous networks: the dynamic network self-optimisation algorithm (DNSA). This algorithm is based on the Q-learning algorithm and the self-optimisation of each network entity acting as independent agents. In the proposed algorithm, multiple agents perform the optimisation cooperatively to reduce the system blocking rate and improve network revenue. The dynamic network self-optimisation problem is transformed into a multiple-agent reinforcement learning problem which has much lower complexity and better performance.

In the Ubiquitous Environment with Wireless Heart Rate Meter ECG Status Information System

Recently, it is emerging in computing industry related ubiquitous. Also U-Healthcare system develops rapidly based on this trends. It is required system of health-status information to check user's health information on real-time and wireless network environment. Consequently, in this paper propose a health-Information system based on wireless and real-time environment. It can check a tremor pulse with method of photo-plethysmography. It convert pulse state information into 2-dimension bar code in order to confirm health information using mobile and also this system provide service of pulse visual record and explanation from specialist with MMS.

Q-learning based heterogenous network self-optimization for reconfigurable network with CPC assistance

With the irreversible trend of the convergence and cooperation among heterogeneous networks, there emerge some important issues for network evolution. One of them is to reconfigure network elements such as cellular base stations (BSs) or access points (APs) of wireless local area networks (WLANs) according to the real-time network environment, in order to maximize the cooperation gain of different networks. In this paper, we consider cognitive pilot channel (CPC) as an assistant to enable cooperation among heterogeneous networks. Based on the widely used reinforcement learning algorithm, this paper has proposed the heterogeneous network self-optimization algorithm (HNSA) to solve the adaptation problem in reconfigurable systems. In the algorithm, distributed agents perform reinforcement learning, and make decisions cooperatively with the help of CPC in order to reduce the system blocking rate and improve network revenue. Finally our simulation proves the anticipated goal is achieved.

Solving the response time variability problem by means of a psychoclonal approach

The response time variability problem (RTVP) is a combinatorial scheduling problem that has recently appeared in the literature. This problem has a wide range of real life applications in fields such as manufacturing, hard real-time systems, operating systems and network environments. Originally, the RTVP occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. Since the RTVP is hard to solve, heuristic techniques are needed for solving it. Three metaheuristic--multi-start, GRASP and PSO--algorithms proposed for solving the RTVP in two previous studies have been the most efficient to date in solving non-small instances of the RTVP. We propose solving the RTVP by means of a psychoclonal algorithm based approach. The psychoclonal algorithm inherits its attributes from Maslow's hierarchy of needs theory and the artificial immune system (AIS) approach, specifically the clonal selection principle. In this paper, we compare the proposed psychoclonal algorithm with the three aforementioned metaheuristic algorithms and show that, on average, the psychoclonal algorithm strongly improves on the results obtained.

Depth Control of Autonomous Underwater Vehicle Based on Open Control Platform

This paper describes depth control of autonomous underwater vehicle (AUV) using a new approach of decentralized system environment using Open Control Platform (OCP), to accommodate in changing information and control components. An LQR control algorithm with Genetic Algorithm (GA) is employed for simulation. In the simulation results, we present empirical results that show how OCP could helps to increase the degree of machine intelligence, and demonstrated by simulation on the realtime network environment.