The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems. However, such static IDSs struggle with real-world scenarios where IoT system behaviors and attack strategies can undergo rapid evolution, necessitating dynamic and adaptable IDSs. In response to this challenge, we propose AOC-IDS, a novel online IDS that features an autonomous anomaly detection module (ADM) and a labor-free online framework for continual adaptation. In order to enhance data comprehension, the ADM employs an Autoencoder (AE) with a tailored Cluster Repelling Contrastive (CRC) loss function to generate distinctive representation from limited or incrementally incoming data in the online setting. Moreover, to reduce the burden of manual labeling, our online framework leverages pseudo-labels automatically generated from the decision-making process in the ADM to facilitate periodic updates of the ADM. The elimination of human intervention for labeling and decision-making boosts the system's compatibility and adaptability in the online setting to remain synchronized with dynamic environments. Experimental validation using the NSL-KDD and UNSW-NB15 datasets demonstrates the superior performance and adaptability of AOC-IDS, surpassing the state-of-the-art solutions. The code is released at https://github.com/xinchen930/AOC-IDS.
How to identify the comprehensive comparable performance of various Intrusion Detection (ID) algorithms which are based on the Model Checking (MC) techniques? To address this open issue, we conduct some tests for the model-checking-based intrusion detection systems (IDS) algorithms. At first, Linear Temporal Logic (LTL), Interval Temporal Logic (ITL) and Real-time Attack Signature Logic (RASL) are employed respectively to establish formula models for twenty-four types of attacks. And then, a standard intrusion set, called Intrusion Set for Intrusion Detection based on Model Checking (ISIDMC) i…
With the ubiquitous nature of information technology solutions that facilitate communication in the modern world, cyber attacks are increasing in volume and becoming more sophisticated in nature. From classic network-based Denial of Service (DoS) attacks to the more recent concerns of privacy compromises, Intrusion Detection Systems (IDS) are becoming an urgent need to safeguard the modern information technology landscape. The development of these IDS relies on training and evaluation datasets that must evolve with time and represent the contemporary threat landscape. The purpose of this analy…
Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly, intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under…
The objective of this is to develop a Fuzzy aided Application layer Semantic Intrusion Detection System (FASIDS) which works in the application layer of the network stack. FASIDS consist of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. For detecting such malicious activities, FASIDS is proposed in this paper. At application layer, HTTP traffic's header and payload are analyzed for possible intrusion. In the proposed misuse dete…
Intrusion detection systems have become a key component in ensuring the safety of systems and networks. As networks grow in size and speed continues to increase, it is crucial that efficient scalable techniques should be developed for IDS systems. Signature based detection is the most extensively used threat detection technique for Intrusion Detection Systems (IDS). One of the foremost challenges for signaturebased IDS systems is how to keep up with large volume of incoming traffic when each packet needs to be compared with every signature in the database. When an IDS cannot keep up with the t…
With the increasing number of intrusions in system and network infrastructures, Intrusion Detection Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of Evolutionary Algorithms in IDS has proved its maturity over the times. Although most of the research works have been based on the use of genetic algorithms in IDS, this paper presents an approach toward the generation of rules for the identification of anomalous connections using evolution strategies . The emphasis is given on how the problem can be modeled into…
Intrusion Detection Systems (IDSs) are a necessary cyber defense mechanism. Unfortunately, their capability has fallen behind that of attackers. This motivates us to improve our understanding of the root causes of their false-negatives. In this paper we make a first step towards the ultimate goal of drawing useful insights and principles that can guide the design of next-generation IDSs. Specifically, we propose a methodology for analyzing the root causes of IDS false-negatives and conduct a case study based on Snort and a real-world dataset of cyber attacks. The case study allows us to draw u…
Prevention of cyber attacks on the critical network resources has become an important issue as the traditional Intrusion Detection Systems (IDSs) are no longer effective due to the high volume of network traffic and the deceptive patterns of network usage employed by the attackers. Lack of sufficient amount of labeled observations for the training of IDSs makes the semi-supervised IDSs a preferred choice. We propose a semi-supervised IDS by extending a data analysis technique known as Logical Analysis of Data, or LAD in short, which was proposed as a supervised learning approach. LAD uses part…
Cyber threats are increasing not only in their volume but also in their sophistication and difficulty to detect. Attacks have become a national/global threat as they have targeted private and public, as well as government sectors over the years. This is a growing issue and organisations are taking steps to reduce, detect and prevent threats. To do this they need to use systems that are equipped with the capabilities to do either of those steps and develop them for the type of networks they use, for instance wired or wireless. One of these systems are Intrusion Detection Systems (IDS), which ca…
The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT …
Since it is impossible to predict and identify all the vulnerabilities of a network, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities for ensuring the security of a networked system. To be effective in carrying out their functions, the IDSs need to be accurate, adaptive, and extensible. Given these stringent requirements and the high level of vulnerabilities of the current days' networks, the design of an IDS has become a very challenging task. Although, an extensive research has been done on intrusion de…
This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. Similarly, a section surveying algorithmic development…
This paper presents a new masquerade attack called the cloaking attack and provides formal analyses for clock skew-based Intrusion Detection Systems (IDSs) that detect masquerade attacks in the Controller Area Network (CAN) in automobiles. In the cloaking attack, the adversary manipulates the message inter-transmission times of spoofed messages by adding delays so as to emulate a desired clock skew and avoid detection. In order to predict and characterize the impact of the cloaking attack in terms of the attack success probability on a given CAN bus and IDS, we develop formal models for two cl…
Network Intrusion Detection (NID) is the process of identifying network activity that can lead to the compromise of a security policy. In this paper, we will look at four intrusion detection approaches, which include ANN or Artificial Neural Network, SOM, Fuzzy Logic and SVM. ANN is one of the oldest systems that have been used for Intrusion Detection System (IDS), which presents supervised learning methods. However, in this research, we also came across SOM or Self Organizing Map, which is an ANN-based system, but applies unsupervised methods. Another approach is Fuzzy Logic (IDS-based), whic…
A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this …
Cybersecurity has become one of the focuses of organisations. The number of cyberattacks keeps increasing as Internet usage continues to grow. An intrusion detection system (IDS) is an alarm system that helps to detect cyberattacks. As new types of cyberattacks continue to emerge, researchers focus on developing machine learning (ML) based IDS to detect zero-day attacks. Researchers usually remove some or all attack samples from the training dataset and only include them in the testing dataset when evaluating the performance of an IDS on detecting zero-day attacks. Although this method may sho…
Attackers demonstrated the use of remote access to the in-vehicle network of connected vehicles to launch cyber-attacks and remotely take control of these vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques have been proposed for the detection of such attacks. The evaluation of some of these IDS demonstrated their efficacy in terms of accuracy in detecting message injections but was performed offline, which limits the confidence in their use for real-time protection scenarios. This paper evaluates four architecture designs for real-time IDS for connected vehicles usi…
Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. The procedure of implementing a IDS for Internet of Things (IoT) networks is not without challenges due to the variability of these systems and specifically the difficulty in accessing data. The specifics of these very constrained devices render the design of an IDS capable of dealing with the varied attacks a very challenging problem and a very active research subject. In the current state of literature, a number of approaches have been pr…
Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are classified, and the different algorithms used to detect anomalous activities. It attempts to compare the various methods of intrusion techniques. It also describes the various approaches and the importance of IDSs in information security.
Internet of Things (IoT) brings new challenges to the security solutions of computer networks. So far, intrusion detection system (IDS) is one of the effective security tools, but the vast amount of data that is generated by heterogeneous protocols and "things" alongside the constrained resources of the hosts, make some of the present IDS schemes defeated. To grant IDSs the ability of working in the IoT environments, in this paper, we propose a new distributed dimension reduction scheme which addresses the limited resources challenge. A novel autoencoder (AE) designed, and it learns to generat…
The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS technologies suffer from imbalanced and missing sample data, which makes the training of IDS …
Intrusion Detection System (IDS) is one of the security measures being used as an additional defence mechanism to prevent the security breaches on web. It has been well known methodology for detecting network-based attacks but still immature in the domain of securing web application. The objective of the paper is to thoroughly understand the design methodology of the detection system in respect to web applications. In this paper, we discuss several specific aspects of a web application in detail that makes challenging for a developer to build an efficient web IDS. The paper also provides a com…
Intrusion Detection Systems (IDS) have been the industry standard for securing IoT networks against known attacks. To increase the capability of an IDS, researchers proposed the concept of blockchain-based Collaborative-IDS (CIDS), wherein blockchain acts as a decentralised platform allowing collaboration between CIDS nodes to share intrusion related information, such as intrusion alarms and detection rules. However, proposals in blockchain-based CIDS overlook the importance of continuous evaluation of the trustworthiness of each node and generally work based on the assumption that the nodes a…
In recent years, wireless sensor network becomes popular both in civil and military jobs. However, security is one of the significant challenges for sensor network because of their deployment in open and unprotected environment. As cryptographic mechanism is not enough to protect sensor network from external attacks, intrusion detection system (IDS) needs to be introduced. In this paper we propose a policy based IDS for hierarchical architecture that fits the current demands and restrictions of wireless ad hoc sensor network. In this proposed IDS architecture we followed clustering mechanism t…
Intrusion detection systems (IDS) are used to monitor networks or systems for attack activity or policy violations. Such a system should be able to successfully identify anomalous deviations from normal traffic behavior. Here we discuss the machine learning approach to building an anomaly-based IDS using the CSE-CIC-IDS2018 dataset. Since the publication of this dataset a relatively large number of papers have been published, most of them presenting IDS architectures and results based on complex machine learning methods, like deep neural networks, gradient boosting classifiers, or hidden Marko…
This paper considers the use of novel technologies for mitigating attacks that aim at compromising intrusion detection systems (IDSs). Solutions based on collaborative intrusion detection networks (CIDNs) could increase the resilience against such attacks as they allow IDS nodes to gain knowledge from each other by sharing information. However, despite the vast research in this area, trust management issues still pose significant challenges and recent works investigate whether these could be addressed by relying on blockchain and related distributed ledger technologies. Towards that direction,…
Intrusion Detection Systems (IDSs) can analyze network traffic for signs of attacks and intrusions. However, encrypted communication limits their visibility and sophisticated attackers additionally try to evade their detection. To overcome these limitations, we extend the scope of Network IDSs (NIDSs) with additional data from the hosts. For that, we propose the integrated open-source zeek-osquery platform that combines the Zeek IDS with the osquery host monitor. Our platform can collect, process, and correlate host and network data at large scale, e.g., to attribute network flows to processes…
Collaborative intrusion detection networks are often used to gain better detection accuracy and cost efficiency as compared to a single host-based intrusion detection system (IDS). Through cooperation, it is possible for a local IDS to detect new attacks that may be known to other experienced acquaintances. In this paper, we present a sequential hypothesis testing method for feedback aggregation for each individual IDS in the net- work. Our simulation results corroborate our theoretical results and demonstrate the properties of cost efficiency and accuracy compared to other heuristic methods. …
Shanghai University（上海大学）；Xi’an Innovation College, Yan’an University（延安大学西安创新学院）；United Automotive Electronic Systems Co.（联合汽车电子有限公司）
As an active network security protection scheme, intrusion detection system (IDS) undertakes the important responsibility of detecting network attacks in the form of malicious network traffic. Intrusion detection technology is an important part of IDS. At present, many scholars have carried out extensive research on intrusion detection technology. However, developing an efficient intrusion detection method for massive network traffic data is still difficult. Since Generative Adversarial Networks (GANs) have powerful modeling capabilities for complex high-dimensional data, they provide new idea…
Wireless Sensor Network (WSN) consists of low cost sensor nodes which cannot afford to implement sophisticated security system in it. That is why intrusion detection architecture for WSN is considerably different and difficult to implement. Most of the current implementations are based on exchanging anomaly signals among the leaf level sensors resulting in too much power consumption. We propose a novel architecture for Intrusion Detection System (IDS) in WSN based on Hierarchical Overlay Design (HOD) that will distribute the overall responsibility of intrusion detection into entities and thus …
Henan University（河南大学）；National Institute of Technology Kurukshetra（库鲁克舍特拉国立技术学院）；Asia University（亚洲大学）；GuangZhou University（广州大学）
With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the p…
Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to survey the application of IDS to the Internet of Things (IoT) networks, where also the edge computing is used to support the IDS implementation. New c…
University of Strathclyde（斯特拉斯克莱德大学）；University of Abertay Dundee（阿伯泰邓迪大学）
Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their perf…
In this paper, we consider the applications of process mining in intrusion detection. We propose a novel process mining inspired algorithm to be used to preprocess data in intrusion detection systems (IDS). The algorithm is designed to process the network packet data and it works well in online mode for online intrusion detection. To test our algorithm, we used the CSE-CIC-IDS2018 dataset which contains several common attacks. The packet data was preprocessed with this algorithm and then fed into the detectors. We report on the experiments using the algorithm with different machine learning (M…
