SH-IDS: Specification Heuristics Based Intrusion Detection System for IoT Networks

Babu, M. Jagadeesh; Reddy, A. Raji

doi:10.1007/s11277-020-07137-0

Cited by 19 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another blockchain based secure data sharing mechanism was proposed by Veeramakali et al, 17 but here again the overall computational complexity of the system has to be reduced. A Specification based Intrusion Detection was proposed by Jagadeesh Babu et al, 18 which relies on a supervised learning approach to discover various transaction specifications for normal and abnormal behavior. The discovered specifications are then further optimized using the proposed heuristics approach, which results in a system capable of identifying normal and abnormal (malicious) behavior depending on the specifications of the network transactions performed by various IoT devices.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Ahmad

Shah

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary The Internet of Things (IoT) has altered living by controlling devices/things over the Internet. IoT has specified many smart solutions for daily problems, transforming cyber‐physical systems (CPS) and other classical fields into smart regions. Most of the edge devices that make up the Internet of Things have very minimal processing power. To bring down the IoT network, attackers can utilize these devices to conduct a variety of network attacks. In addition, as more and more IoT devices are added, the potential for new and unknown threats grows exponentially. For this reason, an intelligent security framework for IoT networks must be developed that can identify such threats. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabeled dataset. The system‐generated labeled dataset is used to train a deep learning model to detect IoT network attacks. Additionally, the research presents a feature selection mechanism for identifying the most relevant aspects in the dataset for detecting attacks. The study shows that the suggested model is able to identify the unlabeled IoT network datasets and DBN (Deep Belief Network) outperform the other models with a detection accuracy of 97.5% and a false alarm rate of 2.3% when trained using labeled dataset supplied by the proposed approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Ahmad

Shah

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…A specification heuristic intrusion detection system for the IoT environment is proposed in [33]. The proposed system is based on discovering a unique n-gram pattern for sequential attributes values.…”

Section: Related Workmentioning

confidence: 99%

A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer

Alazzam

AbuAlghanam

Al-zoubi

et al. 2022

Cybernetics and Information Technologies

View full text Add to dashboard Cite

The Internet of Things (IoT) is widespread in our lives these days (e.g., Smart homes, smart cities, etc.). Despite its significant role in providing automatic real-time services to users, these devices are highly vulnerable due to their design simplicity and limitations regarding power, CPU, and memory. Tracing network traffic and investigating its behavior helps in building a digital forensics framework to secure IoT networks. This paper proposes a new Network Digital Forensics approach called (NDF IoT). The proposed approach uses the Owl optimizer for selecting the best subset of features that help in identifying suspicious behavior in such environments. The NDF IoT approach is evaluated using the Bot IoT UNSW dataset in terms of detection rate, false alarms, accuracy, and f-score. The approach being proposed has achieved 100% detection rate and 99.3% f-score and outperforms related works that used the same dataset while reducing the number of features to three features only.

show abstract

“…Whereas, if the movement is much quicker, there is a sudden spurt or drop in the performance, based on the assessed metrics. As a result, the process of authentic decisions on detections, as well as prevention approaches as needed, shall be introduced to the system, which can aid in the overall process improvement [17].…”

Section: Attack Modelmentioning

confidence: 99%

“…Based on the pivot levels drawn for the model, the initial, final, max, and min movement for the respective metric are estimated as per the timeframe considered. If the movement for the metric is within the range of min R to min S , the request load seems to be normal in function, and if there is any significant variations wherein the metric touches max R or max S , then the flow of requests said to be prone to denial of service attack, which tends to alert the administration team for further decisive actions [17].…”

Section: Detection and Defense Modelmentioning

confidence: 99%

“…For the proposed model, the moving average is considered for 20 period, as the 21 is a Fibonacci number, wherein the movements can be expected to have retracements. Detailed brief of the Fibonacci ratios and numbers can be reviewed from [17]. Depending on the load factor, and the customization essential, the moving average periods can be altered to the application as required.…”

Section: Simple Moving Average (Sma)mentioning

confidence: 99%

See 1 more Smart Citation

Central Pivot Heuristics for Botnet Attack Defense in Iot

Swathi

Kumar²,

Kumar

2022

IJRITCC

View full text Add to dashboard Cite

Botnet assaults on IoT systems have become a big issue, and several strategies for botnet protection have been investigated by the academic and industry communities. While many of these methods are practical and effective for botnet attack prevention, one of the important limits is the load factor on the servers that manage monitoring and control in addition to catering to client system requests. To address load factor difficulties, the focus of this study report is on the conditions of installing a four-layer security control system based on the notion of central pivot points. Inspired by the effective and systematic Markov Chains concept, this publication proposes a four-layer filtering model that shows if botnet detection and prevention methods for servers are required. The model's simulated experimental study demonstrates the potential scope of deploying the system. The study also highlights the future possibilities of model improvisation that can reduce any erroneous signal production that is judged necessary.

show abstract

SH-IDS: Specification Heuristics Based Intrusion Detection System for IoT Networks

Cited by 19 publications

References 27 publications

Unsupervised ensemble based deep learning approach for attack detection in IoT network

Unsupervised ensemble based deep learning approach for attack detection in IoT network

A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer

Central Pivot Heuristics for Botnet Attack Defense in Iot

Contact Info

Product

Resources

About