Towards Machine Learning Based Intrusion Detection in IoT Networks

The Internet of Things(IoT)integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention.In recent years,IoT based systems have been used in improving the experience in many applications including healthcare,agriculture,supply chain,education,transportation and traffic monitoring,utility services etc.However,node heterogeneity raised security concern which is one of the most complicated issues on the IoT.Implementing security measures,including encryption,access control,and authentication for the IoT devices are ineffective in achieving security.In this paper,we identified various types of IoT threats and shallow(such as decision tree(DT),random forest(RF),support vector machine(SVM))as well as deep machine learning(deep neural network(DNN),deep belief network(DBN),long short-term memory(LSTM),stacked LSTM,bidirectional LSTM(Bi-LSTM))based intrusion detection systems(IDS)in the IoT environment have been discussed.The performance of these models has been evaluated using five benchmark datasets such as NSL-KDD,IoTDevNet,DS2OS,IoTID20,and IoT Botnet dataset.The various performance metrics such as Accuracy,Precision,Recall,F1-score were used to evaluate the performance of shallow/deep machine learning based IDS.It has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.

Guided Dropout: Improving Deep Networks Without Increased Computation

Deep convolution neural networks are going deeper and deeper.How-ever,the complexity of models is prone to overfitting in training.Dropout,one of the crucial tricks,prevents units from co-adapting too much by randomly drop-ping neurons during training.It effectively improves the performance of deep net-works but ignores the importance of the differences between neurons.To optimize this issue,this paper presents a new dropout method called guided dropout,which selects the neurons to switch off according to the differences between the convo-lution kernel and preserves the informative neurons.It uses an unsupervised clus-tering algorithm to cluster similar neurons in each hidden layer,and dropout uses a certain probability within each cluster.Thereby this would preserve the hidden layer neurons with different roles while maintaining the model’s scarcity and gen-eralization,which effectively improves the role of the hidden layer neurons in learning the features.We evaluated our approach compared with two standard dropout networks on three well-established public object detection datasets.Experimental results on multiple datasets show that the method proposed in this paper has been improved on false positives,precision-recall curve and average precision without increasing the amount of computation.It can be seen that the increased performance of guided dropout is thanks to shallow learning in the net-works.The concept of guided dropout would be beneficial to the other vision tasks.