Biblio

Insider attacks is a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who take advantage of familiarity with the computational environment and abuse their privileges, can easily cause significant damage or losses. In this paper, we present an active defense model and framework of insider threat detection and sense. Firstly, we describe the hierarchical framework which deal with insider threat from several aspects, and subsequently, show a hierarchy-mapping based insider threats model, the kernel of the threats detection, sense and prediction. The experiments show that the model and framework could sense the insider threat in real-time effectively.

Nearest neighbor search, a classic way of identifying similar data, can be applied to various areas, including database, machine learning, natural language processing, software engineering, etc. Secure nearest neighbor search aims to find nearest neighbors to a given query point over encrypted data without accessing data in plaintext. It provides privacy protection to datasets when nearest neighbor queries need to be operated by an untrusted party (e.g., a public server). While different solutions have been proposed to support nearest neighbor queries on encrypted data, these existing solutions still encounter critical drawbacks either in efficiency or privacy. In light of the limitations in the current literature, we propose a novel approximate nearest neighbor search solution, referred to as Sparser, by leveraging a combination of space-filling curves, perturbation, and Order-Preserving Encryption. The advantages of Sparser are twofold, strengthening privacy and improving efficiency. Specifically, Sparser pre-processes plaintext data with space-filling curves and perturbation, such that data is sparse, which mitigates leakage abuse attacks and renders stronger privacy. In addition to privacy enhancement, Sparser can efficiently find approximate nearest neighbors over encrypted data with logarithmic time. Through extensive experiments over real-world datasets, we demonstrate that Sparser can achieve strong privacy protection under leakage abuse attacks and minimize search time.

Routing security plays an important role in the mobile ad hoc networks (MANETs). Despite many attempts to improve its security, the routing mechanism of MANETs remains vulnerable to attacks. Unlike most existing solutions that prevent the specific problems, our approach tends to detect the misbehavior and identify the anomalous nodes in MANETs automatically. The existing approaches offer support for detecting attacks or debugging in different routing phases, but many of them cannot answer the absence of an event. Besides, without considering the privacy of the nodes, these methods depend on the central control program or a third party to supervise the whole network. In this paper, we present a system called DAPV that can find single or collaborative malicious nodes and the paralyzed nodes which behave abnormally. DAPV can detect both direct and indirect attacks launched during the routing phase. To detect malicious or abnormal nodes, DAPV relies on two main techniques. First, the provenance tracking enables the hosts to deduce the expected log information of the peers with the known log entries. Second, the privacy-preserving verification uses Merkle Hash Tree to verify the logs without revealing any privacy of the nodes. We demonstrate the effectiveness of our approach by applying DAPV to three scenarios: 1) detecting injected malicious intermediated routers which commit active and passive attacks in MANETs; 2) resisting the collaborative black-hole attack of the AODV protocol, and; 3) detecting paralyzed routers in university campus networks. Our experimental results show that our approach can detect the malicious and paralyzed nodes, and the overhead of DAPV is moderate.

Routing security plays an important role in Mobile Ad hoc Networks (MANETs). Despite many attempts to improve its security, the routing procedure of MANETs remains vulnerable to attacks. Existing approaches offer support for detecting attacks or debugging in different routing phases, but many of them have not considered the privacy of the nodes during the anomalies detection, which depend on the central control program or a third party to supervise the whole network. In this paper, we present an approach called LAD which uses the raw logs of routers to construct control a flow graph and find the existing communication rules in MANETs. With the reasoning rules, LAD can detect both active and passive attacks launched during the routing phase. LAD can also protect the privacy of the nodes in the verification phase with the specific Merkle hash tree. Without deploying any special nodes to assist the verification, LAD can detect multiple malicious nodes by itself. To show that our approach can be used to guarantee the security of the MANETs, we deploy our experiment in NS3 as well as the practical router environment. LAD can improve the accuracy rate from 2.28% to 29.22%. The results show that LAD performs limited time and memory usages, high detection and low false positives.

RPL is a lightweight IPv6 network routing protocol specifically designed by IETF, which can make full use of the energy of intelligent devices and compute the resource to build the flexible topological structure. This paper analyzes the security problems of RPL, sets up a test network to test RPL network security, proposes a RPL based security routing protocol M-RPL. The routing protocol establishes a hierarchical clustering network topology, the intelligent device of the network establishes the backup path in different clusters during the route discovery phase, enable backup paths to ensure data routing when a network is compromised. Setting up a test prototype network, simulating some attacks against the routing protocols in the network. The test results show that the M-RPL network can effectively resist the routing attacks. M-RPL provides a solution to ensure the Internet of Things (IoT) security.