An Intelligent Security System Using Enhanced Anomaly-Based Detection Scheme

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

At present, APT attack detection has become the focus of the network security protection field. APT attacks are one of the most difficult attacks in cyber attacks. The complexity and variability of APT attack behavior greatly increases the difficulty of attack detection. In order to cope with APT attack, some well-known network security companies at home and abroad have developed a commercial APT intrusion detection system. This highly targeted attack can not be identified by the traditional intrusion detection system. Therefore, in order to deal with this new type of cyber attack. The paper proposes a new method to detect APT attack from different organizations. Data mining algorithm is used to analyze every organization’s APT network attack behavior and obtain association rules, so as to customize the design of the Snort rules and apply them to intrusion detection system. Experiments have shown that the evaluation index of the intrusion detection system using the extended Snort rule is significantly better than the traditional Snort intrusion detection system when detecting the same test data. The precision of the extended Snort intrusion detection system is as high as 98.3%, and the false alarm rate is almost 0, which ultimately achieves the purpose of APT detection.

The rapid growth of the internet and digital communication, network security has become a critical concern. Traditional Intrusion Detection Systems (IDS), especially rule-based or signature-based systems, are effective only against known attacks [1], [2]. However, they fail to detect novel or zero-day threats because they rely on predefined signatures [5], [7]. To overcome this limitation, anomaly detection techniques have gained importance. Anomaly-based systems monitor network traffic and identify deviations from normal behavior, which could indicate potential cyber-attacks [6]. This project focuses on Network Anomaly Detection using Deep Learning techniques [3]. The proposed system leverages models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), or Long Short-Term Memory (LSTM) networks to learn patterns of normal network traffic and accurately identify abnormal or malicious activities [4], [5]. Deep learning models are capable of handling large-scale, high-dimensional data, and can automatically extract complex features without manual intervention [3], [6]. The objective of this work is to design a robust, intelligent intrusion detection framework that can not only detect known threats but also adapt to new and evolving attack patterns. The system will be trained and validated using benchmark datasets like NSL-KDD or CICIDS2017 [1], [2]. Performance will be measured using metrics such as accuracy, precision, recall, and F1-score. By implementing deep learning-based anomaly detection, this project aims to enhance the efficiency of network security, minimize false positives, and provide a scalable solution for real-time intrusion detection in modern networks [5], [6], [7].

MP-MID: Multi-Protocol Oriented Middleware-level Intrusion Detection method for wireless sensor networks

Intrusion detection has become a critical component of network administration due to the vast number of attacks persistently threaten our computers. Traditional intrusion detection systems are limited and do not provide a complete solution for the problem. They search for potential malicious activities on network traffics; they sometimes succeed to find true security attacks and anomalies. However, in many cases, they fail to detect malicious behaviours (false negative) or they fire alarms when nothing wrong in the network (false positive). In addition, they require exhaustive manual processing and human expert interference. Applying Data Mining (DM) techniques on network traffic data is a promising solution that helps develop better intrusion detection systems. Moreover, Network Behaviour Analysis (NBA) is also an effective approach for intrusion detection. In this paper, we discuss DM and NBA approaches for network intrusion detection and suggest that a combination of both approaches has the potential to detect intrusions in networks more effectively.

Based on the research of the traditional intrusion detection technology analysis, statistical model for the formation of the rule base, management and the ability of intrusion detection and so on there are still some deficiencies and defects, due to the actual test result can not meet the requirements. This article is based on the traditional intrusion detection system in the high miss rate and error rate, slow training process and low detection efficiency problems to be solved, this paper proposes a intrusion detection system based on BP neural network.

The Industrial Internet of Things (IIoT) has brought transformative changes to industrial sectors by enabling intelligent automation, real-time monitoring, and data-driven decision-making. However, this increased connectivity and system integration have expanded the cybersecurity threat landscape, exposing critical infrastructure to sophisticated cyberattacks. Traditional Intrusion Detection Systems (IDS), particularly those relying on static signature-based detection, have proven inadequate in addressing the dynamic, heterogeneous, and resource-constrained nature of IIoT environments. Motivated by these emerging challenges, this paper presents a comprehensive review of AI-based IDS solutions developed over the past decade, with a specific focus on IIoT contexts. In doing so, it addresses key limitations in prior literature, including an insufficient focus on industry-specific constraints, a lack of attention to explainability and adversarial robustness, and an absence of a structured, future-oriented research agenda. The primary objectives are to synthesize the evolution of IDS methodologies, provide a multidimensional taxonomy that incorporates learning paradigms, deployment layers, and resource constraints, and critically assess the suitability of available datasets and benchmarks. The review highlights significant shifts from conventional machine learning techniques toward more adaptive, lightweight, explainable, and federated learning approaches. Additionally, it proposes a structured research roadmap outlining short-, mid-, and long-term directions for enhancing IDS in IIoT, such as edge-based detection, the integration of explainable AI (XAI), and the development of resilient, self-adaptive intrusion detection architectures. This paper offers a unified, actionable reference for researchers, practitioners, and policymakers seeking to strengthen industrial cybersecurity in increasingly complex and intelligent IIoT systems.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.

With the rapid development of computer network and applications, attacks are becoming more and more complicated and elusive. The traditional intrusion detection systems have been unable to meet new security requirements. This paper proposes a collaborative intrusion detection model based on multi-agents. In this model, four kinds of agents are defined, which are organized in a hierarchical structure. The basic agents in every host or at the entrances of subnets are responsible for performing the simple detection and response tasks. The complicated collaboration task is executed by some separate coordination agents, which are responsible for synthetically analyzing the suspicious behavior that the lower-level agents are unable to identify. Coordination agents are also able to assign the task to the lower-level associated agents. Based on the hierarchical structure, the formal description of the model is given. By adaptive policies and dynamic association among some elements, this model provides dynamic adaptability to the changing environment and attacks. In addition, this paper also proposes the concept of coordination domain which facilitates the management of collaborative detection. The model lays a theoretical foundation for constructing dynamically adaptive intrusion detection system.

Software-defined networking (SDN) is an innovative network technology. It changed the world of computer networking by providing solutions to many challenges. SDN provides programmability, easy and centralized network management, dynamic configuration, and improved security. Although SDN offers remarkable benefits but it provides centralized network management which is prone to attacks. So, intrusion detection systems (IDS) are essential to detect and prevent security attacks in SDN. Traditional IDS follow a centralized machine learning approach which causes vulnerabilities in IDS. Old-style IDS lack data privacy preservation, and solution for training data unavailability due to privacy. Federated learning (FL) is a distributed machine learning approach which provides a collaborative training approach without data sharing. In FL, training is performed on multiple nodes creating a global model without sharing the data. To address challenges and the limitations of traditional IDS, we proposed a FL based multi class classification IDS for SDN. FL delivers an efficient and scalable solution to address challenges of traditional IDS. The proposed model enhances security of SDN by not requiring the centralization of data. To test the impact and efficiency of proposed model, we used a latest and realistic cybersecurity dataset. We also compared the proposed model with state of art existing multi class classification studies. The results and their comparison with existing studies highlight the potential of proposed model to enhance network security while providing a privacypreserving learning environment for intrusion detection.

The traditional Intrusion Detection System (IDS) generally use the misuse detection model based on rules because this model has low false alarm rate. But the disadvantage of this model is that it could not detect the new attacks, even the variation of existed ones. In this paper we proposed a novel model based on KPCA and SVM to solve the mentioned problem above. Different from traditional IDS, we added a pre-process module before the classifier. We use principal components extracted from the input data using KPCA, which is the main part of the pre-process module, as input of the SVM classifier that differentiates the normal and abnormal actions. Applying proposed system to KDDCUP99 data, experimental results clearly demonstrate that this model has a remarkable performance in detecting both existed intrusions and mutated ones.

Nowadays, cyber-attacks are beginning to be smart and hard to be detected, these attacks can be classified as Advance Persistence Threat(APT), which are hard to be detected and need sophisticated mechanisms of cybersecurity detection systems to be implemented to detect these types of attacks. Snort is an Intrusion Detection System (IDS) open source application that gained a high level of trust from hundreds of companies by using it as an IDS sensor whether Host-based Intrusion Detection System (HIDS) or Network Intrusion Detection System (NIDS) mode. Snort is depending on rules that are predefined to detect known attacks where if there is a new attack released and have not been registered to CheckPoint as an attack, then Snort will not detect it as an attack and the attack will bypass. The main problem that stands with traditional IDS (Snort) is the rate of false positive alerts. A new technique has been revealed by understanding the behavior of the traffic flow and deciding if the traffic flow matches the attributes that have abnormal activities that the traditional misuse IDS cannot detect. The new technique is a machine learning process that depending on training and testing the data of the traffic after converting the PCAP file to CSV file through an application that calls CICFlowMeter. it is an open source machine learning application that used as GUI mode calls Weka. After the PCAP file converted through CICFlowMeter, a dataset end with CSV extension will be generated with 80 plus attributes that Weka will learn as the training phase after that the testing phase will determine the matched traffic if they are normal or abnormal.

Intrusion detection is an important part of network security protection. Traditional intrusion detection systems (IDSs) only focus on low-level attacks and raise alerts independently, though there may be logical connections between them. At the same time, the amount of alerts becomes unmanageable including actual alerts mixed with false alerts. Therefore, improved techniques are needed. The general idea is to introduce collaboration achieved by taking advantage of vulnerability analysis as contextual information and thus enable IDSs to correctly identify successful attacks while simultaneously reducing the number of false positives and providing a stronger validation attack scenario. In particular, with the verification pattern with precondition and effect of successful attack and necessary context (mainly modeled as host and connectivity information), the architecture that proposes in this paper can reduce the false alert rate and identify true alerts corresponding to successful attacks to construct attack scenario. Through the experimental results with DARPA Data Sets 2000 from Lincoln laboratory and the Treasure Hunt Dataset, it demonstrates the potential of the proposed techniques.

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem.

With the rapid development of information technology, network security issues have become increasingly prominent. In particular, data security intrusions pose serious threats to the data privacy and system security of enterprises and individuals. Traditional intrusion detection systems often exhibit low detection accuracy and high false alarm rates when faced with complex and dynamic network environments and diverse attack methods. Therefore, this paper proposes a data security intrusion detection system based on deep learning, which integrates the Mamba model and ECANet model and employs an end-to-end learning approach for training and optimization. First, the Mamba model is introduced for preliminary data feature extraction, whose efficient feature representation capabilities provide a solid foundation for the subsequent detection process. Then, by integrating the ECANet model, feature selection is further optimized through the attention mechanism, enhancing the model’s focus on important features. Finally, an end-to-end learning approach is adopted to train and optimize the entire system, ensuring excellent performance and robustness in practical applications. Experimental results show that the proposed intrusion detection system demonstrates higher detection accuracy on multiple test datasets, improving by approximately 5% compared to traditional methods, providing a new and effective solution for data security.