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Abstract. Manet security has a lot of open issues. Due to its characteristics, this kind of network needs preventive and corrective protection. In this paper, we focus on corrective protection proposing an anomaly IDS model for Manet. The design and development of the IDS are considered in our 3 main stages: normal behavior construction, anomaly detection and model update. A parametrical mixture model is used for behavior modeling from reference data. The associated Bayesian classification leads to the detection algorithm. MIB variables are used to provide IDS needed information. Experiments of DoS and scanner attacks validating the model are presented as well.

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Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user's electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers' identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128 bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design.2 of 25 consists of obfuscating the instantaneous consumption pattern of each consumer [5,6]. This is generally accomplished by hiding the instantaneous power consumption of the client as fine-grained data can reveal in detail the lifestyle of the consumer [7,8]. However, the profile of traded energy also delivers relevant information about prosumers to their neighbors. As in [9], the ability to link the bids to individual consumers allows the untrusted entity to build up a profile of the consumer's behavior. In particular, the quantities of traded energy can be very informative about the economical welfare of the owner [10]. Privacy requirements dictate that prosumers cannot gain information regarding other prosumers' consumption and production-not even if they are trade partners [11]. Models dealing with energy trade directly among prosumers [3,12] limit themselves to exploiting the trade environment without discussing in detail data-security aspects related to the identities of the traders in relation to their neighbors. As a consequence, several topics related to privacy requirements are still open in SGs, such as power production and bidding in trading systems.In this paper, we consider the problem of providing data privacy for self-interested players that trade energy in the context of a Neighborhood Area Network (NAN). The energy is sold by local micro-generators and locally purchased by their neighbors, also known as the final users. Our framework deals simultaneously with SG data-security requirements and energy-trade systems. As a first contribution, the proposed framework has a privacy-preserving model which has a low computational complexity and avoids completely an unauthorized party to identify the bidders, ...