Search Results (18)

This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system (IDPS) method. We deploy DL and MLP together as DLMLP. DLMLP improves the high detection of all intrusion attack features on the Internet of Things (IoT) device dataset, known as the CICIoT2023 dataset. We reference the CICIoT2023 dataset from the Canadian Institute of Cybersecurity (CIC) IoT device dataset. Our proposed method, the deep learning multilayer perceptron intrusion detection and prevention system model (DLMIDPSM), provides IDPST (intrusion detection and prevention system topology) capability. We use our proposed IDPST to capture, analyze, and prevent all intrusion attacks in the dataset. Moreover, our proposed DLMIDPSM employs a combination of artificial neural networks, ANNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Consequently, this project aims to develop a robust real-time intrusion detection and prevention system model. DLMIDPSM can predict, detect, and prevent intrusion attacks in the CICIoT2023 IoT dataset, with a high accuracy of above 85% and a high precision rate of 99%. Comparing the DLMIDPSM to the other literature, deep learning models and machine learning (ML) models have used decision tree (DT) and support vector machine (SVM), achieving a detection and prevention rate of 81% accuracy with only 72% precision. Furthermore, this research project breaks new ground by incorporating combined machine learning and deep learning models with IDPS capability, known as ML and DLMIDPSMs. We train, validate, or test the ML and DLMIDPSMs on the CICIoT2023 dataset, which helps to achieve higher accuracy and precision than the other deep learning models discussed above. Thus, our proposed combined ML and DLMIDPSMs achieved higher intrusion detection and prevention based on the confusion matrix’s high-rate attack detection and prevention values. Full article

Internet of Things (IoT) devices usually face some difficulty in supporting complex security protocols or intrusion-prevention mechanisms, due to their limited system resources. As a result, IoT devices are fraught with significant security vulnerabilities and are vulnerable to cyberattacks. Correspondingly, the Internet-wide port scan (IWPS) technique has garnered significant attention for its ability to discover and probe Internet-wide connected IoT devices. However, the existing scanners for IWPSs are often not satisfactory in terms of scan efficiency. Improving the scan rate is an important avenue in IWPS research. In this paper, we found, through experimental analysis, that the regional characteristics of scanners greatly affect the scan rate. Based on this, we then proposed an area-aware IWPS approach, to improve scan efficiency. Firstly, we clustered the scanners according to the region, and we built an average delay table for each cluster. The average delay table records the average time delay for scanners in the cluster to detect IP addresses in different regions. Secondly, to avoid wasting resources, we also designed a two-layer balancing mechanism, to ensure the workload balance of the system. Finally, we performed extensive experiments on a real platform to demonstrate the effectiveness of our algorithm. The scan rate of our proposed approach improved compared to that of the most popular open scan tool, Nmap, by 3–4 times, and the detection accuracy increased by 8%. Full article

Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification. However, traditional approaches are not always applicable in a real-time environment as they do not integrate concrete traffic management after a malicious packet pattern has been identified. In this paper, a novel combined approach to both identify and discard potential malicious traffic in a real-time fashion is proposed. In detail, a Long Short-Term Memory (LSTM) supervised artificial neural network model is provided in which consecutive packet groups are considered as they flow through the corporate network. Moreover, the whole IDS architecture is modeled by a Petri Net (PN) that either blocks or allows packet flow throughout the network based on the LSTM model output. The novel hybrid approach combining LSTM with Petri Nets achieves a 99.71% detection accuracy—a notable improvement over traditional LSTM-only methods, which averaged around 97%. The LSTM–Petri Net approach is an innovative solution combining machine learning with formal network modeling for enhanced threat detection, offering improved accuracy and real-time adaptability to meet the rapid security needs of virtual environments and CPS. Moreover, the approach emphasizes the innovative role of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) as a form of “virtual sensing technology” applied to advanced network security. An extensive case study with promising results is provided by training the model with the popular IDS 2018 dataset. Full article

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods. Full article

High-performance computing (HPC) relies heavily on network security, particularly when supercomputing services are provided via public networks. As supercomputer operators, we introduced several security devices, such as anti-DDoS, intrusion prevention systems (IPSs), firewalls, and web application firewalls, to ensure the secure use of supercomputing resources. Potential threats are identified based on predefined security policies and added to the firewall rules for access control after detecting abnormal behavior through anti-DDoS, IPS, and system access logs. After analyzing the status change patterns for rule policies added owing to human errors among these added firewall log events, 289,320 data points were extracted over a period of four years. Security experts and operators must go through a strict verification process to rectify policies that were added incorrectly owing to human error, which adds to their workload. To address this challenge, our research applies various machine- and deep-learning algorithms to autonomously determine the normalcy of detection without requiring administrative intervention. Machine-learning algorithms, including naïve Bayes, K-nearest neighbor (KNN), OneR, a decision tree called J48, support vector machine (SVM), logistic regression, and the implemented neural network (NN) model with the cross-entropy loss function, were tested. The results indicate that the KNN and NN models exhibited an accuracy of 97%. Additional training and feature refinement led to even better improvements, increasing the accuracy to 98%, a 1% increase. By leveraging the capabilities of machine-learning and deep-learning technologies, we have provided the basis for a more robust, efficient, and autonomous network security infrastructure for supercomputing services. Full article

The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems. Full article

This article presents a statistical approach using entropy and classification-based analysis to detect anomalies in industrial control systems traffic. Several statistical techniques have been proposed to create baselines and measure deviation to detect intrusion in enterprise networks with a centralized intrusion detection approach in mind. Looking at traffic volume alone to find anomalous deviation may not be enough—it may result in increased false positives. The near real-time communication requirements, coupled with the lack of centralized infrastructure in operations technology and limited resources of the sensor motes, require an efficient anomaly detection system characterized by these limitations. This paper presents extended results from our previous work by presenting a detailed cluster-based entropy analysis on selected network traffic features. It further extends the analysis using a classification-based approach. Our detailed entropy analysis corroborates with our earlier findings that, although some degree of anomaly may be detected using univariate and bivariate entropy analysis for Denial of Service (DOS) and Man-in-the-Middle (MITM) attacks, not much information may be obtained for the initial reconnaissance, thus preventing early stages of attack detection in the Cyber Kill Chain. Our classification-based analysis shows that, overall, the classification results of the DOS attacks were much higher than the MITM attacks using two Modbus features in addition to the three TCP/IP features. In terms of classifiers, J48 and random forest had the best classification results and can be considered comparable. For the DOS attack, no resampling with the 60–40 (training/testing split) had the best results (average accuracy of 97.87%), but for the MITM attack, the 80–20 non-attack vs. attack data with the 75–25 split (average accuracy of 82.81%) had the best results. Full article

The vulnerability of Apache Log4j, Log4Shell, is known for its widespread impact; many attacks that exploit Log4Shell use obfuscated attack patterns, and Log4Shell has revealed the importance of addressing such variants. However, there is no research which focuses on the response to variants. In this paper, we propose a defense system that can protect against variants as well as known attacks. The proposed defense system can be divided into three parts: honeypots, machine learning, and rule generation. Honeypots are used to collect data, which can be used to obtain information about the latest attacks. In machine learning, the data collected by honeypots are used to determine whether it is an attack or not. It generates rules that can be applied to an IPS (Intrusion Prevention System) to block access that is determined to be an attack. To investigate the effectiveness of this system, an experiment was conducted using test data collected by honeypots, with the conventional method using Suricata, an IPS, as a comparison. Experimental results show that the discrimination performance of the proposed method against variant attacks is about 50% higher than that of the conventional method, indicating that the proposed method is an effective method against variant attacks. Full article

Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system’s effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS. Full article

The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios. Full article

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed. Full article

A compromised Smart Grid, or its components, can have cascading effects that can affect lives. This has led to numerous cybersecurity-centric studies focusing on the Smart Grid in research areas such as encryption, intrusion detection and prevention, privacy and trust. Even though trust is an essential component of cybersecurity research; it has not received considerable attention compared to the other areas within the context of Smart Grid. As of the time of this study, we observed that there has neither been a study assessing trust within the Smart Grid nor were there trust models that could detect malicious attacks within the substation. With these two gaps as our objectives, we began by presenting a mathematical formalization of trust within the context of Smart Grid devices. We then categorized the existing trust-based literature within the Smart Grid under the NIST conceptual domains and priority areas, multi-agent systems and the derived trust formalization. We then proposed a novel substation-based trust model and implemented a Modbus variation to detect final-phase attacks. The variation was tested against two publicly available Modbus datasets (EPM and ATENA H2020) under three kinds of tests, namely external, internal, and internal with IP-MAC blocking. The first test assumes that external substation adversaries remain so and the second test assumes all adversaries within the substation. The third test assumes the second test but blacklists any device that sends malicious requests. The tests were performed from a Modbus server’s point of view and a Modbus client’s point of view. Aside from detecting the attacks within the dataset, our model also revealed the behaviour of the attack datasets and their influence on the trust model components. Being able to detect all labelled attacks in one of the datasets also increased our confidence in the model in the detection of attacks in the other dataset. We also believe that variations of the model can be created for other OT-based protocols as well as extended to other critical infrastructures. Full article

The present paper deals with selected aspects of energy prosumers’ security needs. The analysis reported aim to illustrate the concept of the implementation of intrusion-detection systems (IDS)/intrusion-prevention systems (IPS), as supporting agent systems for smart grids. The contribution proposes the architecture of an agent system aimed at collecting, processing, monitoring, and possibly reacting to changes in the smart grid. Furthermore, an algorithm is proposed to support the construction of a smart-grid-operating profile, based on a set of parameters describing the devices. Its application is presented in the example of data collected from the network, indicating the process of building a device-operation profile and a possible mechanism for detecting its changes. The proposed algorithm for building the operating profile of devices in the smart grid, based on the mechanism of continuous learning by the system, allows for detecting network malfunctions not only in terms of individual events but also regarding limits of the scope of system alerts, by determining the typical behavior of devices in the smart grid. The paper gives recommendations to a software-agent system development, which is dedicated to detecting and preventing anomalies in smart grids. Full article

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts. Full article

Intrusion Detection Systems (IDS) are used to prevent attacks by detecting potential harmful intrusion attempts. Currently, there are a set of available Open Source IDS with different characteristics. The Open Source Host-based Intrusion Detection System (OSSEC) supports multiple features and its implementation consists of Agents that collect and send event logs to a Manager that analyzes and tests them against specific rules. In the Manager, if certain events match a specific rule, predefined actions are triggered in the Agents such as to block or unblock a particular IP address. However, once an action is triggered, the systems administrator is not able to centrally check and obtain detailed information of the past event logs. In addition, OSSEC may assume false positive or negative detections and their triggered actions: previously harmless but blocked IP addresses by OSSEC have to be unblocked in order to reestablish normal operation or potential harmful IP addresses not previously blocked by OSSEC should be blocked in order to increase protection levels. These operations to override OSSEC actions must be manually performed in every Agent, thus requiring time and human resources. Both these limitations have a higher impact on large scale OSSEC deployments assuming tens or hundreds of Agents. This paper proposes an extension to OSSEC that improves the administrator analysis capability by maintaining, organizing and presenting Agent logs in a central point, and it allows for blocking or unblocking IP addresses in order to override actions triggered by false detections. The proposed extension aims to increase efficiency of time and human resources management, mainly considering large scale OSSEC deployments. Full article