Enhanced Intrusion Detection System Research Articles

Detection of Intrusions using Transformer - LSTM Model

The project focuses on addressing the escalating issue of web attacks and ensuring cyberspace security in the era of advancing internet technology. The primary goal is to enhance intrusion detection systems (IDS) by moving beyond traditional keyword and rule- based methods. The project explores the integration of artificial intelligence, specifically deep learning techniques, to develop a more robust and adaptive approach to identifying network intrusion events. With the limitations of conventional detection methods in mind, there is a critical need for a more effective and versatile intrusion detection system. The rise in diverse attack methods necessitates an intelligent system capable of autonomously adapting to new attack patterns without relying on manual rule creation. This project is designed to benefit both individuals and organizations reliant on network services. Improved intrusion detection ensures a higher level of cybersecurity, safeguarding sensitive data and systems. The project's outcomes have the potential to contribute significantly to the broader field of network security. Leveraging the power of deep learning, the proposed model- Transformer (autoencoder) LSTM combines Transformer and LSTM architectures. Multiple attention mechanisms are employed to select and extract features, while the LSTM model facilitates understanding the sequential relationships within network traffic, enabling the accurate identification of intrusion events. And we are comparing the model with DNN, LSTM and Transformer(autoencoder). To enhance performance, CNN and CNN+LSTM extensions were incorporated into the project, complementing the Transformer (autoencoder) LSTM model. These models aim to further refine feature extraction and exploit spatial relationships within network traffic. Key Words:intrusion detection, transformer, LSTM, deep learning.

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

AbstractNetwork Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

Intrusion Detection Systems Using Machine Learning.

The utilization of machine learning to enhance Intrusion Detection Systems (IDS). It encompasses an exploration of diverse IDS categories, fundamental evaluation metrics, and the dynamic landscape of machine learning methodologies. Recent trends underscore a shift towards the adoption of deep learning techniques for improving attack detection capabilities. Challenges arise from heightened model complexity and increased resource requirements. The paper also suggests future directions that encompass the development of updated datasets and the efficient management of resources through cloud integration. Throughout, this study emphasizes the continuous demand for research and innovation in the field of cybersecurity.

Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System

ABSTRACT This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness.

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.

Leveraging siamese networks for one-shot intrusion detection model

The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as “One-Shot Learning”, whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup’99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.

A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches.

An Enhanced Intrusion Detection System for IoT Networks Based on Deep Learning and Knowledge Graph

Nowadays, the intrusion detection system (IDS) plays a crucial role in the Internet of Things (IoT) networks, which could effectively protect sensitive data from various attacks. However, the existing works have not considered multiview features fusion and failed to capture the semantic relationships among the anomalous requests. They are not robust and cannot detect the attack types in real-time. This paper proposes a lightweight intrusion detection system based on deep learning and knowledge graph. First, our system extracts semantic relationships and key features by knowledge graph and statistical analysis. Then, IoT network requests are converted into word vectors through multiview feature fusion and feature alignment. Finally, an attention-based CNN-BiLSTM model is designed to identify malicious request attacks, which can capture long-distance dependence and contextual semantic information. Experiment results show that the proposed model significantly outperforms the existing solution in the robustness of the model. Moreover, it can select more critical features for IDS to achieve better accuracy and lower the false alarm rate. Compared with the state-of-the-art systems, the proposed IDS achieves a higher detection accuracy of 90.01%. In addition, our system can detect various stealthy attack types (including DoS, Probe, R2L, and U2L) and extract semantic relationships among features.

Tif: A Transfer Learning Approach for Enhanced Intrusion Detection System in Can and Can-Fd

Tif: A Transfer Learning Approach for Enhanced Intrusion Detection System in Can and Can-Fd

Enhanced Intrusion Detection System for an EH IoT Architecture Using a Cooperative UAV Relay and Friendly UAV Jammer

In this paper, the detection capabilities and system performance of an energy harvesting (EH) Internet of Things (IoT) architecture in the presence of an unmanned aerial vehicle (UAV) eavesdropper (UE) are investigated. The communication protocol is divided into two phases. In the first phase, a UAV relay (UR) cooperates with a friendly UAV jammer (UJ) to detect the UE, and the UR and UJ harvest energy from a power beacon (PB). In the second phase, a ground base station (GBS) sends a confidential signal to the UR using non-orthogonal multiple access (NOMA); the UR then uses its harvested energy to forward this confidential signal to IoT destinations (IDs) using the decode-and-forward (DF) technique. Simultaneously, the UJ uses its harvested energy to emit an artificial signal to combat the detected UE. A closed-form expression for the probability of detecting the UE (the detection probability, DP) is derived to analyze the detection performance. Furthermore, the intercept probability (IP) and throughput of the considered IoT architecture are determined. Accordingly, we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy performance. Monte Carlo simulations are employed to verify our approach.

An efficient intrusion detection system for cognitive radio networks with improved fuzzy logic based spectrum utilization

Wireless Communication is one of most demanded and technologically evolving field due to the growing services which is regarded as the foremost challenge. Spectrum scarcity arises due to the growing services in turn demands new spectrum bands and also the allocated spectrum is not resourcefully utilized. Cognitive radio (CR) is deliberated as key solutions for effective utilization of spectrum band. Among the CR techniques, fuzzy logic based framework forms the foundation for this research. The Spectrum Access Control is mainly achieved in the prevailing Fuzzy Logic System (FLS), however precise results are obtained due to the assumptions involved in perceiving outcome. Additionally, more concentration is given for spectrum utilization efficacy and spectrum utilization with security is not accomplished. The familiar security extortions of wireless systems are inherited by means of Cognitive Radio Networks which is mitigated through novel approach called enhanced spectrum access control (ESAC) by chiefly exploiting probabilistic neural network for spectrum access control. As well as, the antecedents are regarded for selection of spectrum for secondary user with various possibilities. An enhanced Intrusion Detection System (IDS) expending support vector machine is suggested for network anomaly behavior detection which is attained through normal protocol operation behavior, traffic flow and primary user access time. These factors help in recognition of abnormal activities for attaining security. The suggested intrusion detection has its major role in spectrum utilization performance enhancing pertaining to Throughput, System Utility Rate and Packet Delivery Ratio which is validated by the experimental results, which is the spectrum scarcity, despite the fact that new spectrum bands are greatly necessitated; in addition, underutilized licensed spectrum are presently observed. Security threats is one of the major threat of all basic wireless networks including CRNs because of inheriting property.

Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks

Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the capability of these networks to provide its functions. In this paper, an enhanced intrusion detection system (IDS) is proposed by using the modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). The GWOSVM-IDS used 3 wolves, 5 wolves and 7 wolves to find the best number of wolves. The proposed method aims to increase intrusion detection accuracy and detection rate and reduce processing time in the WSN environment through decrease false alarms rates, and the number of features resulted from the IDSs in the WSN environment. Indeed, the NSL KDD’99 dataset is used to demonstrate the performance of the proposed method and compare it with other existing methods. The proposed methods are evaluated in terms of accuracy, the number of features, execution time, false alarm rate, and detection rate. The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.

Enhanced intrusion detection system via agent clustering and classification based on outlier detection

The rapid evolution of cloud computing technology has enabled seamless connection of devices on a broad spectrum. Also, it enables storage of massive quantity of data in the form of data centers. This suggests a shared pool of resources where users situated all over the world have access to the aforementioned data centers. Such a framework has cyber-security based challenges where it becomes vulnerable to external attacks. There arises a need for an Intrusion Detection System (IDS) to prevent the system from unwanted and malicious attacks. However, the existing IDS have not been able to efficiently combinehigh accuracy with low complexity and time efficiency. In order to overcome these challenges an Enhanced Intrusion Detection System via Agent Clustering and Classification based on Outlier Detection (EIDS-ACC-OD) is proposed. At first, preprocessing is performed to remove unwanted spaces using outlier detection. Then modified K-means clustering algorithm is developed for data segmentation. Further, K-Nearest Neighbor (KNN) is utilized for categorization of the attacks.

Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things

Internet of Things (IoT) is a new age technology, developed with the vision to connect and interconnect all the objects everywhere. This technology enables an overwhelming smartness, which helps the humankind in many ways. Connecting the objects around us, make them communicate with each other towards a mission of intelligent healthcare, safety, Industrial processing applications. As the Internet of Things involved in many various entities and diverse applications, that the vulnerability to unauthorized access is much higher. Today, cyber-attacks faced by the communication networks are very strong and critically alarming. This research represents an intelligent technique or methodology to defend the security breach, developed with the enhancement of Deep Learning algorithms (Deep Belief Network), i.e., Deep Belief Network. This intelligent intrusion detection methodology scrutinizes the malicious activity that is active inside the network, and one tries to get its entry. In this paper, the investigation of embedding the Deep learning methodology is discussed. The DBN enhancement to the security network is compared with standard DGAs and IDS algorithms, and the results are analyzed.

An enhanced intrusion detection system for mobile ad-hoc network based on traffic analysis

The problem of intrusion detection in MANET’s has been approached in different dimension in this paper. This paper proposes a novel system network information based moderation model to identify and alleviate routing attacks. The proposed system uses time variant snapshots to detect routing attacks. Each node learns network details using the network information theory (NIT) to get the knowledge about the nodes of network, the neighbor locations, energy details, displacement speed from the route discovery packets and reply packets. From the learned details each node constructs the network topology at each time window to perform intrusion detection. At each packet reception, the node performs intrusion detection using NIT and TVS learned. The proposed technique has delivered effective outcome in mitigation of intrusion detection in mobile ad-hoc networks and improves the performance to a higher level.

A survey of cloud-based network intrusion detection analysis

AbstractAs network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.

Enhanced Intrusion Detection System for Detecting Rare Class Attacks using Correlation based Dimensionality Reduction Technique

Background/Objective: With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS major use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The aim of the work done is to find out the smallest subset of most important attributes to design an efficient IDS model. Methods/Statistical Analysis: By implementing Correlation Feature Selection (CFS) mechanism using 6 search algorithms, a smallest set of features sis selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found i.e.12 features. Further, the performances using Naive Bayes and Random Tree classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The simulation results with enhanced classifiers accuracy is approx. 82% to 86% for Random tree and 33% to 65% for Naive Bayes with 41 and 12 features respectively. There is a noticeable improvement in classifiers accuracy and exposure of U2R attacks s for the proposed smallest subset in comparison to other six subsets as shown in the result. Application: The proposed work with such an improved detection rate and lesser classification time and larger merits of the minimal subset found will play a vital role for the network administrator in choosing efficient IDS.

A Survey of Automatic Protocol Reverse Engineering Tools

Computer network protocols define the rules in which two entities communicate over a network of unique hosts. Many protocol specifications are unknown, unavailable, or minimally documented, which prevents thorough analysis of the protocol for security purposes. For example, modern botnets often use undocumented and unique application-layer communication protocols to maintain command and control over numerous distributed hosts. Inferring the specification of closed protocols has numerous advantages, such as intelligent deep packet inspection, enhanced intrusion detection system algorithms for communications, and integration with legacy software packages. The multitude of closed protocols coupled with existing time-intensive reverse engineering methodologies has spawned investigation into automated approaches for reverse engineering of closed protocols. This article summarizes and organizes previously presented automatic protocol reverse engineering tools by approach. Approaches that focus on reverse engineering the finite state machine of a target protocol are separated from those that focus on reverse engineering the protocol format.

Implementation of Enhanced & AdaptiveIntrusion Detection System for MANET

Mobile Ad hoc network has become one of the most important wireless communication mechanisms. Mobile Ad-hoc Networks (MANETS) is a collection of wireless mobile nodes without any infrastructure support, in which every single node works as both transmitter and receiver .Nodes in MANET can communicates directly with each other when they are in a same communication range. The self-configuring ability of mobile nodes in a MANET made them popular in vital mission applications like military use or emergency recovery. However the open medium of MANET allows them vulnerable to attacks. Because of MANET’s distributed architecture and changing topology a traditional centralized monitoring system is not longer feasible in MANET .In this case intrusion detection should be focused as another part before an attacker can damage the structure of the system. So, this work presents various intrusion detection system named as TWOACK, AACK, ACK and Enhanced Adaptive Acknowledgement (EAACK).Enhanced intrusion detection system use digital signature technique to digitally signed packets before they are transmitted to ensure integrity and confidentiality of packets.

Enhanced Intrusion Detection System for Mobile Adhoc Networks using Mobile Agents with no Manager

Now days everything is communicated via network because everybody is running a short of time. There are so many types of networks but in wireless network when data moves from one place to another, then it becomes susceptible to vulnerabilities. So there is need of secure transmission among the users. In the mobile adoc networks, moving nodes make a network with no special infrastructure and they communicate each other. So communication can be made more secure with the help of mobile agents which are the small programs which collect information from the nodes and send this information to the place where it is decided whether not there is an intrusion into the system.