SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques (original) (raw)
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AI-Driven Intrusion Detection Systems:Leveraging Deep Learning for Network Security
Nanotechnology Perceptions, 2024
In order to improve network security, this study investigates the integration of deep learning and artificial intelligence (AI) in the development of advanced intrusion detection systems (IDS). The inadequacy of traditional security methods has been demonstrated by the exponential rise in cyber threats that target complex network systems. Deep learning techniques are used by AI-driven IDS to evaluate large datasets, allowing for the real-time identification and categorisation of normal and deviant behaviour. This paper examines many deep learning approaches, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Recurrent Neural Networks (RNNs), emphasising how well these methods detect sophisticated attacks, such as advanced persistent threats and zero-day exploits. Furthermore, these systems' performance is assessed using important metrics including recall, accuracy, and precision. The results highlight how deep learning has the ability to transform intrusion detection and hence greatly increase the overall resilience of network security frameworks against changing cyber threats.
Long Short-Term Memory (LSTM) Deep Learning Method for Intrusion Detection in Network Security
International Journal of Engineering Research and, 2020
Nowadays, large numbers of people were affected by data infringes and cyber-attacks due to dependency on internet. India is lager country for any resource use or consumer. Over the past ten years, the average cost of a data breach has increased by 12%. Hacking in India is take share of 2.3% of global criminal activity. To prevent such malicious activity, the network requires a system that detects anomaly and inform to the admin or service operator for taking an action according to the alert. System used for intrusion detection (IDS) is software that helps to identify and observes a network or systems for malicious, anomaly or policy violation. Deep learning algorithm techniques is an advanced method for detect intrusion in network. In this paper, intrusion detection model is train and test by NSL-KDD dataset which is enhanced version of KDD99 dataset. Proposed method operations are done by Long Short-Term Memory (LSTM) and detect attack. So admin can take action according to alert for prevent such activity. This method is used for binary and multiclass classification of data for binary classification it gives 99.2% accuracy and for multiclass classification it gives 96.9% accuracy.
Network Intrusion Detection System using Deep Learning
Procedia Computer Science, 2021
The widespread use of interconnectivity and interoperability of computing systems have become an indispensable necessity to enhance our daily activities. Simultaneously, it opens a path to exploitable vulnerabilities that go well beyond human control capability. The vulnerabilities deem cyber-security mechanisms essential to assume communication exchange. Secure communication requires security measures to combat the threats and needs advancements to security measures that counter evolving security threats. This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks. The emphasis is how deep learning or deep neural networks (DNNs) can facilitate flexible IDS with learning capability to detect recognized and new or zero-day network behavioral features, consequently ejecting the systems intruder and reducing the risk of compromise. To demonstrate the model's effectiveness, we used the UNSW-NB15 dataset, reflecting real modern network communication behavior with synthetically generated attack activities.
A Proposed Method for Detecting Network Intrusion Using Deep Learning Approach
2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA)
NIDSs, also known as network intrusion detection systems, are essential for protecting computer networks. Nonetheless, there are concerns about the viability and sustainability of current methods for meeting the needs of modern networks. These issues are more specifically connected to the decreased detection accuracy and the increased human involvement needed. This paper introduces a novel deep-learning intrusion detection method to address these problems. We use a deep learning method by creating a Deep Neural Network (DNN) model for intrusion detection systems and training it using the NSLKDD Dataset. From the 41 features in the NSL-KDD Dataset, we only use 37 basic features in this work. We demonstrate from various studies that the deep learning approach has much potential for use in NIDs. In this paper, we show the effectiveness of our method and compare it to a few previous studies in terms of accuracy, precision, recall, and f-measure values.
International journal of data and network science, 2023
With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.
Intrusion detection systems using long short-term memory (LSTM)
Journal of Big Data, 2021
An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and mul...
A Method For Network Intrusion Detection Using Deep Learning
Journal of Student Research
In an increasingly digitally reliant world, organizations are facing the ever more challenging problem of how to best defend their digital information and infrastructure. Current non-machine learning methods for detecting network intrusion, like signature-based and anomaly-based algorithms, are slow and unreliable. Signature based detection holds signatures, or known information and warning signs, about a known attack and compares them to the current flow of data. If a signature matches with the network activity, users and network administrators are notified. Anomaly based detection is where the system monitors current network traffic and compares it to a set baseline traffic. Again, if any unusual traffic occurs, members of the network are notified. In this research, new advancements in deep learning algorithms are used to bolster the defenses of digital networks. Neural networks are used to create a multi-class classifier, which will determine whether the network activity is a cer...
Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks
Information
An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying t...
Predictive Model for Network Intrusion Detection System Using Deep Learning
Revue d'Intelligence Artificielle, 2020
Given the recent COVID-19 situation, many organizations and companies have asked their employees to work from home by connecting to their on-premises servers. This situation may continue a much more extended period in the future, thereby opening more threats to confidentiality and security to the information available in the organizations. It becomes of hell of a task for network administrators to counter the threats. Intrusion Detection Systems are deployed in firewalls to identify attacks or threats. In preset modern technologies, Network Intrusion Detection System plays a significant role in defense of the network threat. Statistical or pattern-based algorithms are used in NIDS to detect the benign activities that are taking place in the network. In this work, deep learning algorithms have developed in NIDS predictive models to detect anomalies and threats automatically. Performance of the proposed model assessed on the NSL-KDD dataset in the view of metrics such as accuracy, recall, precision, and F1-score. The experimental results show that the proposed deep learning model outperforms when compared with existing shallow models.
Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study
Journal of Information Security and Applications , 2020
In this paper, we present a survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines , and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.