The Multi-Vector Portable Intrusion Detection System (MVP-IDS): A hybrid approach to intrusion detection for portable information devices

Moyers, Benjamin R.; Dunning, John P.; Marchany, Randy; Tront, Joseph G.

doi:10.1109/icwits.2010.5612449

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…In [36], the authors present an anomaly host-based intrusion detection system (HIDS) which detects battery exhaustion attacks (a type of DoS) which targets one process, an attack particularly relevant to IoT devices due to their constrained power source but specific for mobile devices such as laptops. Whilst a similar approach is seen in [47], both of these methods use anomaly profiling as a HIDS. Although able to detect a specific attack, the validity of the results is limited due to potential subversion of the devices.…”

Section: A Centralisedmentioning

confidence: 99%

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

Benkhelifa

Welsh

Hamouda

2018

IEEE Commun. Surv. Tutorials

173

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The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current Intrusion Detection Systems (IDS) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimised solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected.

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Section: A Centralisedmentioning

confidence: 99%

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

Benkhelifa

Welsh

Hamouda

2018

IEEE Commun. Surv. Tutorials

173

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“…Their modular architecture, leveraging software-defined networking, was validated through real DDoS attack traces, demonstrating scalability and efficiency. Moyers et al [ 18 ] introduced the Multi-Vector Portable Intrusion Detection System (MVP-IDS), extending the Battery-Sensing Intrusion Protection System (B-SIPS). The study illustrated how combining a low-overhead tripwire with advanced detection mechanisms proved effective in safeguarding limited-resource wireless information technology devices.…”

Section: Related Workmentioning

confidence: 99%

Evaluating modern intrusion detection methods in the face of Gen V multi-vector attacks with fuzzy AHP-TOPSIS

Alhakami

2024

PLoS ONE

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The persistent evolution of cyber threats has given rise to Gen V Multi-Vector Attacks, complex and sophisticated strategies that challenge traditional security measures. This research provides a complete investigation of recent intrusion detection systems designed to mitigate the consequences of Gen V Multi-Vector Attacks. Using the Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we evaluate the efficacy of several different intrusion detection techniques in adjusting to the dynamic nature of sophisticated cyber threats. The study offers an integrated analysis, taking into account criteria such as detection accuracy, adaptability, scalability, resource effect, response time, and automation. Fuzzy AHP is employed to establish priority weights for each factor, reflecting the nuanced nature of security assessments. Subsequently, TOPSIS is employed to rank the intrusion detection methods based on their overall performance. Our findings highlight the importance of behavioral analysis, threat intelligence integration, and dynamic threat modeling in enhancing detection accuracy and adaptability. Furthermore, considerations of resource impact, scalability, and efficient response mechanisms are crucial for sustaining effective defense against Gen V Multi-Vector Attacks. The integrated approach of Fuzzy AHP and TOPSIS presents a strong and adaptable strategy for decision-makers to manage the difficulties of evaluating intrusion detection techniques. This study adds to the ongoing discussion about cybersecurity by providing insights on the positive and negative aspects of existing intrusion detection systems in the context of developing cyber threats. The findings help organizations choose and execute intrusion detection technologies that are not only effective against existing attacks, but also adaptive to future concerns provided by Gen V Multi-Vector Attacks.

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“…Moyers, et al [73] studied Multi-Vector Portable IDS (MVP-IDS) which is targeted for WPAN applications. The authors use a combined anomaly and signature-based detection technique, a combined behavior (battery current, specifically) and traffic-based collection approach and pattern matching analysis.…”

Section: Anomaly + Signature Based Designsmentioning

confidence: 99%

“…While Moyers, et al did claim one MVP-IDS module [Bluetooth Attack Detection and Signature System (BADSS)] achieved a 100% detection rate with only a 2.97% false positive rate, the authors did not provide numerical results on key metrics for the complete system. [72] anomaly traffic yes data mining MVP-IDS [73] anomaly + signature behavior + traffic pattern matching Bluetooth IDS [74] signature traffic pattern matching…”

Section: Anomaly + Signature Based Designsmentioning

confidence: 99%

A survey of intrusion detection in wireless network applications

Mitchell

Chen

2014

Computer Communications

182

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Information systems are becoming more integrated into our lives. As this integration deepens, the importance of securing these systems increases. Because of lower installation and maintenance costs, many of these systems are largely networked by wireless means. In order to identify gaps and propose research directions in wireless network intrusion detection research, we survey the literature of this area. Our approach is to classify existing contemporary wireless intrusion detection system (IDS) techniques based on target wireless network, detection technique, collection process, trust model and analysis technique. We summarize pros and cons of the same or different types of concerns and considerations for wireless intrusion detection with respect to specific attributes of target wireless networks including wireless local area networks (WLANs), wireless personal area networks (WPANs), wireless sensor networks (WSNs), ad hoc networks, mobile telephony, wireless mesh networks (WMNs) and cyber physical systems (CPSs). Next, we summarize the most and least studied wireless IDS techniques in the literature, identify research gaps, and analyze the rationale for the degree of their treatment. Finally, we identify worthy but little explored topics and provide suggestions for ways to conduct research.

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The Multi-Vector Portable Intrusion Detection System (MVP-IDS): A hybrid approach to intrusion detection for portable information devices

Cited by 3 publications

References 2 publications

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

Evaluating modern intrusion detection methods in the face of Gen V multi-vector attacks with fuzzy AHP-TOPSIS

A survey of intrusion detection in wireless network applications

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