Neighbourhood-pair attack in social network data publishing

Vertex re-identification is one of the significant and challenging problems in social network. In this paper, we show a new type of vertex reidentification attack called neighbourhood-pair attack. This attack utilizes the neighbourhood topologies of two connected vertices. We show both theoretically and empirically that this attack is possible on anonymized social network and has higher re-identification rate than the existing structural attacks.

Further observations on smart-card-based password-authenticated key agreement in distributed systems

This paper initiates the study of two specific security threats on smart-card-based password authentication in distributed systems. Smart-card-based password authentication is one of the most commonly used security mechanisms to determine the identity of a remote client, who must hold a valid smart card and the corresponding password to carry out a successful authentication with the server. The authentication is usually integrated with a key establishment protocol and yields smart-card-based password-authenticated key agreement. Using two recently proposed protocols as case studies, we demonstrate two new types of adversaries with smart card: 1) adversaries with pre-computed data stored in the smart card, and 2) adversaries with different data (with respect to different time slots) stored in the smart card. These threats, though realistic in distributed systems, have never been studied in the literature. In addition to point out the vulnerabilities, we propose the countermeasures to thwart the security threats and secure the protocols. © 2013 IEEE.

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

Packet faking attack: a novel attack and detection mechanism in OppNets

 Security is a major challenge in Opportunistic Networks (OppNets) due to its characteristics of being an open medium with dynamic topology, there is neither a centralized management nor clear lines of defence. A packet dropping attack is one of the major security threats in OppNets as neither source nodes nor destination nodes have any knowledge of when or where a packet will be dropped. In this paper, we present a novel attack and detection mechanism against a special type of packet dropping where the malicious node drops one packet or more and injects a new fake packet instead. Our novel detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea; the creation time of each packet. Significant results show this robust mechanism achieves a very high accuracy and detection rate.

Analysis of insiders attack mitigation strategies

Insider threat has become a serious information security issues within organizations. In this paper, we analyze the problem of insider threats with emphases on the Cloud computing platform. Security is one of the major anxieties when planning to adopt the Cloud. This paper will contribute towards the conception of mitigation strategies that can be relied on to solve the malicious insider threats. While Cloud computing relieves organizations from the burden of the data management and storage costs, security in general and the malicious insider threats in particular is the main concern in cloud environments. We will analyses the existing mitigation strategies to reduce malicious insiders threats in Cloud computing.

The influence of rare earth mercaptoacetate on the initiation of corrosion on AA2024-T3 Part II: The influence of intermetallic compositions within heavily attacked sites

Localised corrosion is typical on AA2024-T3 due to intermetallic particles embedded in the alloy. The effect of intermetallic compositions on corrosion are not yet fully understood. EPMA data on AA2024-T3 surfaces before and after a 16. min immersion, analyses the influence of intermetallic clustering on the severity attack at local sites. While sites with a high number of domains and a large S-phase surface area typically lead to severe attack, maximising these features did not always lead to severe corrosion attack. Cerium or praseodymium mercaptoacetate inhibited corrosion ring formation. The common trends observed from such attack sites was also discussed.

Out of sight but not out of mind: corvids prey extensively on eggs of burrow-nesting penguins

Context Egg depredation is a major cause of reproductive failure among birds and can drive population declines. In this study we investigate predatory behaviour of a corvid (little raven; Corvus mellori) that has only recently emerged, leading to widespread and intense depredation of eggs of a burrow-nesting seabird (little penguin; Eudyptula minor). Aims The main objective of this study was to measure the rate of penguin egg depredation by ravens to determine potential threat severity. We also examined whether penguin burrow characteristics were associated with the risk of egg depredation. Ravens generally employ two modes of predatory behaviour when attacking penguin nests; thus we examined whether burrow characteristics were associated with these modes of attack. Methods Remote-sensing cameras were deployed on penguin burrows to determine egg predation rates. Burrow measurements, including burrow entrance and tunnel characteristics, were measured at the time of camera deployment. Key results Overall, clutches in 61% of monitored burrows (n≤203) were depredated by ravens, the only predator detected by camera traps. Analysis of burrow characteristics revealed two distinct types of burrows, only one of which was associated with egg depredation by ravens. Clutches depredated by ravens had burrows with wider and higher entrances, thinner soil or vegetation layer above the egg chamber, shorter and curved tunnels and greater areas of bare ground and whitewash near entrances. In addition, 86% were covered by bower spinach (Tetragonia implexicoma), through which ravens could excavate. Ravens used two modes to access the eggs: they attacked through the entrance (25% of burrow attacks, n≤124); or dug a hole through the burrow roof (75% of attacks, n≤124). Burrows that were subject to attack through the entrance had significantly shorter tunnels than burrows accessed through the roof. Conclusions The high rates of clutch loss recorded here highlight the need for population viability analysis of penguins to assess the effect of egg predation on population growth rates. Implications The subterranean foraging niche of a corvid described here may have implications for burrow-nesting species worldwide because many corvid populations are increasing, and they exhibit great capacity to adopt new foraging strategies to exploit novel prey. Journal compilation

An efficient detection mechanism against packet faking attack in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In our previous novel attack (Packet Faking Attack [1]) we presented a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. In this paper, we present an efficient detection mechanism against this type of attack where each node can detect the attack instead of the destination node. Our detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea, that is, the packet creation time of each packet. Simulation results show this robust mechanism achieves a very high accuracy, detection rate and good network traffic reduction.

Catabolism attack and anabolism defense: a novel attack and traceback mechanism in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense.A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a novel attack and traceback mechanism against a special type of packet dropping where the malicious node drops one or more packets and then injects new fake packets instead. We call this novel attack a Catabolism Attack and we call our novel traceback mechanism against this attack Anabolism Defense. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.