Memory-aware Efficient Deep Learning Mechanism for IoT Devices

2021 ◽  
Author(s):  
Jishnu Banerjee ◽  
Sahidul Islam ◽  
Wei Wei ◽  
Chen Pan ◽  
Dakai Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Mechanism ◽  
Iot Devices

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

scholarly journals Learner’s Dilemma: IoT Devices Training Strategies in Collaborative Deep Learning

2020 ◽  
Author(s):  
Deepti Gupta ◽  
Olumide Kayode ◽  
Smriti Bhatt ◽  
Maanak Gupta ◽  
Ali Saman Tosun
Keyword(s):  
Deep Learning ◽  
Training Strategies ◽  
Iot Devices

A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids

2021 ◽  
Author(s):  
Yangyang Tian ◽  
Qi Wang ◽  
Zhimin Guo ◽  
Huitong Zhao ◽  
Sulaiman Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Smart Grids ◽  
Power Line ◽  
Line Detection ◽  
Learning Mechanism

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

Deep Learning Based Malware Detection for IoT Devices

2021 ◽  
pp. 1247-1254
Author(s):  
N. Naveen ◽  
Mohammed Asim Safwan ◽  
T. G. Manoj Nayaka ◽  
N. Nischal
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Iot Devices

scholarly journals A Stacked Deep Learning Approach for IoT Cyberattack Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bandar Alotaibi ◽  
Munif Alotaibi
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Denial Of Service ◽  
Large Datasets ◽  
Learning Approach ◽  
Distributed Denial Of Service ◽  
Learning Method ◽  
Traffic Data ◽  
Security Breaches ◽  
Iot Devices

Internet of things (IoT) devices and applications are dramatically increasing worldwide, resulting in more cybersecurity challenges. Among these challenges are malicious activities that target IoT devices and cause serious damage, such as data leakage, phishing and spamming campaigns, distributed denial-of-service (DDoS) attacks, and security breaches. In this paper, a stacked deep learning method is proposed to detect malicious traffic data, particularly malicious attacks targeting IoT devices. The proposed stacked deep learning method is bundled with five pretrained residual networks (ResNets) to deeply learn the characteristics of the suspicious activities and distinguish them from normal traffic. Each pretrained ResNet model consists of 10 residual blocks. We used two large datasets to evaluate the performance of our detection method. We investigated two heterogeneous IoT environments to make our approach deployable in any IoT setting. Our proposed method has the ability to distinguish between benign and malicious traffic data and detect most IoT attacks. The experimental results show that our proposed stacked deep learning method can provide a higher detection rate in real time compared with existing classification techniques.

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