Biblio

Wireless Sensor Networks (WSNs) utilizes many dedicated sensors for large scale networks in order to record and monitor the conditions over the environment. Cluster-Based Wireless Sensor Networks (CBWSNs) elucidates essential challenges like routing, load balancing, and lifetime of a network and so on. Conversely, security relies a major challenge in CBWSNs by limiting its resources or not forwarding the data to the other clusters. Wireless Sensor Networks utilize different security methods to offer secure information transmission. Encryption of information records transferred into various organizations thus utilizing a very few systems are the normal practices to encourage high information security. For the most part, such encoded data and also the recovery of unique data depend on symmetric or asymmetric key sets. Collectively with the evolution of security advances, unfruitful or unauthorized endeavors have been made by different illicit outsiders to snip the transmitted information and mystery keys deviously, bother the transmission procedure or misshape the transmitted information and keys. Sometimes, the limitations made in the correspondence channel, transmitting and receiving devices might weaken information security and discontinue a critical job to perform. Thus, in this paper we audit the current information security design and key management framework in WSN. Based on this audit and recent security holes, this paper recommends a plausible incorporated answer for secure transmission of information and mystery keys to address these confinements. Thus, consistent and secure clusters is required to guarantee appropriate working of CBWSNs.

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. The intensity and frequency of these attacks are increasing at an alarming rate. Numerous schemes have been proposed to mitigate the impact of DDoS attacks. This paper presents a comprehensive empirical evaluation of Machine Learning (ML)based DDoS detection techniques, to gain better understanding of their performance in different types of environments. To this end, a framework is developed, focusing on different attack scenarios, to investigate the performance of a class of ML-based techniques. The evaluation uses different performance metrics, including the impact of the “Class Imbalance Problem” on ML-based DDoS detection. The results of the comparative analysis show that no one technique outperforms all others in all test cases. Furthermore, the results underscore the need for a method oriented feature selection model to enhance the capabilities of ML-based detection techniques. Finally, the results show that the class imbalance problem significantly impacts performance, underscoring the need to address this problem in order to enhance ML-based DDoS detection capabilities.