289 research outputs found
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection
Enterprise networks that host valuable assets and services are popular and
frequent targets of distributed network attacks. In order to cope with the
ever-increasing threats, industrial and research communities develop systems
and methods to monitor the behaviors of their assets and protect them from
critical attacks. In this paper, we systematically survey related research
articles and industrial systems to highlight the current status of this arms
race in enterprise network security. First, we discuss the taxonomy of
distributed network attacks on enterprise assets, including distributed
denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing
methods in monitoring and classifying network behavior of enterprise hosts to
verify their benign activities and isolate potential anomalies. Third,
state-of-the-art detection methods for distributed network attacks sourced from
external attackers are elaborated, highlighting their merits and bottlenecks.
Fourth, as programmable networks and machine learning (ML) techniques are
increasingly becoming adopted by the community, their current applications in
network security are discussed. Finally, we highlight several research gaps on
enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive
Know Your Enemy: Stealth Configuration-Information Gathering in SDN
Software Defined Networking (SDN) is a network architecture that aims at
providing high flexibility through the separation of the network logic from the
forwarding functions. The industry has already widely adopted SDN and
researchers thoroughly analyzed its vulnerabilities, proposing solutions to
improve its security. However, we believe important security aspects of SDN are
still left uninvestigated. In this paper, we raise the concern of the
possibility for an attacker to obtain knowledge about an SDN network. In
particular, we introduce a novel attack, named Know Your Enemy (KYE), by means
of which an attacker can gather vital information about the configuration of
the network. This information ranges from the configuration of security tools,
such as attack detection thresholds for network scanning, to general network
policies like QoS and network virtualization. Additionally, we show that an
attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk
of being detected. We underline that the vulnerability exploited by the KYE
attack is proper of SDN and is not present in legacy networks. To address the
KYE attack, we also propose an active defense countermeasure based on network
flows obfuscation, which considerably increases the complexity for a successful
attack. Our solution offers provable security guarantees that can be tailored
to the needs of the specific network under consideratio
Distributed, multi-level network anomaly detection for datacentre networks
Over the past decade, numerous systems have been proposed to detect and subsequently prevent or mitigate security vulnerabilities. However, many existing intrusion or anomaly detection solutions are limited to a subset of the traffic due to scalability issues, hence failing to operate at line-rate on large, high-speed datacentre networks. In this paper, we present a two-level solution for anomaly detection leveraging independent execution and message passing semantics. We employ these constructs within a network-wide distributed anomaly detection framework that allows for greater detection accuracy and bandwidth cost saving through attack path reconstruction. Experimental results using real operational traffic traces and known network attacks generated through the Pytbull IDS evaluation framework, show that our approach is capable of detecting anomalies in a timely manner while allowing reconstruction of the attack path, hence further enabling the composition of advanced mitigation strategies. The resulting system shows high detection accuracy when compared to similar techniques, at least 20% better at detecting anomalies, and enables full path reconstruction even at small-to-moderate attack traffic intensities (as a fraction of the total traffic), saving up to 75% of bandwidth due to early attack detection
A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
Cybersecurity attacks are becoming increasingly sophisticated and pose a
growing threat to individuals, and private and public sectors. Distributed
Denial of Service attacks are one of the most harmful of these threats in
today's internet, disrupting the availability of essential services. This
project presents a novel deep learning-based approach for detecting DDoS
attacks in network traffic using the industry-recognized DDoS evaluation
dataset from the University of New Brunswick, which contains packet captures
from real-time DDoS attacks, creating a broader and more applicable model for
the real world. The algorithm employed in this study exploits the properties of
Convolutional Neural Networks (CNN) and common deep learning algorithms to
build a novel mitigation technique that classifies benign and malicious
traffic. The proposed model preprocesses the data by extracting packet flows
and normalizing them to a fixed length which is fed into a custom architecture
containing layers regulating node dropout, normalization, and a sigmoid
activation function to out a binary classification. This allows for the model
to process the flows effectively and look for the nodes that contribute to DDoS
attacks while dropping the "noise" or the distractors. The results of this
study demonstrate the effectiveness of the proposed algorithm in detecting DDOS
attacks, achieving an accuracy of .9883 on 2000 unseen flows in network
traffic, while being scalable for any network environment
Tennison: A Distributed SDN Framework for Scalable Network Security
Despite the relative maturity of the Internet, the computer networks of today are still susceptible to attack. The necessary distributed nature of networks for wide area connectivity has traditionally led to high cost and complexity in designing and implementing secure networks. With the introduction of software-defined networks (SDNs) and network functions virtualization, there are opportunities for efficient network threat detection and protection. SDN's global view provides a means of monitoring and defense across the entire network. However, current SDN-based security systems are limited by a centralized framework that introduces significant control plane overhead, leading to the saturation of vital control links. In this paper, we introduce TENNISON, a novel distributed SDN security framework that combines the efficiency of SDN control and monitoring with the resilience and scalability of a distributed system. TENNISON offers effective and proportionate monitoring and remediation, compatibility with widely available networking hardware, support for legacy networks, and a modular and extensible distributed design. We demonstrate the effectiveness and capabilities of the TENNISON framework through the use of four attack scenarios. These highlight multiple levels of monitoring, rapid detection, and remediation, and provide a unique insight into the impact of multiple controllers on network attack detection at scale
A distributed cyber-security framework for heterogeneous environments
Evolving business models, computing paradigms, and management practices are rapidly re-shaping the usage models of ICT infrastructures, and demanding for more flexibility and dynamicity in enterprise security, beyond the traditional "security perimeter" approach. Since valuable ICT assets cannot be easily enclosed within a trusted physical sandbox any more, there is an increasing need for a new generation of pervasive and capillary cyber-security paradigms over distributed and geographically-scattered systems. Following the generalized trend towards virtualization, automation, software-definition, and hardware/software disaggregation, in this paper we elaborate on a multi-tier architecture made of a common, programmable, and pervasive data-plane and a powerful set of multi-vendor detection and analysis algorithms. Our approach leverages the growing level of programmability of ICT infrastructures to create a common and unified framework that could be used to monitor and protect distributed heterogeneous environments, including legacy enterprise networks, IoT installations, and virtual resources deployed in the cloud
Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art
Software-Defined Networking (SDN) is an evolutionary networking paradigm
which has been adopted by large network and cloud providers, among which are
Tech Giants. However, embracing a new and futuristic paradigm as an alternative
to well-established and mature legacy networking paradigm requires a lot of
time along with considerable financial resources and technical expertise.
Consequently, many enterprises can not afford it. A compromise solution then is
a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN
functionalities are leveraged while existing traditional network
infrastructures are acknowledged. Recently, hSDN has been seen as a viable
networking solution for a diverse range of businesses and organizations.
Accordingly, the body of literature on hSDN research has improved remarkably.
On this account, we present this paper as a comprehensive state-of-the-art
survey which expands upon hSDN from many different perspectives
On the placement of security-related Virtualised Network Functions over data center networks
Middleboxes are typically hardware-accelerated appliances such as firewalls, proxies, WAN optimizers, and NATs that play an important role in service provisioning over today's data centers. Reports show that the number of middleboxes is on par with the number of routers, and consequently represent a significant commitment from an operator's capital and operational expenditure budgets. Over the past few years, software middleboxes known as Virtual Network Functions (VNFs) are replacing the hardware appliances to reduce cost, improve the flexibility of deployment, and allow for extending network functionality in short timescales.
This dissertation aims at identifying the unique characteristics of security modules implementation as VNFs in virtualised environments. We focus on the placement of the security VNFs to minimise resource usage without violating the security imposed constraints as a challenge faced by operators today who want to increase the usable capacity of their infrastructures. The work presented here, focuses on the multi-tenant environment where customised security services are provided to tenants. The services are implemented as a software module deployed as a VNF collocated with network switches to reduce overhead. Furthermore, the thesis presents a formalisation for the resource-aware placement of security VNFs and provides a constraint programming solution along with examining heuristic, meta-heuristic and near-optimal/subset-sum solutions to solve larger size problems in reduced time.
The results of this work identify the unique and vital constraints of the placement of security functions. They demonstrate that the granularity of the traffic required by the security functions imposes traffic constraints that increase the resource overhead of the deployment. The work identifies the north-south traffic in data centers as the traffic designed for processing for security functions rather than east-west traffic. It asserts that the non-sharing strategy of security modules will reduce the complexity in case of the multi-tenant environment. Furthermore, the work adopts on-path deployment of security VNF traffic strategy, which is shown to reduce resources overhead compared to previous approaches
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