Biblio

Voting among replicated data collection devices is a means to achieve dependable data delivery to the end-user in a hostile environment. Failures may occur during the data collection process: such as data corruptions by malicious devices and security/bandwidth attacks on data paths. For a voting system, how often a correct data is delivered to the user in a timely manner and with low overhead depicts the QoS. Prior works have focused on algorithm correctness issues and performance engineering of the voting protocol mechanisms. In this paper, we study the methods for autonomic management of device replication in the voting system to deal with situations where the available network bandwidth fluctuates, the fault parameters change unpredictably, and the devices have battery energy constraints. We treat the voting system as a `black-box' with programmable I/O behaviors. A management module exercises a macroscopic control of the voting box with situational inputs: such as application priorities, network resources, battery energy, and external threat levels.

The Internet is vulnerable to bandwidth distributed denial-of-service (BW-DDoS) attacks, wherein many hosts send a huge number of packets to cause congestion and disrupt legitimate traffic. So far, BW-DDoS attacks have employed relatively crude, inefficient, brute force mechanisms; future attacks might be significantly more effective and harmful. To meet the increasing threats, we must deploy more advanced defenses.

The shrew distributed denial of service (DDoS) attack is very detrimental for many applications, since it can throttle TCP flows to a small fraction of their ideal rate at very low attack cost. Earlier works mainly focused on empirical studies of defending against the shrew DDoS, and very few of them provided analytic results about the attack itself. In this paper, we propose a mathematical model for estimating attack effect of this stealthy type of DDoS. By originally capturing the adjustment behaviors of victim TCPs congestion window, our model can comprehensively evaluate the combined impact of attack pattern (i.e., how the attack is configured) and network environment on attack effect (the existing models failed to consider the impact of network environment). Henceforth, our model has higher accuracy over a wider range of network environments. The relative error of our model remains around 10% for most attack patterns and network environments, whereas the relative error of the benchmark model in previous works has a mean value of 69.57%, and it could be more than 180% in some cases. More importantly, our model reveals some novel properties of the shrew attack from the interaction between attack pattern and network environment, such as the minimum cost formula to launch a successful attack, and the maximum effect formula of a shrew attack. With them, we are able to find out how to adaptively tune the attack parameters (e.g., the DoS burst length) to improve its attack effect in a given network environment, and how to reconfigure the network resource (e.g., the bottleneck buffer size) to mitigate the shrew DDoS with a given attack pattern. Finally, based on our theoretical results, we put forward a simple strategy to defend the shrew attack. The simulation results indicate that this strategy can remarkably increase TCP throughput by nearly half of the bottleneck bandwidth (and can be higher) for general attack patterns.

Distributed Denial of Service (DDoS) attacks are one of the challenging network security problems to address. The existing defense mechanisms against DDoS attacks usually filter the attack traffic at the victim side. The problem is exacerbated when there are spoofed IP addresses in the attack packets. In this case, even if the attacking traffic can be filtered by the victim, the attacker may reach the goal of blocking the access to the victim by consuming the computing resources or by consuming a big portion of the bandwidth to the victim. This paper proposes a Trace back-based Defense against DDoS Flooding Attacks (TDFA) approach to counter this problem. TDFA consists of three main components: Detection, Trace back, and Traffic Control. In this approach, the goal is to place the packet filtering as close to the attack source as possible. In doing so, the traffic control component at the victim side aims to set up a limit on the packet forwarding rate to the victim. This mechanism effectively reduces the rate of forwarding the attack packets and therefore improves the throughput of the legitimate traffic. Our results based on real world data sets show that TDFA is effective to reduce the attack traffic and to defend the quality of service for the legitimate traffic.

In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of ℓ1 nodes as well as all the repair traffic entering a second disjoint set of ℓ2 nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever ℓ2 ≤ d - k + 1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.

Sybil attack poses a serious threat to geographic routing. In this attack, a malicious node attempts to broadcast incorrect location information, identity and secret key information. A Sybil node can tamper its neighboring nodes for the purpose of converting them as malicious. As the amount of Sybil nodes increase in the network, the network traffic will seriously affect and the data packets will never reach to their destinations. To address this problem, researchers have proposed several schemes to detect Sybil attacks. However, most of these schemes assume costly setup such as the use of relay nodes or use of expensive devices and expensive encryption methods to verify the location information. In this paper, the authors present a method to detect Sybil attacks using Sequential Hypothesis Testing. The proposed method has been examined using a Greedy Perimeter Stateless Routing (GPSR) protocol with analysis and simulation. The simulation results demonstrate that the proposed method is robust against detecting Sybil attacks.

Sybil attack poses a serious threat to geographic routing. In this attack, a malicious node attempts to broadcast incorrect location information, identity and secret key information. A Sybil node can tamper its neighboring nodes for the purpose of converting them as malicious. As the amount of Sybil nodes increase in the network, the network traffic will seriously affect and the data packets will never reach to their destinations. To address this problem, researchers have proposed several schemes to detect Sybil attacks. However, most of these schemes assume costly setup such as the use of relay nodes or use of expensive devices and expensive encryption methods to verify the location information. In this paper, the authors present a method to detect Sybil attacks using Sequential Hypothesis Testing. The proposed method has been examined using a Greedy Perimeter Stateless Routing (GPSR) protocol with analysis and simulation. The simulation results demonstrate that the proposed method is robust against detecting Sybil attacks.

We consider the setting of HTTP traffic over encrypted tunnels, as used to conceal the identity of websites visited by a user. It is well known that traffic analysis (TA) attacks can accurately identify the website a user visits despite the use of encryption, and previous work has looked at specific attack/countermeasure pairings. We provide the first comprehensive analysis of general-purpose TA countermeasures. We show that nine known countermeasures are vulnerable to simple attacks that exploit coarse features of traffic (e.g., total time and bandwidth). The considered countermeasures include ones like those standardized by TLS, SSH, and IPsec, and even more complex ones like the traffic morphing scheme of Wright et al. As just one of our results, we show that despite the use of traffic morphing, one can use only total upstream and downstream bandwidth to identify – with 98% accuracy - which of two websites was visited. One implication of what we find is that, in the context of website identification, it is unlikely that bandwidth-efficient, general-purpose TA countermeasures can ever provide the type of security targeted in prior work.