Loading...
Please wait, while we are loading the content...
Similar Documents
Implementing Filtering Techniques to Prevent Packet Drop Attacks and Detecting Provenance Forgery
| Content Provider | Semantic Scholar |
|---|---|
| Author | Priya, Vishnu Tech, M. |
| Copyright Year | 2017 |
| Abstract | Large-scale sensor networks are used in numerous application domains, and the data they collect are used in decision making for critical infrastructures. Data are travelled from multiple sources through intermediate processing nodes that aggregate information. A malicious adversary may introduce additional nodes in the network or compromise existing ones. Therefore, assuring high data trustworthiness is crucial for correct decision-making. Data provenance represents a key factor in evaluating the trustworthiness of sensor data. Provenance management for sensor networks introduces several challenging requirements, such as low energy and bandwidth consumption, efficient storage and secure transmission. I implemented the filtering techniques to securely transmit provenance for sensor data. The proposed technique relies on in-packet Bloom filters to encode provenance. I introduce efficient mechanisms for provenance verification and reconstruction at the base station. In addition, we extend the secure provenance scheme with functionality to detect packet drop attacks staged by malicious data forwarding nodes. I evaluate the proposed technique both analytically and empirically, and the results prove the effectiveness and efficiency of the implementing filtering techniques in detecting packet forgery and loss attacks. As opposed to existing research that employs separate transmission channels for data and provenance. In contrast, only require a single transmission channel for both data and provenance. Furthermore, traditional provenance security solutions use intensively cryptography and digital signatures, and they employ append-based data structures to store provenance, leading to prohibitive costs. In contrast, I use only fast Message Authentication Code (MAC) schemes and Bloom filters (BF), which are fixed-size data structures that compactly represent provenance. Introduction Sensor networks are used in many application domains, such as cyber physical infrastructure systems, environmental monitoring, power grids, etc. Data are produced at a large number of sensor node sources and processed in-network at intermediate hops on their way to a Base Station (BS) that performs decision-making. The diversity of data sources creates the need to assure the trustworthiness of data, such that only trustworthy information is considered in the decision process. Data provenance is an effective method to assess data trustworthiness, since it summarizes the history of ownership and the actions performed on the data. Recent research [12] highlighted the key contribution of provenance in systems where the use of untrustworthy data may lead to catastrophic failures (e.g., SCADA systems). Although provenance modeling, collection, and querying have been studied extensively for workflows and curated databases [13], [14], provenance in sensor networks has not been properly addressed. I investigate the problem of secure and efficient provenance transmission and processing for sensor networks, and i use provenance to detect packet loss attacks staged by malicious sensor nodes. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijmetmr.com/oljune2017/VallavojuKrishnaPriya-JavvajiVenkatarao-83.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
