72,355 research outputs found
A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense
Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric
field of attack and defense, and shuffling-based MTD has been regarded as one
of the most effective ways to mitigate DDoS attacks. However, previous work
does not acknowledge that frequent shuffles would significantly intensify the
overhead. MTD requires a quantitative measure to compare the cost and
effectiveness of available adaptations and explore the best trade-off between
them. In this paper, therefore, we propose a new cost-effective shuffling
method against DDoS attacks using MTD. By exploiting Multi-Objective Markov
Decision Processes to model the interaction between the attacker and the
defender, and designing a cost-effective shuffling algorithm, we study the best
trade-off between the effectiveness and cost of shuffling in a given shuffling
scenario. Finally, simulation and experimentation on an experimental software
defined network (SDN) indicate that our approach imposes an acceptable
shuffling overload and is effective in mitigating DDoS attacks
Reputation and cooperation in defence
Surprisingly high levels of within-group cooperation are observed in conflict situations. Ex-
periments confirm that external threats lead to higher cooperation. The psychological literature
suggests proximate explanations in the form of group processes, but does not explain how these
processes can evolve and persist. We provide an ultimate explanation, in which cooperation is rational response to an external threat. We introduce a model in which groups vary in their willing-
ness to help each other against external attackers. Attackers infer cooperativeness of groups from
members’ behaviour under attack, and may be deterred by a group that bands together against an
initial attack. Then, even self-interested individuals may defend each other when threatened in
order to deter future attacks. We argue that a group’s reputation is a public good with a natural
weakest-link structure. We extend the model to cooperative and altruistic behaviour in general
Moving Target Defense Using Live Migration of Docker Containers
abstract: Today the information technology systems have addresses, software stacks and other configuration remaining unchanged for a long period of time. This paves way for malicious attacks in the system from unknown vulnerabilities. The attacker can take advantage of this situation and plan their attacks with sufficient time. To protect our system from this threat, Moving Target Defense is required where the attack surface is dynamically changed, making it difficult to strike.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.Dissertation/ThesisMasters Thesis Computer Science 201
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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