Biblio

Wireless sensor networks (WSN) are used to perceive many data such as temperature, vibration, pressure in the environment and to produce results; it is widely used, including in critical fields such as military, intelligence and health. However, because of WSNs have different infrastructure and architecture than traditional networks, different security measures must be taken. In this study, an intrusion detection system (IDS) is modeled to ensure WSN security. Since the signature, misuse and anomaly based detection methods for intrusion detection systems are insufficient to provide security alone, a hybrid model is proposed in which these methods are used together. In the hybrid model, anomaly rules were defined for attack detection, and machine learning algorithms BayesNet, J48 and Random Forest were used to classify normal and abnormal traffic. Unlike the studies in the literature, CSE-CIC-IDS2018, the most up-to-date data set, was used to create attack profiles. Considering both hardware constraints and battery capacities of WSNs; the data was pre-processed in accordance with data mining principles. The results showed that the developed model has high accuracy and low false alarm rate.

Collaborative filtering (CF) is a successful and hence most widely used technique for recommender systems. However, it is vulnerable to shilling attack due to its open nature, which results in generating biased or false recommendations for users. In literature, segment attack (push attack) has been widely studied and investigated while rare studies have been performed on nuke attack, to the best of our knowledge. Further, the robustness of binary collaborative filtering and hybrid approach has not been investigated against segment-focused attack. In this paper, from the perspective of robustness, binary collaborative filtering, hybrid approach, stand-alone rating user-based, and stand-alone rating item- based recommendation have been evaluated against segment attack on a large dataset (100K ratings) which is found to be more successful as it attacks target set of items. With an aim to find an approach which reflects a higher accuracy in recommending items and is less vulnerable to segment-based attack, the possibility of any relationship between accuracy and vulnerability of six CF approaches were studied. Such an approach needs to be re-examined by the researchers marking the future of recommender system (RS). Experimental results show negligible positive correlation between accuracy and vulnerability of techniques. Publicly available dataset namely MovieLens was used for conducting experiments. Robustness and accuracy of CF techniques were calculated using prediction shift and F-measure, respectively.

In Ad hoc networks the main purpose is communication without infrastructure and there are many implementations already done on that. There is little effort done for security to prevent threats in ad hoc networks (like MANETs). It is already proven that; there is no any centralized mechanism for defence against threats, such as a firewall, an intrusion detection system, or a proxy in ad hoc networks. Ad hoc networks are very convenient due to its features like self-maintenance, self-organizing and providing wireless communication. In Ad hoc networks there is no fixed infrastructure in which every node works like simply a router which stores and forwards packet to final destination. Due to these dynamic topology features, Ad hoc networks are anywhere, anytime. Therefore, it is necessary to make a secure mechanism for the ad hoc components so that with flexibility they have that security also. This paper shows the secure and flexible implementation about to protect any ad hoc networks. This proposed system design is perfect solution to provide security with flexibility by providing a hybrid system which combines ECC and MAES to detect and prevent Ad hoc network attacks using Intrusion detection system. The complete proposed system designed on NS 2.35 software using Ubuntu (Linux) OS.