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Register a new userA Review of Intrusion Detection Systems and Their Evaluation in the IoT
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Added by
Luca Arnaboldi `
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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 proposed to improve the efficiency of intrusion detection, catering to some of these limitations, such as resource constraints and mobility. In this article, we review works on IDS specifically for these kinds of devices from 2008 to 2018, collecting a total of 51 different IDS papers. We summarise the current themes of the field, summarise the techniques employed to train and deploy the IDSs and provide a qualitative evaluations of these approaches. While these works provide valuable insights and solutions for sub-parts of these constraints, we discuss the limitations of these solutions as a whole, in particular what kinds of attacks these approaches struggle to detect and the setup limitations that are unique to this kind of system. We find that although several paper claim novelty of their approach little inter paper comparisons have been made, that there is a dire need for sharing of datasets and almost no shared code repositories, consequently raising the need for a thorough comparative evaluation.
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Internet of Things (IoT) is a disruptive technology with applications across diverse domains such as transportation and logistics systems, smart grids, smart homes, connected vehicles, and smart cities. Alongside the growth of these infrastructures, the volume and variety of attacks on these infrastructures has increased highlighting the significance of distinct protection mechanisms. Intrusion detection is one of the distinguished protection mechanisms with notable recent efforts made to establish effective intrusion detection for IoT and IoV. However, unique characteristics of such infrastructures including battery power, bandwidth and processors overheads, and the network dynamics can influence the operation of an intrusion detection system. This paper presents a comprehensive study of existing intrusion detection systems for IoT systems including emerging systems such as Internet of Vehicles (IoV). The paper analyzes existing systems in three aspects: computational overhead, energy consumption and privacy implications. Based on a rigorous analysis of the existing intrusion detection approaches, the paper also identifies open challenges for an effective and collaborative design of intrusion detection system for resource-constrained IoT system in general and its applications such as IoV. These efforts are envisaged to highlight state of the art with respect to intrusion detection for IoT and open challenges requiring specific efforts to achieve efficient intrusion detection within these systems.
Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance. In this paper, we conduct a systematic literature review (SLR) of data mining techniques (DMT) used in IDS-based solutions through the period 2013-2018. We employed criterion-based, purposive sampling identifying 32 articles, which constitute the primary source of the present survey. After a careful investigation of these articles, we identified 17 separate DMTs deployed in an IDS context. This paper also presents the merits and disadvantages of the various works of current research that implemented DMTs and distributed streaming frameworks (DSF) to detect and/or prevent malicious attacks in a big data environment.
Neural networks are increasingly used in security applications for intrusion detection on industrial control systems. In this work we examine two areas that must be considered for their effective use. Firstly, is their vulnerability to adversarial attacks when used in a time series setting. Secondly, is potential over-estimation of performance arising from data leakage artefacts. To investigate these areas we implement a long short-term memory (LSTM) based intrusion detection system (IDS) which effectively detects cyber-physical attacks on a water treatment testbed representing a strong baseline IDS. For investigating adversarial attacks we model two different white box attackers. The first attacker is able to manipulate sensor readings on a subset of the Secure Water Treatment (SWaT) system. By creating a stream of adversarial data the attacker is able to hide the cyber-physical attacks from the IDS. For the cyber-physical attacks which are detected by the IDS, the attacker required on average 2.48 out of 12 total sensors to be compromised for the cyber-physical attacks to be hidden from the IDS. The second attacker model we explore is an $L_{infty}$ bounded attacker who can send fake readings to the IDS, but to remain imperceptible, limits their perturbations to the smallest $L_{infty}$ value needed. Additionally, we examine data leakage problems arising from tuning for $F_1$ score on the whole SWaT attack set and propose a method to tune detection parameters that does not utilise any attack data. If attack after-effects are accounted for then our new parameter tuning method achieved an $F_1$ score of 0.811$pm$0.0103.
In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.
The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.
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