Efficiency Of Intrusion Detection System Research Articles

IMPROVING DETECTION FOR INTRUSION USING DEEP LSTM WITH HYBRID FEATURE SELECTION METHOD

Due to the importance of the intrusion detection system, which is considered supportive of enhancing network security. Therefore, we seek to increase the efficiency of intrusion detection systems through the use of deep learning mechanisms. However, intrusion detection algorithms still suffer from problems in the process of classification and determining the presence and type of attack, which causes a decrease in the detection rate, an increase in the number of false alarms, and reduces system performance. This is due to a large number of redundant features that are not relevant to the dataset. To find the solve for this problem, here propose a hybrid algorithm based on the use of the feature selection technique, which helps in reaching the goal optimally by choosing the best and most important features. And it works by integrating three ways to minimize the features by deleting the static features, that do not have much value from the information gain and is done before the training stage by the deep learning model of LSTM as preprocessing for the CSE-CIC-IDS data set, which helps in improving the performance of the system By minimizing the time for processing and increasing the detection rate and accuracy ratio. The results of the experiment showed a high accuracy of 99.84%.

A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs

This study proposes an innovative intrusion detection system for Android malware based on a zero-shot learning GAN approach. Our system achieved an accuracy of 99.99%, indicating that this approach can be highly effective for identifying intrusion events. The proposed approach is particularly valuable for analyzing complex datasets such as those involving Android malware. The results of this study demonstrate the potential of this method for improving the accuracy and efficiency of intrusion detection systems in real-world scenarios. Future work could involve exploring alternative feature selection techniques and evaluating the performance of other machine learning classifiers on larger datasets to further enhance the accuracy of intrusion detection systems. The study highlights the importance of adopting advanced machine learning techniques such as zero-shot learning GANs to enhance the effectiveness of intrusion detection systems in cybersecurity. The proposed system presents a significant contribution to the field of intrusion detection, providing an effective solution for detecting malicious activities in Android malware, which can improve the security of mobile devices.

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among the numerous cyber threats, denial of service (DoS) and distributed denial of service (DDoS) attacks pose significant risks, as they can render websites and servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying and mitigating these attacks due to their widespread nature, intricate patterns, and computational complexities. However, by harnessing the power of deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy in detecting and classifying DoS and DDoS attacks. The successful implementation of our proposed framework addresses the challenges posed by imbalanced data and exhibits its potential for real-world applications. By leveraging the dense channel-spatial attention mechanism, our model can precisely identify and classify DoS and DDoS attacks, bolstering the cybersecurity defenses of websites and servers. The high accuracy rates achieved across different datasets reinforce the robustness of our approach, underscoring its efficacy in enhancing intrusion detection capabilities. As a result, our framework holds promise in bolstering cybersecurity measures in real-world scenarios, contributing to the ongoing efforts to safeguard against cyber threats in an increasingly interconnected digital landscape. Comparative analysis with current intrusion detection methods reveals the superior performance of our model. We achieved accuracy rates of 99.38%, 99.26%, and 99.43% for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. These remarkable results demonstrate the capability of our approach to accurately detect and classify various types of DoS and DDoS assaults. By leveraging the inherent strengths of deep learning, such as pattern recognition and feature extraction, our model effectively overcomes the limitations of traditional methods, enhancing the accuracy and efficiency of intrusion detection systems.

Reducing False Negative Intrusions Rates of Ensemble Machine Learning Model based on Imbalanced Multiclass Datasets

In spite of the efforts to improve the efficiency of intrusion detection systems based on machine learning algorithms, these systems still need more. The false negative (FN) prediction outcome is of a major priority among other outcomes, when attacks are considered as normal by classifiers. FN outputs are highly a concern issue, especially for multiclass classification, where minor classes have less instances in imbalanced datasets. In this work, three types of well-known imbalanced multiclass classification datasets are used with ensemble machine learning classifiers. The datasets: KDD99, UNSW_NB15, and CICIDS2017 are balanced using different combination of oversampling and under-sampling techniques to improve false negative rates. Suitable performance metrics have been used to obtain significant outputs improvements in all three datasets types using Random Forest classifier. Achieved accuracies are 99.9852% for KDD99, 83.5451% for UNSW_NB15 and 99.8613% for CICIDS2017. The outcomes of the work using the mentioned datasets have been compared with state-of-the-art related works and the results show a clear improvement in false negative rates.

A two-stage intrusion detection method based on light gradient boosting machine and autoencoder.

Intrusion detection systems can detect potential attacks and raise alerts on time. However, dimensionality curses and zero-day attacks pose challenges to intrusion detection systems. From a data perspective, the dimensionality curse leads to the low efficiency of intrusion detection systems. From the attack perspective, the increasing number of zero-day attacks overwhelms the intrusion detection system. To address these problems, this paper proposes a novel detection framework based on light gradient boosting machine (LightGBM) and autoencoder. The recursive feature elimination (RFE) method is first used for dimensionality reduction in this framework. Then a focal loss (FL) function is introduced into the LightGBM classifier to boost the learning of difficult samples. Finally, a two-stage prediction step with LightGBM and autoencoder is performed. In the first stage, pre-decision is conducted with LightGBM. In the second stage, a residual is used to make a secondary decision for samples with a normal class. The experiments were performed on the NSL-KDD and UNSWNB15 datasets, and compared with the classical method. It was found that the proposed method is superior to other methods and reduces the time overhead. In addition, the existing advanced methods were also compared in this study, and the results show that the proposed method is above 90% for accuracy, recall, and F1 score on both datasets. It is further concluded that our method is valid when compared with other advanced techniques.

Review on IDS based on ML Algorithms

Abstract: Intrusion detection is one of the challenging problems encountered by the modern network security industry. The developing pace of digital assaults on framework networks as of late compounds the protection and security of PC foundation and PCs. Intrusion Detection and Prevention systems are transforming into a critical part of PC organizations and network safety. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, Random Forest, SVM, Decision Tree. This paper presents an intensive survey on different examination articles that utilized single, hybrid and ensemble classification algorithms. The outcomes measurements, weaknesses and datasets involved by the concentrated on articles in the advancement of IDS were looked at. A future heading for potential explores is likewise given. The paper addressed latest research papers written from the use of machine learning classifiers in intrusion detection systems.

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection.

Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics.

To Decrease the Issue of False Alarm Rate by Providing Authentication & Thus Improving the Efficiency of Intrusion Detection System by Comparing the Result of Filtered Clusterer Algorithm Make-Density Based Clustering Algorithm without Attribute Count

The Intrusion Detection System sends alerts when it detects doubtful activities while monitoring the network traffic and other known threats. In today’s time in the field of Cyber security Intrusion Detection is considered a brilliant topic that could be objective. But it might not remain objectionable for a longer period. For understanding Intrusion Detection, the meaning of Intrusion must be clear at first. According to the oxford’s learners dictionary “Intrusion is the act of entering a place that is private or where you may not be wanted”. For this article, here it defines intrusion as any un-possessed system or network festivity on one (or more) computer(s) or network(s). Here is the example of a faithful user trying to access the system taking more than the usual trial counts to complete his access to the particular account or trying to connect to an unauthorized remote port of a server. The ex-employee who was being fired lately can provoke intrusion or any authentic worker can also provoke intrusion or any other person from the outside world could perform it. In this clause, the average data is found as the attack which is considered as the case of false positive. In this paper, the main focus is on the illustration and a solution offered for the same problem. Here we are using the KDD CUP 1999 data set. According to the outcome, the anomaly class is the one that has a higher number of counts than this class. Even if it is the true user trying to get access but the outcome is an anomaly due to the high number of counts in the class. This paper introduces a solution for the detection of a true person and eradicates the false positive.

Reducing the False Alarm Rate in Intrusion Detection System by Providing Authentication and Improving the Efficiency of Intrusion Detection System by using Filtered Clusterer Algorithm using Weka Tool

An IDS supervises network traffic by searching for skeptical activities and previously determined threats and sends alerts when detected. In the current times, the splendors of Intrusion detection still prevail censorial in cyber safety, but maybe not as a lasting resolution. To study a plant, one must start with roots, so Cambridge dictionary defines an intrusion as "an occasion when someone goes into an area or situation where they're not wanted or expected to be". For understanding the article, we will characterize interruption as any network movement or unapproved framework identified with one or more PCs or networks. This is an interpretation of permissible use of a system attempting to strengthen his advantages to acquire more noteworthy access to the framework that he is at present endowed, or a similar client attempting to associate with an unapproved far-off port of a server. These are the interruptions which will cause from the surface world, a bothered ex-representative who was terminated recently, or from your reliable staff. In this proviso, the fair information is found as an attack when the case is a false positive. Here they are zeroing in on this issue with a representation and offering one answer for a similar issue. The KDD CUP 1999 informational index is utilized. Here we dropped the number of counts and considered the OTP authentication system. In the result of this test, it may be very well seen that on the off chance that a class has a higher number of checks, at that point this class is believed to be an anomaly class. In any case, it will be considered an oddity if the genuine individual is passing the edge esteem is considered an intruder. One arrangement is proposed to distinguish the genuine individual and to eliminate false positives.

OPTIMIZATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW

With the growth and development of the Internet, the devices and the hosts connected to the Internet have become the target for attackers and intruders. Consequently, the integrity of systems and data has become more sophisticated. Meanwhile, many institutions suffer from money-losing or other losses due to attacks on computer systems. Accordingly, the detection of intrusion and attacks has become a challenge and a vital necessity at the same time. Many different methods were used to build intrusion detection systems (IDSs), and all these methods seek to a plus the efficiency of intrusion detection systems. This paper is a survey which tries to covers some of the optimization algorithms used in the field of intrusion detection in past ten years such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Cuttlefish Algorithms (CFA), and Particle Swarm Optimization (PSO). It is hoped that this review will provide useful insights about the intrusion detection literature and is a good source for anyone interested in applying one of the used optimization algorithms in the field of intrusion detection.

EVALUATION OF THE EFFICIENCY OF INTRUSION DETECTION SYSTEMS IN PRODUCTION MANAGEMENT INFORMATION SYSTEM OF A MILITARY-INDUSTRIAL COMPLEX

Security components of the production management information system of the military-industrial complex are a single mechanism that can protect restricted access information from possible violators. If one of the elements of information security does not work efficiency, it will become a problem for the entire defense system of the military-industrial complex. Production management information systems need to take into account all available ways to prevent information leakage to do this, the owners of military-industrial complexes must conduct an efficiency assessment. The article suggests a possible way to solve the problem when considering one of the most important subsystems: intrusion detection system. Currently, there is no methodology for evaluating the efficiency of intrusion detection systems for production management information systems. This system is located on a real object located in the Irkutsk Region and belongs to the military-industrial complex.

A hybrid feature selection algorithm combining information gain and genetic search for intrusion detection

Network attacks are one of the main threats to the stable operation of smart grid equipment. As a real-time monitoring system to prevent network attacks, intrusion detection is widely used in smart grid protection. However, the massive data in the network transmission process contains a large number of redundant and irrelevant features, which makes it difficult for the intrusion detection system to process in time and reduce the efficiency. Feature selection is a method to solve this kind of problem. It can improve the speed of intrusion detection by filtering the characteristics of massive data. Therefore, a hybrid feature selection algorithm which combines information gain and genetic search to improve the work efficiency of intrusion detection systems is proposed. The algorithm is mainly divided into three parts. Firstly, the information gain value of all features is calculated by using information gain, according to which all features are ordered, and the ordered features is ranked according to an exponential increase strategy; secondly, the ranked features is used to guide the genetic algorithm search process, and a new fitness function can be used to control the search direction of genetic algorithm; finally, a classification algorithm is used to test the dataset after feature selection. In experiments, by comparing with other feature selection algorithms on 5 sets of high-dimensional UCI datasets, it is concluded that the IGExpGA proposed in this paper significantly improves the detection rate and detection speed. More importantly, in the KDD1998 network data, the algorithm proposed improves the detection rate to 98.8%, which is significantly better than other algorithms.

Research on Application of Data Mining Technology in Network Intrusion Detection

In order to improve the efficiency of intrusion detection systems, data mining technology is applied to network intrusion detection. This article introduces the basic concepts of intrusion detection systems, describes the techniques commonly used in data mining research in intrusion detection systems, proposes an intrusion detection system based on data mining, and an improved k-means algorithm.

Hybrid feature selection technique for intrusion detection system

High dimensionality's problems have make feature selection as one of the most important criteria in determining the efficiency of intrusion detection systems. In this study we have selected a hybrid feature selection model that potentially combines the strengths of both the filter and the wrapper selection procedure. The potential hybrid solution is expected to effectively select the optimal set of features in detecting intrusion. The proposed hybrid model was carried out using correlation feature selection (CFS) together with three different search techniques known as best-first, greedy stepwise and genetic algorithm. The wrapper-based subset evaluation uses a random forest (RF) classifier to evaluate each of the features that were first selected by the filter method. The reduced feature selection on both KDD99 and DARPA 1999 dataset was tested using RF algorithm with ten-fold cross-validation in a supervised environment. The experimental result shows that the hybrid feature selections had produced satisfactory outcome.

Hybrid feature selection technique for intrusion detection system

High dimensionality's problems have make feature selection as one of the most important criteria in determining the efficiency of intrusion detection systems. In this study we have selected a hybrid feature selection model that potentially combines the strengths of both the filter and the wrapper selection procedure. The potential hybrid solution is expected to effectively select the optimal set of features in detecting intrusion. The proposed hybrid model was carried out using correlation feature selection (CFS) together with three different search techniques known as best-first, greedy stepwise and genetic algorithm. The wrapper-based subset evaluation uses a random forest (RF) classifier to evaluate each of the features that were first selected by the filter method. The reduced feature selection on both KDD99 and DARPA 1999 dataset was tested using RF algorithm with ten-fold cross-validation in a supervised environment. The experimental result shows that the hybrid feature selections had produced satisfactory outcome.

Intelligent intrusion detection system using temporal analysis and type-2 fuzzy neural classification

Cyber attack detection is an important and challenging area of research in the field of information technology. In such a scenario, intruders introduce new mechanisms by applying polymorphic mechanisms in order to escape from the intrusion detection systems. This leads to loss in data and increase in security vulnerabilities. In the past, many soft computing techniques were used from the field of machine learning for enhancing the efficiency of intrusion detection systems (IDSs) in computer networks. Among them, fuzzy logic played a major role for making effective decisions. In addition, the neural networks are also contributing more in this area for training the datasets to form rules which can be used to develop an effective intrusion detection system. In this paper, we propose a new intrusion detection system by combining neural networks with temporal and type-2 fuzzy logic for performing effective classification of the dataset. In addition, a new feature selection algorithm is also proposed in this paper which uses information gain of attributes with fuzzy logic decision making for selecting the optimal number of features from the dataset. This work has been tested by using NSL-KDD dataset and through the experiments conducted in this work it is proved that the proposed system increases the intrusion detection accuracy and reduces the false positive rate when it is compared with other existing systems.

Fortifying Intrusion Detection Systems in Dynamic Ad Hoc and Wireless Sensor Networks

We investigate three aspects of dynamicity in ad hoc and wireless sensor networks and their impact on the efficiency of intrusion detection systems (IDSs). The first aspect is magnitude dynamicity, in which the IDS has to efficiently determine whether the changes occurring in the network are due to malicious behaviors or or due to normal changing of user requirements. The second aspect is nature dynamicity that occurs when a malicious node is continuously switching its behavior between normal and anomalous to cause maximum network disruption without being detected by the IDS. The third aspect, named spatiotemporal dynamicity, happens when a malicious node moves out of the IDS range before the latter can make an observation about its behavior. The first aspect is solved by defining a normal profile based on the invariants derived from the normal node behavior. The second aspect is handled by proposing an adaptive reputation fading strategy that allows fast redemption and fast capture of malicious node. The third aspect is solved by estimating the link duration between two nodes in dynamic network topology, which allows choosing the appropriate monitoring period. We provide analytical studies and simulation experiments to demonstrate the efficiency of the proposed solutions.

Nature and modeling of network anomalies

Methods and ways of denial of service attacks realization for marking valuable invariants of system behavior are described. Marking invariants of attacks and information security threats based on defined phase space will let to icrease efficiency of intrusion detection systems. In the future technology of building phase space and marking invariants will let to predict attacks and anomalies appearance in network environment.

The Analysis and Research of Intrusion Detection System Based on IPv6 Protocol Analysis

In order to solve the security issue about network intrusion in IPv6 system, two modes of intrusion detection system based on IPv6 protocol analysis have been developed and designed. Based on these two modes, packet capture module, protocol analysis module, command parsing module and out-put processing module have been designed. The accuracy and efficiency of intrusion detection system based on IPv6 protocol analysis have been verified through the test focused on the key modules.

Intrusion Detection Method Based on LEGClust Algorithm

Clustering analysis is a typical unsupervised learning technology in data mining, which can improve the efficiency of intrusion detection system. LEGClust cluster algorithm is a new clustering analysis technique and it can effectively find the arbitrary shape clusters hidden in the data. We apply this algorithm to the intrusion detection field and present an intrusion detection method. We introduce the real dissimilarity among data into the determination of data connection relationship. Experiment results on KDD CUP1999 Dataset show that LEGClust algorithm is an effective technique for intrusion detection and the improved LEGClust performs even better.