Raspberry Pi-Based Intelligent Cyber Defense Systems for SMEs: An Exploratory Study

Tirumala, Sreenivas Sremath; Nepal, Narayan; Ray, Sayan Kumar

doi:10.1007/978-3-031-06371-8_1

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…A study conducted by Tirumala et al [26], explored that ransomware (malware) attacks have forced businesses to think about the security of their resources due to SMEs not having the right cyber defense mechanisms in place. This research explores implementing a "Raspberry Pi" based intelligent cyber defense system (iCDS) for SME networks and Smart-homes and how these devices are used to filter malicious contents from incoming traffic and be able to detect malware using AI.…”

Section: Malware Impacts On Smesmentioning

confidence: 99%

Detection and Minimization of Malware by Implementing AI in SMEs

Rawindaran¹,

Nawaf²,

Bentotahewa³

et al. 2023

Malware - Detection and Defense

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The malware can threaten personal privacy by opening backdoors for attackers to access user passwords, IP addresses, banking information, and other personal data, whilst some malware extracts personal data and sends them to people unknown to the users. In this chapter, the authors will present recent case studies and discuss the privacy and security threats associated with different types of malwares. The small medium enterprises (SMEs) have a unique working model forming the backbone of the UK economy and malware affects SMEs’ organizations. Also, the use of Artificial Intelligence (AI) as both an offense and defense mechanism, for the hacker, and the end user will be investigated further. In conclusion, finding a balance between IT expertise and the costs of products that are able to help SMEs protect and secure their data will benefit the SMEs by using a more intelligent controlled environment with applied machine learning techniques and not compromising on costs will be discussed.

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Section: Malware Impacts On Smesmentioning

confidence: 99%

Detection and Minimization of Malware by Implementing AI in SMEs

Rawindaran¹,

Nawaf²,

Bentotahewa³

et al. 2023

Malware - Detection and Defense

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“…Using some channel bonding technology, however, it is possible to channelize the network traffic to flow through two separate network interface connections, one for incoming traffic entering the Raspberry Pi device from external network and the other for outgoing traffic from the Raspberry Pi device [42]. This will need two network interface connections (e.g., network adaptors or network interface cards) in the Raspberry Pi 4.0 board and such connections can be in any form, like, the on-board Ethernet interface, onboard WiFi interface, USB Ethernet, and USB WiFi.…”

Section: Use Of Raspberry Pi As An Icdsmentioning

confidence: 99%

Raspberry Pi-based Intelligent Cyber Defense Systems for SMEs and Smart-homes: An Exploratory Study

Tirumala

Nepal²,

Ray

2022

EAI Endorsed Trans Smart Cities

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Ongoing ransomware attacks have forced business to think about security of their resources. Recently, small- to-medium enterprises (SMEs) and Smart-homes have become easy targets for attackers since they don’t have cyber defense mechanism in place other than simple firewall systems which are quite vulnerable. Cyber defense systems are costly and often not within the budget of SMEs or families which inspired to think about low cost yet highly efficient cyber defense solutions. Regular individuals and families who use internet for day to day use often end-up becoming a possible resource for using them as Trojan or bitcoin nodes. This research explores the prospects of implementing a Raspberry Pi (Raspberry Pi)-based intelligent cyber- defense system (iCDS) for SME networks and Smart-homes to filter malicious contents from incoming traffic and detect malware using artificial intelligence. Primarily, the work presented in this paper tries to evaluate the hardware capability of network interfaces (both internal, and attached) of Raspberry Pi for handle high volumes of incoming traffic. For this, we measure the network performance of the Raspberry Pi using the speed test software and try to explore the possibility of a light weight machine learning (ML) based malware detection. The results show that the built in Ethernet interface outperforms the built in WiFi and external attached USB to Ethernet Adapter in terms of latency, download and upload throughput. Also, a new DNA based ML approach was successfully able to produce over 19.5% better accuracy rates of over classifier trained with hash-sequence. The experiment results further emphasise on the importance of generating complex malware signatures with variety to face existing threats which has taken a new form due to increase in malware based attacks, particularly for ransomware. The complexity of the generated malware is based on generic yet strong encryption principles which produced good results which is quite encouraging at this stage.

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