4,666 research outputs found
Preventing DDoS using Bloom Filter: A Survey
Distributed Denial-of-Service (DDoS) is a menace for service provider and
prominent issue in network security. Defeating or defending the DDoS is a prime
challenge. DDoS make a service unavailable for a certain time. This phenomenon
harms the service providers, and hence, loss of business revenue. Therefore,
DDoS is a grand challenge to defeat. There are numerous mechanism to defend
DDoS, however, this paper surveys the deployment of Bloom Filter in defending a
DDoS attack. The Bloom Filter is a probabilistic data structure for membership
query that returns either true or false. Bloom Filter uses tiny memory to store
information of large data. Therefore, packet information is stored in Bloom
Filter to defend and defeat DDoS. This paper presents a survey on DDoS
defending technique using Bloom Filter.Comment: 9 pages, 1 figure. This article is accepted for publication in EAI
Endorsed Transactions on Scalable Information System
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Hardware Acceleration of Network Intrusion Detection System Using FPGA
This thesis presents new algorithms and hardware designs for Signature-based Network Intrusion Detection System (SB-NIDS) optimisation exploiting a hybrid hardwaresoftware co-designed embedded processing platform. The work describe concentrates
on optimisation of a complete SB-NIDS Snort application software on a FPGA based
hardware-software target rather than on the implementation of a single functional unit
for hardware acceleration. Pattern Matching Hardware Accelerator (PMHA) based on
Bloom filter was designed to optimise SB-NIDS performance for execution on a Xilinx
MicroBlaze soft-core processor. The Bloom filter approach enables the potentially large
number of network intrusion attack patterns to be efficiently represented and searched
primarily using accesses to FPGA on-chip memory. The thesis demonstrates, the viability of hybrid hardware-software co-designed approach for SB-NIDS. Future work is
required to investigate the effects of later generation FPGA technology and multi-core
processors in order to clearly prove the benefits over conventional processor platforms
for SB-NIDS.
The strengths and weaknesses of the hardware accelerators and algorithms are analysed,
and experimental results are examined to determine the effectiveness of the implementation. Experimental results confirm that the PMHA is capable of performing network
packet analysis for gigabit rate network traffic. Experimental test results indicate that
our SB-NIDS prototype implementation on relatively low clock rate embedded processing platform performance is approximately 1.7 times better than Snort executing on
a general purpose processor on PC when comparing processor cycles rather than wall
clock time
Multiple pattern matching for network security applications: Acceleration through vectorization (pre-print version)
As both new network attacks emerge and network traffic increases in volume, the need to perform network traffic inspection at high rates is ever increasing. The core of many security applications that inspect network traffic (such as Network Intrusion Detection) is pattern matching. At the same time, pattern matching is a major performance bottleneck for those applications: indeed, it is shown to contribute to more than 70% of the total running time of Intrusion Detection Systems. Although numerous efficient approaches to this problem have been proposed on custom hardware, it is challenging for pattern matching algorithms to gain benefit from the advances in commodity hardware. This becomes even more relevant with the adoption of Network Function Virtualization, that moves network services, such as Network Intrusion Detection, to the cloud, where scaling on commodity hardware is key for performance. In this paper, we tackle the problem of pattern matching and show how to leverage the architecture features found in commodity platforms. We present efficient algorithmic designs that achieve good cache locality and make use of modern vectorization techniques to utilize data parallelism within each core. We first identify properties of pattern matching that make it fit for vectorization and show how to use them in the algorithmic design. Second, we build on an earlier, cache-aware algorithmic design and show how we apply cache-locality combined with SIMD gather instructions to pattern matching. Third, we complement our algorithms with an analytical model that predicts their performance and that can be used to easily evaluate alternative designs. We evaluate our algorithmic design with open data sets of real-world network traffic: Our results on two different platforms, Haswell and Xeon-Phi, show a speedup of 1.8x and 3.6x, respectively, over Direct Filter Classification (DFC), a recently proposed algorithm by Choi et al. for pattern matching exploiting cache locality, and a speedup of more than 2.3x over Aho–Corasick, a widely used algorithm in today\u27s Intrusion Detection Systems. Finally, we utilize highly parallel hardware platforms, evaluate the scalability of our algorithms and compare it to parallel implementations of DFC and Aho–Corasick, achieving processing throughput of up to 45Gbps and close to 2 times higher throughput than Aho–Corasick
Security Applications of GPUs
Despite the recent advances in software security hardening techniques, vulnerabilities can always be exploited if the attackers are really determined. Regardless the protection enabled, successful exploitation can always be achieved, even though admittedly, today, it is much harder than it was in the past. Since securing software is still under ongoing research, the community investigates detection methods in order to protect software. Three of the most promising such methods are monitoring the (i) network, (ii) the filesystem, and (iii) the host memory, for possible exploitation. Whenever a malicious operation is detected then the monitor should be able to terminate it and/or alert the administrator. In this chapter, we explore how to utilize the highly parallel capabilities of modern commodity graphics processing units (GPUs) in order to improve the performance of different security tools operating at the network, storage, and memory level, and how they can offload the CPU whenever possible. Our results show that modern GPUs can be very efficient and highly effective at accelerating the pattern matching operations of network intrusion detection systems and antivirus tools, as well as for monitoring the integrity of the base computing systems
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
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