An
Optimal Distributed Malware Defense System for Mobile Networks with
Heterogeneous Devices
Abstract—as
malware attacks become more frequent in mobile networks, deploying an efficient
defense system to protect against infection and to help the infected nodes to
recover is important to Contain
serious spreading and outbreaks. The technical challenges are that mobile
devices are heterogeneous in terms of operating systems, and the malware can
infect the targeted system in any opportunistic fashion via local and global
connectivity, while the to-be-deployed defense system on the other hand would be
usually resource limited. In this paper, we investigate the problem of optimal
distribution of content-based signatures of malware to minimize the number of
infected nodes, which can help to detect the corresponding malware and to
disable further propagation. We model the defense system with realistic assumptions
addressing all the above challenges, which have not been addressed in previous
analytical work. Based on the proposed framework of optimizing the system
welfare utility through the signature allocation, we provide an encounter-based
distributed algorithm based on Metropolis sampler. Through extensive
simulations with both synthetic and real mobility traces, we show that the
distributed algorithm achieves the optimal solution, and performs efficiently
in realistic environments.
Behavioral
Detection and Containment of Proximity Malware in Delay Tolerant Networks
Abstract—with
the universal presence of short-range connectivity technologies (e.g.,
Bluetooth and, more recently, Wi-Fi Direct) in the consumer electronics market,
the delay tolerant-network (DTN) model is becoming a viable alternative to the
traditional infrastructural model. Proximity malware, Which
exploits the temporal dimension and distributed nature of DTNs in
self-propagation, poses threats to users of new technologies? In this paper, we
address the proximity malware detection and containment problem with explicit
consideration for the unique characteristics of DTNs. We formulate the malware detection
process as a decision problem under a general behavioral malware
characterization framework. We analyze the risk associated with the decision
problem and design a simple yet effective malware containment strategy,
look-ahead, which is distributed by nature and reflects an individual node’s
intrinsic trade-off between staying connected (with other nodes) and staying
safe (from malware). Furthermore, we consider the benefits of sharing assessments
among directly connected nodes and address the challenges derived from the DTN
model to such sharing in the presence of liars (i.e., malicious nodes sharing
false assessments) and defectors (i.e., good nodes that have turned malicious
due to malware infection). Real mobile
Network
traces are used to verify our analysis.
Dynamic
Trust Management for Delay Tolerant Networks and Its Application to Secure
Routing
Abstract—Delay tolerant networks (DTNs) are
characterized by high end-to-end latency, frequent disconnection, and
opportunistic communication over unreliable wireless links. In this paper, we
design and validate a dynamic trust management protocol for secure routing
optimization in DTN environments in the presence of well-behaved, selfish and
malicious nodes. We develop a novel model-based methodology for the analysis of
our trust protocol and validate it via extensive simulation. Moreover, we
address dynamic trust management, i.e., determining and applying the best
operational settings at runtime in response to dynamically changing network
conditions to minimize trust bias and to maximize the routing application
performance. We perform a comparative analysis of our proposed routing protocol
against Bayesian trust-based and non-trust based (PROPHET and epidemic) routing
protocols. The results demonstrate that our protocol is able to deal with
selfish behaviors and is resilient against trust-related attacks. Furthermore,
our trust-based routing protocol can effectively trade off message overhead and
message delay for a significant gain in delivery ratio. Our trust-based routing
protocol operating under identified best settings outperforms Bayesian
trust-based routing and PROPHET, and approaches the ideal performance of
epidemic routing in delivery ratio and message delay without incurring high
message or protocol maintenance overhead.
Reliable
Energy Efficient Routing Algorithms in Wireless Ad Hoc Networks
Abstract:
Low Energy Adaptive Reliable Routing (LEARR) finds routes which require least amount
of energy for reliable packet transfer in ad hoc networks. It defines the
energy cost of packet forwarding by a node as the fraction of remaining battery
energy which is consumed by a node to forward a packet. It includes the energy
consumed for retransmission of the packet as well, when the packet or its
acknowledgment is lost. It is found that LEARR can effectively reduce the
energy consumption of nodes and balance the traffic load among them.
Furthermore, LEARR is able to find reliable routes, in which constituent links
require less number of packet retransmissions due to packet loss. It in turns
decreases the latency of packet delivery and saves energy as well. To prolong
the network lifetime, power management and energy-efficient routing techniques
become necessary. Energy-aware routing is an effective way to extend the
operational lifetime of wireless ad hoc networks.
E-MACs:
Towards More Secure and More Efficient Constructions of Secure Channels
Optimal
Multicast Capacity and Delay Tradeoffs in MANETs
Abstract—In
this paper, we give a global perspective of multicast capacity and delay
analysis in Mobile Ad Hoc Networks (MANETs). Specifically, we consider four
node mobility models: (1) two-dimensional i.i.d. mobility, (2) two-dimensional
hybrid random walk, (3) one-dimensional i.i.d. mobility, and (4)
one-dimensional hybrid random walk. Two mobility time-scales are investigated
in this paper: (i) Fast mobility where node mobility is at the same time-scale
as data transmissions; (ii) Slow mobility where node mobility is assumed to
occur at a much slower time-scale than data transmissions. Given a delay
constraint D, we first characterize the optimal multicast capacity for each of
the eight types of mobility models, and then we develop a scheme that can
achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic
factor. In addition, we also study heterogeneous networks with infrastructure
support.
STARS:
A Statistical Traffic Pattern Discovery System for Anonymous MANET communications
Abstract—Anonymous
MANET routing relies on techniques such as re-encryption on each hop to hide
end-to-end communication relations. However, passive signal detectors and
traffic analyzers can still retrieve sensitive information from PHY and MAC
layers to derive end-to-end communication relations through statistical traffic
analysis. In this paper, we propose a Statistical Traffic pattern discovery
System (STARS) based on Eigen analysis which can greatly improve the accuracy
to derive traffic patterns in MANETs. A STAR intends to find out the sources
and destinations of captured packets and to discover the end-to-end
communication relations. The proposed approach is purely passive. It does not
require analyzers to be actively involved in MANET transmissions and to possess
encryption keys to decrypt traffic. We present theoretical models as well as
extensive simulations to demonstrate our solutions.
Ref : IEEE 2012 TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Project Price: Contact US
Abstract—In dynamic peer to peer (P2P) e-commerce, it is an important and difficult problem to promote online businesses without sacrificing the desired trust to secure transactions. In this paper, we address malicious threats in order to guarantee secrecy and integrity of recommendations exchanged among peers in P2P e-commerce. In addition to trust, secret keys are required to be established between each peer and its neighbors. Further, we propose a key management approach gkeying to generate six types of keys. Our work mainly focuses on key generation for securing recommendations, and ensuring the integrity of recommendations. The proposed approach presented with a security and performance analysis, is more secure and more efficient in terms of communication cost, computation cost, storage cost, and feasibility.
Secured Trust: A Dynamic Trust Computation Model for Secured Communication in Multiagent Systems
Ref: IEEE 2012 TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING,
Project Price: Contact US
Abstract—Security and privacy issues have become critically important with the fast expansion of multiagent systems. Most network applications such as pervasive computing, grid computing, and P2P networks can be viewed as multiagent systems which are open, anonymous, and dynamic in nature. Such characteristics of multiagent systems introduce vulnerabilities and threats to providing secured communication. One feasible way to minimize the threats is to evaluate the trust and reputation of the interacting agents. Many trust/reputation models have done so, but they fail to properly evaluate trust when malicious agents start to behave in an unpredictable way. Moreover, these models are ineffective in providing quick response to a malicious agent’s oscillating behavior. Another aspect of multiagent systems which is becoming critical for sustaining good service quality is the even distribution of workload among service providing agents. Most trust/reputation models have not yet addressed this issue. So, to cope with the strategically altering behavior of malicious agents and to distribute workload as evenly as possible among service providers; we present in this paper a dynamic trust computation model called “SecuredTrust.” In this paper, we first analyze the different factors related to evaluating the trust of an agent and then propose a comprehensive quantitative model for measuring such trust. We also propose a novel load-balancing algorithm based on the different factors defined in our model. Simulation results indicate that our model compared to other existing models can effectively cope with strategic behavioral change of malicious agents and at the same time efficiently distribute workload among the service providing agents under stable condition.
Ref:2012 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Project Price: Contact US
Abstract—In Peer-to-Peer (P2P) trust management, feedback provides an efficient and effective way to build a reputation-based trust relationship among peers. There is no doubt that the scalability of a feedback aggregating overlay is the most fundamental requirement for large-scale P2P computing. However, most previous works either paid little attention to the s calability of feedback aggregating overlay or relied on the flooding-based strategy to collect feedback, which greatly affects the system scalability. In this paper, we proposed a scalable feedback aggregating (SFA) overlay for large-scale P2P trust evaluation. First, the local trust rating method is defined based on the time attenuation function, which can satisfy the two dynamic properties of trust. The SFA overlay is then proposed from a scalable perspective. Not only can the SFA overlay strengthen the scalability of the feedback aggregation mechanism for large-scale P2P applications, but it can also reduce networking risk and improve system efficiency. More importantly, based on the SFA overlay, an adaptive trustworthiness computing method can be defined. This method surpasses the limitations of traditional weighting methods for trust factors, in which weights are assigned subjectively. Finally, the authors design the key techniques and security mechanism to be simple in implementation for the easy incorporation of the mechanism into the existing P2P overlay network. Through theoretical and experimental analysis, the SFA-based trust model shows remarkable enhancement in scalability for largescale P2P computing, as well as has greater adaptability and accuracy in handling various dynamic behaviors of peers.
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