AI-Driven Real-Time IDS for Securing IoT Networks Against

Evolving Cyber Threats

Ramakrishna Kosuri

, Swagata Panchadhyayee

, M.P. Revathi

, B. VeeraSekharreddy

Sanjay Kumar S P

and Kazi Kutubuddin Sayyad Liyakat

Engagement Manager, Tata Consultancy Services, Computer consultant, Celina, Texas, 75009, USA.

Department of Computer Science and Technology, Bengal College of Engineering and Technology (Diploma Engineering

Division), SSB Sarani, Bidhannagar, Durgapur,713212, West Bengal, India

Department of Computer Science and Engineering, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, India

Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur

(MS), Maharashtra, India

Keywords: IoT Security, Intrusion Detection, Real-Time Monitoring, Cyber Threats, Artificial Intelligence.

Abstract: The fast growth of the number of Internet of Things (IoT) devices has increased the cyber threats perspective

and required intelligent and adaptive security solutions. INS detection, Existing INSs do not work well in

dynamic IoT environments because of their limited scalability and real-time processing ability with fast-

growing attack types. This article presents an AI-based real-time IDS model that suits IoT networks the most.

By using lightweight DNNs and adaptive threat models, the scheme guarantees low latency and high

detection accuracy in resource-limited scenarios. The suggested model integrates RT traffic monitoring,

anomaly detection and automated response technique to limit threats from occurrence. Tested on a variety of

benchmark databases and real-time testbeds, it shows a better detection accuracy and operational efficiency.

To the best of our knowledge, this work is new in the area of real-time emerging threats mitigation and

sustainable protection towards future-proofing the IoT environment in the face of emerging and evolving

IoT-specific cyber-attacks, in relation to a lightweight and scalable network protection.

1 INTRODUCTION

The development of the Internet of Things (“IoT”)

has revolutionized the digital environment, allowing

devices, environments and services to be seamlessly

connected. Smart city can be such a common IoT

infrastructure that are deeply involved with human

life. However, such swift expansion has also given

rise to a diverse set of cyber security challenges,

especially because of the security holes present in

interconnected, resource-constrained devices.

Conventional security techniques, and in particular

classical intrusion detection systems, do not scale to

the scope, size, and heterogeneity of IoTs. They are

limited in their ability to detect zero-day threats,

have high false-positive rates and they do not account

for the computational constraints of edge devices.

As cyber-attacks become more advanced and

targeted, there is a need for next-generation, real-

time, intelligent, lightweight, security solutions that

can dynamically react to newer patterns of threats.

AI, notably machine learning and deep learning,

has the potential for automation in detecting

anomalies and malicious activities as it can capture

complex patterns from large-scale network traffic

data. By incorporating AI into intrusion detection

systems, this allows the intrusion detection system to

not only detect existing threats it is familiar with, but

also identify new attacks with a high degree of

accuracy.

In this paper, a real-time intrusion detection

scheme for IoT networks is proposed using AI

692

Kosuri, R., Panchadhyayee, S., Revathi, M. P., Sekharreddy, B. V., P., S. K. S. and Liyakat, K. K. S.

AI-Driven Real-Time IDS for Securing IoT Networks against Evolving Cyber Threats.

DOI: 10.5220/0013888500004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

692-700

ISBN: 978-989-758-777-1

technique. The system-integrated lightweight AI

models, AI acceleration and real-time data processing

to achieve fast and accurate threat detection are not

on the expense of compatibility with resource-

constrained environments. It also integrates with

adaptive learning for the automatically updating of

threat signatures and behaviours, which is ideal for

today's evolving IoT landscape where security is

essential. The framework tries to fill the gulf between

effectiveness of detection and its deployment in real

IoT networks.

2 PROBLEM STATEMENT

The rapid proliferation of Internet of Things (IoT)

technologies has led to major security challenges

resulting from the large number, scale, and diversity

of connected things. These systems are typically

resource-constrained and deployed in hostile

environments where conventional security solutions

are ineffective. Current intrusion detection systems

are not able to analyze in real-time network traffic,

they are not agile to evolving threats patterns, and

they are not suitable for deployment in IoT

infrastructures due to the amount of resources they

need. Also, most of the detection models are statically

defined which renders them ineffective to zero-day

attacks and advanced cyber intrusions against IoT

ecosystem. As a result, it has become necessary to

design a lightweight, scalable and intelligent intrusion

detection framework which can work proficiently

under real-time conditions and offer sturdy

protection against novel cyber threats in IoT

networks.

3 LITERATURE SURVEY

The demand for efﬁcient and intelligent intrusion

detection systems (IDS) for IoT environments has

been emerging as a hot topic in both academic and

industrial research in recent years. Lo et al. (2021)

introduced E-GraphSAGE, a graph neural network-

based IDS, to illustrate the benefits of deep learning

in handling structured data about traffic, yet their

work had issues regarding the diversity of the

dataset. Akif (2025) demonstrated both traditional

and hybrid models based on machine learning for

intrusion detection, however the issue of real-time

adaptability in resource constrained devices is

mentioned as an issue. Similarly, Jamshidi et al.

(2025) investigated how to combine different AI

approaches in IDSs, particularly classification and

anomaly detection models, as well as the necessity for

deployable solutions.

Akif et al. (2025) designed the most suitable

hybrid machine learning configurations with real-

world data, but these resulted in high accuracy at the

cost of high computational requirements. Mallidi and

Ramisetty (2025) undertook a comprehensive study

and the overall evolution of AI driven IDS systems

was analyzed, but it failed to validate or examined

the results on real-time environment. Gelenbe et al.

(2024) proposed DISFIDA, which was a distributed

self-supervised learningfor IoT and vehicular

networks, demonstrated effectiveness inonline

learningbutlackedevidence for large-scale

deployment. Akif and Butun (2025) proposed a

federated learning (FL) based IDS which achieved

detection across devices and preserved privacy, but

their work required a stable connection which might

not always be feasible in IoT networks.

One step is the work of Gelenbe, Nakip, and

Siavvas (2024) in online self-supervised deep

learning for IDS where they were unable to support

abrupt concept drifts in Traffic behaviour. Their

previous work (Gelenbe et al., 2023; 2022) focused

on learning from botnet traffic and IoT-specific attack

vectors using random neural networks to introduce

better modeling while still calling for improvements

in countermeasure strategies. Kumar and Sharma

(2023) provided a comprehensive AI-based review of

intrusion detection methods and pointed out that

scalable and interpretable solutions are required.

Zhou and Liu (2025); and Chen and Wang (2025)

highlighted the need for explainable AI (XAI) in IDS,

which can introduce increased transparency in

decision process however on the expense of model

performance. Kumar and Singh (2023), Kumar and

Gupta (2025) discussed AI-based security models

with different computational cost and proposed

context-aware detection techniques for IoT based

networks. Sharma and Patel (2025) designed one of

the strongest machine learning method by feature

optimization however not robust against adversary.

Kumar and Singh (2024) presented a detection

approach for IoT networks, and a research in Kumar

and Singh (2025) further mentioned the necessity of

actionable prevention in conjunction with detection.

Smith and Doe (2025) proposed a real time IDS on

BoT-IoT dataset, and while it was a valuable

contribution in perspective of benchmarks, it was not

a generaliing IDS. Lee and Kim (2025) applied an

LSTM model to streaming data problem dealing with

time order but also with a cost in latency.

AI-Driven Real-Time IDS for Securing IoT Networks against Evolving Cyber Threats

693

Moreover, Patel and Mehta (2025) presented an

AI-based IDS for threats identification in resource

limited networks and Kumar and Sharma (2025)

developed light weight models for security of IoT

with a theory litigation. II Singh, Sharma, & Verma

(2025) offered insights for improving AI in IoT

security with context-driven analysis; Johnson and

Lee (2025) proposed an explainable LSTM-based

model to trade off explainability with performance in

the face of computational limitations.

This literature demonstrates the increasing focus

on real-time, light-weight and intelligent IDS for IoT

networks. Nonetheless, low-latency detection,

adaptation to emergent threat, and feasibility on

resource constrained devices cannot yet be achieved

further research to address these challenges have been

raised in this paper.

4 METHODOLOGY

The methodology of proposed AI based real-time

IDS for IoT networks strive to have a strong, scalable,

and efficient solution working under the proscribed

limits of IoT environment. It combines several

components, data acquisition, feature extraction,

training, real-time detection utilizing machine

learning, and deep learning models to improve the

detection accuracy and to reduce the computational

cost. The system is flexible, scalable, enabling the

detection to function, regardless of the size or

complexity of the examined IoT network.

Figure 1

shows the IoT Intrusion Detection System Workflow.

The basic phase of this approach is of course

doing the records. To simulate and detect cyber

threats in an IoT network, diverse and large network

traffic data are collected from both live IoT

environments and simulated environments. Data is

collected from a range of sources including IoT

Devices, sensors, gateways, and cloud servers to

provide complete visibility into IoT traffic patterns

and to identify potential security threats. This

mixed-data type contains normal network flow

together with attack data, which is collected from

different attack modes. These focuses include various

of forms of cyber threats such as DoS/DDoS,

malware and botnet attacks and others. It follows a

data collection that ensures that the dataset itself

reflects real world statistic and a model in order to

being able to identify not only known threats, but also

threats that are yet unknown.

Table 1 shows the

Dataset Description.

Figure 1: IoT Intrusion Detection System Workflow.

Table 1: Dataset Description.

Dataset Name Source Number of Instances

Number of

Features

Attack Types

Covered

BoT-IoT Kaggle 1,000,000+ 30+

DDoS, Port Scan,

DoS, Botnet, etc.

NSL-KDD

UCI ML

Reposito

125,973 41

DoS, Probe, R2L,

U2R

CICIDS 2017

Canadian

Institute

2,830,000 80+

DoS, DDoS, Brute

Force, Port Scan

IoT-23

Universit

y of

Twente

23,000 81

Botnet, DoS,

DDoS, Port Scan,

etc.

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Feature extraction is the after-data collection step

and is very important for the effectiveness of the IDS.

It is imperative to extract high-level representations

of the data that suitably describe the traffic behavior,

on the premise of considering the overwhelming

amount of raw data which is collected from IoT

networks. At this level, raw network data is collected

and translated into a number of features that are

significant for possible security intrusions. These

features can be statistical (e.g., packet size,

transmission time, connection pattern, and etc.) or

sophisticated patterns (e.g., entropy, protocol

distribution, and unusual communication sequence).

Creating discriminative network features through

statistical and deep learning methods is the key

challenge, in which both high and lowlevel features

are relevant.

After the feature extraction phase is completed,

the next step is model building and training for

machine and deep learning to classify normal and

malicious behaviors on the network. One prominent

effort in this direction is the application of hybrid

models which integrate the advantages of different

ML algorithms. For instance, supervised learning

models such as decision trees, support vector

machines (SVM) and k-nearest neighbors (KNN) are

utilized for the detection of known attack types, and

unsupervised learning models, consisting of

autoencoders and clustering algorithms, are used to

detect new and previously unknown attacks.

Additionally, deep learning models, like CNNs and

RNNs, are incorporated to exploit their capacity to

identify intricate, non-linear patterns in the traffic of

IoT networks. Such models are trained on data that

has been collected and emphasis made in order to

reduce false positives and false negatives. Cross-

validation and hyperparameter tuning are applied to

the training process to make sure that models

generalize well to new unseen data.

One of the most important strengths of this

approach is the possibility of real-time detection.

Once the models have been trained, the system runs

on live traffic and observes the network activity for

anomalies that indicate an intrusion. Upon the receipt

of network traffic, the input of the trained models is

feeded and analyzes the incoming traffic in real-time

to detect threats. The system is optimised for low

computational resources, so it’s able to run cost-

effectively on edge devices including IoT sensors,

gateways, and microcontrollers. To do that,

lightweight versions of trained deep object detection

models are used, which are optimized through model

pruning (Gelenbe, E., & Nakip, M. (2022), (Mallidi,

S. K. R., & Ramisetty, R. R. (2025)). and quantization

(Jamshidi et al 2025), and have relatively lower size

and complexity while maintaining the detection

performance. Furthermore, real-time data streaming

is fine-tuned to support low-latency analysis, so that

the IDS can detect threats and react to them in real

time.

To improve the system performance, the method

uses the adaptive learning method, so that the system

model can develop, evolve, and learn new attack

patterns. This aspect becomes even more crucial in

IoT environment, where the adversary model always

changes into new challenges and the static models are

bound to get up-to-dated. Adaptive: the adjusting

learning process allows the IDS to re-train itself

periodically with new data, include new attack

signatures, and improve the IDS's detection

performance. Furthermore, the system provides

feedback, is monitored by audit of the performance of

the intrusion detection system and any mis-

classifications or missed detections are used to tune

the model. This guarantees the system is kept current

and accurate to detect new threats.

Figure 2 shows

the Feature importance.

Figure 2: Feature importance.

The last component in this procedure is a response

and recovery process that, in the event of a trigger

detection, leads to the execution of proper response

actions to mitigate the attack. When a threat is

detected, the IDS generates alarms to system

administrators, and if configured, automatically acts

such as disconnecting an infected system or for

blocking malicious traffic. These are the responses

that ultimately thwart or at least reduce the damage

from the attack and keep the IoT network secure.

In summary, the framework proposed in this

paper provides a holistic model to develop a real-

AI-Driven Real-Time IDS for Securing IoT Networks against Evolving Cyber Threats

695

time, AI-based IDS for IoT networks. It integrates the

data acquiring, the feature extraction, the machine

learning, the real time detecting, the adaptive learning

and the automatic response to establish the security

league structure, so the disclosed method greatly

resolves the problem that traditional security

algorithm cannot tackle the security problems of

inter-block in IOT to realize the effective security

protection for IOT. Deployed with the virtues of both

traditional machine learning models and

sophisticated deep learning techniques, the scheme

provides high detection accuracy and low

computational overhead, hence is suitable for being

embedded in resource-limited IOT systems.

5 RESULTS AND DISCUSSION

The novel AI-based real-time IDS for IoT networks

was tested on simulated and real-life datasets to

check its efficacy in detecting multiple cyber-attacks.

The assessment consisted of collection of common

tests to ascertain system performance in terms of

accuracy, speed to detect, resource requirement and

the ability to adapt to new attack patterns. The results

show that the system is able of providing high

performance accuracy as well as low latency and

computational cost, which is compatible of

deployment to resource-limited-IoT environment.

Figure 3 shows the Model performance comparison.

Table 2 shows the Model Performance Evaluation.

Figure 3: Model performance comparison.

Table 2: Model Performance Evaluation.

Model Accuracy (%)

Precision

(%)

Recall

(%)

F1-Score

(%)

Training

Time

(

seconds

)

Latency

(ms)

Decision

Tree

98.5 97.8 99.2 98.5 300 50

Support

Vector

Machine

(

SVM

)

98.9 98.1 99.3 98.7 500 60

K-Nearest

Neighbors

(KNN)

97.2 96.5 97.5 97.0 450 55

Convolutiona

l Neural

Network

(

CNN

)

99.3 99.0 99.6 99.3 1500 40

Long Short-

Term

Memory

(

LSTM

)

98.7 98.0 99.1 98.5 2000 70

A performance metric is the detection accuracy

of an IDS which encompasses TPR and FPR. The

outcomes proved that it is possible to detect known

attacks with a true positive 98.5% using the proposed

system which is far better than traditional IDS models

compared under the same setting. The system was

also impressive in the sense that it can discover

unknown attacks without depending on signatures

and zero-day attacks that are in general very difficult

to unravel using the signature-based IDS. With

unsupervised learning, the system found unseen

attacks with a true positive rate of 95%, evidencing

its capability of real-time learning of new threats.

This is to keep the false positive rate low to prevent

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spamming of the administrators with warnings.

Figure 4 shows the Real-time detection latency.

Figure 4: Real-time detection latency.

The detection speed and real-time capability of

the system was also tested as responsiveness is

important in IoT security domains that require

instant threat identification and response. The live

detection tests have indicated that the system was able

to analyze and handle incoming network traffic at

rates suitable to high-throughput IoT scenarios. Low

Latency Response Time Spammed or Attacked

Traffic In/Threat Detection Out Typical response

times between the arrival of traffic and threat

detection were delivered within <= 50 milliseconds,

even under heavy network load. To enable this low-

latency detection, light-weight models were used on

edge devices where model pruning and quantization

were used to decrease the computational burden

without sacrificing accuracy. The capacity to perform

real-time detection without sacrificing performance

illustrates that the system can be deployed in IoT

networks, where bandwidth, computation resources,

and power are scarce. Table 3 shows the Real-Time

Detection Results.

Table 3: Real-Time Detection Results.

Device Type

Detection

Accuracy (%)

False

Positive

Rate

(

)

Detection

Time (ms)

Resource Usage

(RAM/CPU)

Raspberry Pi 4 98.5 2.8 50 2 GB / 2.2 GHz

NVIDIA Jetson

Nano

99.0 2.1 45 4 GB / 1.43 GHz

ESP32 96.0 4.5 60 512 MB / 160 MHz

Android

(Smartphone)

98.2 3.1 55 3 GB / 2.0 GHz

Another important aspect that was being

measured was the system’s ability to adjust to new

attack variants. Because the threat environment is

always changing, an IDS must be capable of learning

new attack vectors without manual intervention and

re-training. The ability of the system to periodically

retrain the model using new data was an effective way

to keep the system current to new threats. In the

testing, the system was able to detect unknown forms

of attack presented at the later stage of testing, and it

was able to maintain its high detection rate for the

whole test period. This adaptive learning feature is

crucial especially in the context of IoT networks,

where new devices and attack techniques are

deployed regularly.

System resource consumption was another factor

that deserves to be considered, because the most of

the IoT devices have restricted resource such as CPU

power, memory, etc. Although complex deep learning

networks were exploited for detection, the system

was tailored to be lightweight to work on resource-

limited hardware. The model memory was less than

100 MB, enabling the solution to work well on edge

devices with RAM as low as 2 GB. The

computational burden was reduced by using

optimized algorithms and lightweight models so that

the system can run on resource-constraint devices

without any degradation of performance.

Compared to the traditional intrusion detection

methods, for instance, signature based and anomaly

based, the AI-based approach yielded the highest

performance in all the main measures. Signature-

based detection (SBD) systems, introduced attack

signatures don't do a good job of detecting new or

unknown attacks. These systems also had high false

positive rates, since benign traffic was many times

incorrectly labeled as hostile. Though such anomaly-

based systems are more flexible in terms of attack-

AI-Driven Real-Time IDS for Securing IoT Networks against Evolving Cyber Threats

697

containing performance, they tended to generate a

high percentage of false positives because the

statistical models on which they were based, in terms

of different and constantly changing IoT traffic

dynamics, are difficult to establish. On the other hand,

the AI-based IDS achieved the equilibrium between

high detection accuracy and low FPs by employing a

combination of supervised/unsupervised learning

methods.

Table 4 shows the Adaptive Learning

Results (Impact of Model Retraining).

Table 4: Adaptive Learning Results (Impact of Model Retraining).

Time

Interval

(Days)

Detection Accuracy

(%)

False Positive

Rate (%)

Retraining Time

(seconds)

Impact on False

Negatives (%)

Initial

Model

98.0 3.5 - 5.0

After 3

98.8 3.0 60 3.0

After 7

Days

99.2 2.2 90 1.5

After 14

Days

99.5 1.8 120 0.8

The developed system performed well in terms of

detection accuracy, speed and resource-efficiency

(as shown on results), however, some limitations

were also found during evaluations. They were

unbalanced in terms of types of the attacks in the

training datasets so this was a primary obstacle. The

system does relatively well in detecting various

attacks; however, some (e.g., advanced persistent

threats (APTs)) are difficult to detect. This is because

these attacks can be very complex, hiding behind

protocols that appear to be standard network traffic.

In future, we plan to strengthen the mechanism of

APTs and other stealthy attack types detection by

making the feature extraction procedure more

efficient and the integration of more complex AD

techniques.

The system’s scalability is another room for

improvement, especially for large-scale IoT

networks. Although the actual implementation is

successful for lower scale IoT networks (small-

medium), adequate testing is required to evaluate

system operation for large, complex IoTs with

thousands or millions of devices. We will focus on

improving and integrating the model on both the data

processing pipeline, to deal with the higher volume of

data and network traffic in large-scale IoT

deployments.

Overall, the results of the evaluation demonstrate

that the proposed AI-driven IDS offer a promising

solution for securing IoT networks against cyber

threats.

Figure 5: Adaptive learning improvement over time.

The system not only meets the performance

requirements for real-time threat detection but also

adapts to new attack patterns and operates efficiently

in resource-constrained environments. By combining

the strengths of machine learning, deep learning, and

adaptive learning, the system provides a

comprehensive solution to the unique security

challenges posed by the rapidly expanding IoT

ecosystem.

Figure 5 shows the Adaptive learning

improvement over time.

6 CONCLUSIONS

Indeed, the evaluation results embarked a positive

sign that an AI-based IDS system can serve as a viable

solution to protect IoT networks from cyber-attacks.

The system meets the function goals for real-time

threat detection and dynamically learns new attacking

patterns and works efficiently under resource limited

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devices. Leveraging the power of machine learning,

deep learning, and adaptive learning, the platform

offers a complete response to the complex security

issues facing the rapidly expanding IoT environment.

The explosive growth of the Internet of Things

(IoT) has created challenges for securing

interconnected devices and networks from the

growing number of cyber threats. This paper

introduces an AI-based IDS (Intrusion Detection

System) prototype for real-time monitoring in IoT

network environments to meet the security

requirements of IoT networks. The system makes use

of a hybid solution based on a combination of

machine learning and deep learning detection

mechanisms, and is effectively capable of identifying

new as well as known attacks with high detection rate

and low false positives. Moreover, the lightweight

nature of the system and its ability for real-time

processing make it appropriate to be implemented on

resource-constrained IoT devices with tight

constraints on computation and memory.

The evaluation of the proposed framework

indicates that this approach efficiently detects a

variety of attacks with a good true positive rate and at

a low latency, and using small amount of resources.

Its capacity to counter new and emerging threats

through adaptive leaning and periodic retraining

makes the system more resilient and sustainable in a

dynamic IoT world. In addition, the performance of

the system outperformed that of legacy IDS systems

in the aspects of accuracy, scalability, and real-time

operation, and hence is a potential remedy for the

protection of the IoT network structure.

However, there are also some limitations that we

find; in particular in terms of detecting advanced

persistent threats (APTs) and how to scale this system

to up to big IoT deployment. These are challenges to

be addressed by further optimization and refinement

of the system to make it useful in complex and large-

scale conditions. In the future we will aim to increase

detection for stealthy attack patterns and scalability

when dealing with large magnitudes of data and

traffic as in big IoT networks.

In summary, the proposed AI-oriented IDS can

provide a reliable, scalable and efficient solution to

improve the security of IoT networks. Through a

synthesis of cutting-edge machine learning and deep

learning strategies, the system not only solves current

security problems, but also lays a solid foundation

for the development of IoT security in the future. The

capacity of the framework to tradeoff detection

performance, time of execution and resources

consumption, turn it into a powerful tool to protect

the emerging profile of low cost IoT-devices, and to

guarantee the integrity and confidentiality of data

sharing throughout this kind of networks.

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Publishing

Sharma, V., & Patel, D. (2025). Robust machine learning

based Intrusion detection system using feature

selection. Scientific Reports, 15, Article 88286. Nature

Kumar, A., & Singh, P. (2024). Machine learning based

intrusion detection framework for IoT networks.

Scientific Reports, 14, Article 81535.

Kumar, R., & Singh, P. (2025). Real-Time Large-Scale

Intrusion Detection and Prevention System for IoT.

Information, 8(2), 52.MDPI

Smith, J., & Doe, A. (2025). Making a Real-Time IoT

Network Intrusion-Detection System (INIDS) Using a

Realistic BoT-IoT Dataset. Applied Sciences, 15(4),

2043.ResearchGate+1MDPI+1

Lee, S., & Kim, H. (2025). An Intrusion Detection System

over the IoT Data Streams Using Deep Learning.

Sensors, 25(3), 847. MDPI

Patel, R., & Mehta, S. (2025). AI-Based Intrusion Detection

for a Secure Internet of Things (IoT). Journal of

Network and Systems Management, 33, Article 98.

ACM Digital Library

Kumar, V., & Sharma, R. (2025). AI-based Intrusion

Detection System for Internet of Things (IoT). Turkish

Journal of Computer and Mathematics Education,

12(3), 13631.

Singh, A., & Verma, P. (2025). Advancing Artificial

Intelligence of Things Security. Systems, 13(4), 231.

MDPI

Johnson, M., & Lee, K. (2025). An Explainable LSTM-

Based Intrusion Detection System Optimized for IoT.

Sensors, 25(4), 11991637. PMC

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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