896,310 research outputs found
Introduction to Security Onion
Security Onion is a Network Security Manager (NSM) platform that provides multiple Intrusion Detection Systems (IDS) including Host IDS (HIDS) and Network IDS (NIDS). Many types of data can be acquired using Security Onion for analysis. This includes data related to: Host, Network, Session, Asset, Alert and Protocols. Security Onion can be implemented as a standalone deployment with server and sensor included or with a master server and multiple sensors allowing for the system to be scaled as required. Many interfaces and tools are available for management of the system and analysis of data such as Sguil, Snorby, Squert and Enterprise Log Search and Archive (ELSA). These interfaces can be used for analysis of alerts and captured events and then can be further exported for analysis in Network Forensic Analysis Tools (NFAT) such as NetworkMiner, CapME or Xplico. The Security Onion platform also provides various methods of management such as Secure SHell (SSH) for management of server and sensors and Web client remote access. All of this with the ability to replay and analyse example malicious traffic makes the Security Onion a suitable low cost alternative for Network Security Management. In this paper, we have a feature and functionality review for the Security Onion in terms of: types of data, configuration, interface, tools and system management
Evaluation of Network Architecture and Its Implication on Connectivity and Data Security
Networking offers the framework to congregate largely heterogeneous entities so that they can communicate. In this paper we review aspects of Network architectural design that aims to ensure connectivity and data security for network users. Security protocols like the Internet Protocol Security (IPsec) ensures data security for users of a Virtual Private Network which provides encryption, tunneling and authentication services. Virtual Local Area Networks plays a role in network management and security. Access Control lists provides an overview of rights granted to users to access network resources thereby reducing incidence of hacking to the minimum. Combining these techniques in a network would ensure uninterrupted service and data security to network users
A Review Paper on Security of Wireless Network
In the past few years, wireless networks, specifically those based on the IEEE 802.11 Standard, have experienced tremendous growth. A team at Rice University recovered the 802.11 Wired Equivalent Privacy 128-bit security key which is used by an active network. This Standard has increased the interest and attention of many researchers in recent years. The IEEE 802.11 is a family of standards, which defines and specifies the parts of the standard. This paper explains the survey on the latest development in how to secure an 802.11 wireless network by understanding its security protocols and mechanism. In order to fix security loopholes a public key authentication and key-establishment procedure has been proposed which fixes security loopholes in current standard. The public key cryptosystem is used to establish a session key securely between the client and Access point. Knowing how these mechanism and protocols works, including its weakness and vulnerabilities can be very helpful for planning, designing, implementing and/or hardening a much secure wireless network, effectively minimizing the impact of an attack. The methods used in current research are especially emphasized to analysis the technique of securing 802.11 standards. Finally, in this paper we pointed out some possible future directions of research
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions
In recent years, low-carbon transportation has become an indispensable part
as sustainable development strategies of various countries, and plays a very
important responsibility in promoting low-carbon cities. However, the security
of low-carbon transportation has been threatened from various ways. For
example, denial of service attacks pose a great threat to the electric vehicles
and vehicle-to-grid networks. To minimize these threats, several methods have
been proposed to defense against them. Yet, these methods are only for certain
types of scenarios or attacks. Therefore, this review addresses security aspect
from holistic view, provides the overview, challenges and future directions of
cyber security technologies in low-carbon transportation. Firstly, based on the
concept and importance of low-carbon transportation, this review positions the
low-carbon transportation services. Then, with the perspective of network
architecture and communication mode, this review classifies its typical attack
risks. The corresponding defense technologies and relevant security suggestions
are further reviewed from perspective of data security, network management
security and network application security. Finally, in view of the long term
development of low-carbon transportation, future research directions have been
concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable
Energy Review
A Review of Intrusion Detection using Deep Learning
As network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The development of new types of intruders poses a serious threat to network security: although many tools for network security have been developed, the rapid growth of intrusion activity remains a serious problem. Intrusion Detection Systems (IDS) are used to detect intrusive network activity. Preventing and detecting unauthorized access to a computer is an IT security concern. Therefore, network security provides a measure of the level of prevention and detection that can be used to avoid suspicious users. Deep learning has been used extensively in recent years to improve network intruder detection. These techniques allow for automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Network Traffic Matrix (TM) prediction is defined as the problem of
estimating future network traffic from the previous and achieved network
traffic data. It is widely used in network planning, resource management and
network security. Long Short-Term Memory (LSTM) is a specific recurrent neural
network (RNN) architecture that is well-suited to learn from experience to
classify, process and predict time series with time lags of unknown size. LSTMs
have been shown to model temporal sequences and their long-range dependencies
more accurately than conventional RNNs. In this paper, we propose a LSTM RNN
framework for predicting short and long term Traffic Matrix (TM) in large
networks. By validating our framework on real-world data from GEANT network, we
show that our LSTM models converge quickly and give state of the art TM
prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with
arXiv:1402.1128 by other author
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities
Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy
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