Greedy Attack Research Articles

Rumor Detection with A Novel Graph Neural Network Approach

The wide spread of rumors[3] on social media has caused a negative impact on people's daily life, leading to potential panic, fear and mental health problems for the public.[47] How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors[6], while very few works focus on correlation among users that they may coordinate to spread rumors in order to gain a large popularity. In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media. Specifically, we leverage graph neural networks to learn the representations of user correlation from a bipartite graph[5] that describes the correlations between users and source tweets[12], and the representations of information propagation with a tree structure. Then we combine the learned representations from these two modules to classify the rumors. Since malicious users intend to subvert our model after deployment, we further develop a greedy attack scheme to analyze the cost of three adversarial attacks: graph attack, comment attack and joint attack. Evaluation results on two public datasets illustrate that the proposed MODEL outperforms the state-of-the-art rumor detection models. We also demonstrate our method performs well for early rumor detection.[8] Moreover, the proposed detection method is more robust to adversarial attacks compared to the best existing method. Importantly, we show that it requires high cost for attackers to subvert user correlation pattern, demonstrating the importance of considering user correlation for rumor detection.

Deception attacks on Kalman filtering with interval estimation performance loss

This paper studies the problem of optimal deception attacks against remote state estimation, where the measurement data is transmitted through an unreliable wireless channel. A malicious agent is capable of intercepting and modifying raw data, with the goal to cause the maximum estimation quality degradation and deceive χ2 anomaly detectors. Contrary to existing studies that focused on greedy attack performance, we consider a more general scenario that the attacker aims to maximize the summation of estimation errors in a fixed interval. It is shown that the information-based optimal attack is a linear combination of the minimum mean-square error estimates of all historical prediction errors. The combination coefficients can be obtained by solving a convex optimization problem. Moreover, the proposed attack can be generalized to deceive interval χ2 detectors with different lengths by slightly modifying the stealthiness constraint. The effectiveness of the proposed method is verified with numerical examples and comparative studies.

Vulnerability Assessment of Power Grids Against Link-Based Attacks

In modern power grids, a local failure or attack can trigger catastrophic cascading failures, which makes it challenging to assess the attack vulnerability of power grids. In this brief, we define the optimal $K$ -link attack problem and study the attack vulnerability of power grids in cascading failures. We propose a link centrality measure based on both topological and electrical properties for power grids. According to this centrality measure, we propose a greedy attack algorithm and an optimal attack algorithm. Simulation results based on standard IEEE bus test data show that the optimal attack is better than the greedy attack and the traditional PSO-based attack in fracturing power grids. Moreover, the greedy attack has an adequate attack efficiency with a smaller computational cost. This brief aids in understanding the vulnerability of power grids and provides clues for securing power grids.

Dealing with a non green behaviour in WSN

Nowadays, the energy efficiency in wireless sensor networks (WSN) has become a challenging issue. Indeed, due to the sustainable development and deployment of such networks in our daily lives, energy efficiency is asked lto be significantly improved in order to achieve green networking. Greedy behaviours are one of the most malicious and insidious Denial of Service attacks, that threaten the green behaviour of wireless network. In such an attack, a greedy node attempts to improve its bandwidth at the expense of other nodes by not respecting its access network procedure, resulting in an overall increase of the energy consumption in the network. In this paper, we are interested in evaluating the energy efficiency of WSN in presence of a greedy behaviour. For this effect, we consider ZigBee networks using the non slotted IEEE 802.15.4 procedure. A time Petri net based modelling that characterizes the behaviour of both greedy and sane nodes, is first presented. Then, we simulate the model under TiNA platform by considering different scenarios in order to assess the energy efficiency of the network as well as other performance parameters. Hence, we confront the obtained values to discuss the impact of the greedy attack on the energy efficiency as well as a strategy to detect such a non green behaviour. Finally, we propose countermeasures to thwart the impact of the latter on the energy efficiency of the network.

Characterizing the greedy behavior in wireless ad hoc networks

AbstractWhile the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks (WLAN), its study for multi‐hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by manipulating intelligently MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi‐hop fashion over wireless links. In this case, an attempting greedy node, if it behaves similarly as in a WLAN, trying to starve all its neighbors, then its next hop forwarder will be also prevented from forwarding its own traffic, which leads obviously to an end to end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc network, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. Then, we present a new strategy to launch such a greedy attack in a proactive routing based wireless ad hoc network. A detailed description of the proposed strategy is provided along with its validation through extensive simulations. The obtained results show that a greedy node, applying the defined strategy, can gain more bandwidth than its neighbors and keep the end‐to‐end throughput of its own flows highly reasonable. Copyright © 2010 John Wiley & Sons, Ltd.