Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion.

No abstract

No abstract

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.

Internet of things witnessed rapid growth in the last decade and is considered to be a promising field that plays an all-important role in every aspect of modern-day life. However, the growth of IoT is seriously hindered by factors like limited storage, communication capabilities, and computational power. On the other hand, cloud has the potential to support a large amount of data as it has massive storage capacity and can perform complex computations. Considering the tremendous potential of these two technologies and the manner in which they complement one another, they have been integrated to form what is commonly referred to as the cloud of things (CoT). This integration is beneficial as the resulting system is more robust, intelligent, powerful, and offers promising solutions to the users. However, the new paradigm (CoT) is faced with a significant number of challenges that need to be addressed. This chapter discusses in detail various challenges like reliability, latency, scalability, heterogeneity, power consumption, standardization, etc. faced by the cloud of things.