The security of RFID readers with IDS/IPS solution using Raspberry Pi

Zitta, Tomas; Neruda, Marek; Vojtěch, Lukáš

doi:10.1109/carpathiancc.2017.7970418

Cited by 15 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another detection system, Suricata [8], is a popular public IDS, fully supports multithreading architecture, and is more suitable for large-scale network systems. The study [9] used the Suricata to implement the detection system on the resource constraint device, Raspberry Pi. They aim to detect the port scanning attack on the IoT environment.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Soe

Feng

Santosa

et al. 2020

Sensors

122

View full text Add to dashboard Cite

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Soe

Feng

Santosa

et al. 2020

Sensors

122

View full text Add to dashboard Cite

show abstract

“…Misuse-based detection systems are implemented using predefined attack signatures. Thus, they cannot detect new kinds of attacks and new variants of known attacks [5]. Although anomaly detection systems may be effective for new kinds of attacks, the problem of the high false positive rate is one of the main challenges [6,7].…”

Section: Intrusion Detection System (Ids)mentioning

confidence: 99%

“…The IDS studies [5,[19][20][21][22][23][24][25][26][27] also have proposed attack detection based on Raspberry Pi. In the work of [18], the anomaly-based detection method was proposed by capturing the previous traffic patterns asbenign data for attack detection system construction.…”

Section: Raspberry Pi-based Idsmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

et al. 2020

View full text Add to dashboard Cite

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

show abstract

“…In an effort to secure Electronic Health Records (EHR), Zitta et al [46] employed smart contracts on an Ethereum blockchain to develop intelligent EHR that are stored in individual nodes. Garman et al [47] proposed an anonymous credential authentication scheme that does not require a trusted credential issuer rather uses a public append-only ledger (blockchain).…”

Section: A Blockchain-based Security and Privacy Preservation Schemementioning

confidence: 99%

Applying Diffie-Hellman Algorithm to Solve the Key Agreement Problem in Mobile Blockchain-based Sensing Applications

Owoh¹,

Mahinderjit²

2019

ijacsa

View full text Add to dashboard Cite

Mobile blockchain has achieved huge success with the integration of edge computing services. This concept, when applied in mobile crowd sensing, enables transfer of sensor data from blockchain clients to edge nodes. Edge nodes perform proof-of-work on sensor data from blockchain clients and append validated data to the chain. With this approach, blockchain can be performed pervasively. However, securing sensitive sensor data in a mobile blockchain (client/edge node architecture) becomes imperative. To this end, this paper proposes an integrated framework for mobile blockchain which ensures key agreement between clients and edge nodes using Elliptic Curve Diffie-Hellman algorithm. Also, the framework provides efficient encryption of sensor data using the Advanced Encryption Standard algorithm. Finally, key agreement processes in the framework were analyzed and results show that key pairing between the blockchain client and the edge node is a non-trivial process.

show abstract

The security of RFID readers with IDS/IPS solution using Raspberry Pi

Cited by 15 publications

References 10 publications

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Applying Diffie-Hellman Algorithm to Solve the Key Agreement Problem in Mobile Blockchain-based Sensing Applications

Contact Info

Product

Resources

About