Reliable algorithms and attack detection schemes for network systems

Network and Information security and reliability is still a key issue in information technology. This thesis develops two algorithms to improve the reliability and stability of content delivery systems, and proposes three attack detection schemes with high effectiveness and accuracy in detecting network attacks.

Enhancing RFID tag resistance against cloning attack

In its current form, RFID system are susceptible to a range of malevolent attacks. With the rich business intelligence that RFID infrastructure could possibly carry, security is of paramount importance. In this paper, we formalise various threat models due tag cloning on the RFID system. We also present a simple but efficient and cost effect technique that strengthens the resistance of RFID tags to cloning attacks. Our techniques can even strengthen tags against cloning in environments with untrusted reading devices.

Secure localization with attack detection in wireless sensor networks

Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.

Attack vectors against social networking systems : the Facebook example

Social networking systems (SNS’s) such as Facebook are an ever evolving and developing means of social interaction, which is not only being used to disseminate information to family, friends and colleagues but as a way of meeting and interacting with "strangers" through the advent of a large number of social applications. The attractiveness of such software has meant a dramatic increase in the number of frequent users of SNS’s and the threats which were once common to the Internet have now been magnified, intensified and altered as the potential for criminal behaviour on SNS’s increases. Social networking sites including Facebook contain a vast amount of personal information, that if obtained could be used for other purposes or to carry out other crimes such as identity theft. This paper will focus on the security threats posed to social networking sites and gain an understanding of these risks by using a security approach known as “attack trees”. This will allow for a greater understanding of the complexity associated with protecting Social Networking systems with a particular focus on Facebook.

Discriminating DDoS attack traffic from flash crowd through packet arrival patterns

Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission strategies and various forms of attack packets to beat defense systems. These problems lead to defense systems requiring various detection methods in order to identify attacks. Moreover, DDoS attacks can mix their traffics during flash crowds. By doing this, the complex defense system cannot detect the attack traffic in time. In this paper, we propose a behavior based detection that can discriminate DDoS attack traffic from traffic generated by real users. By using Pearson's correlation coefficient, our comparable detection methods can extract the repeatable features of the packet arrivals. The extensive simulations were tested for the accuracy of detection. We then performed experiments with several datasets and our results affirm that the proposed method can differentiate traffic of an attack source from legitimate traffic with a quick response. We also discuss approaches to improve our proposed methods at the conclusion of this paper.

Air pressure and wind velocity - modelling ember attack within the urban-rural interface

As population change places pressure on expanding regional and metropolitan urban boundaries, so the threat of bushfire at the rural/urban interface increases. This paper presents a range of 2D and 3D 1:40 and full scale modelling investigations. Various relationships are explored between the urban and rural interface with respect to: air pressure; changes in wind pattern; vectorial velocity; and the deposition of hot ash and firebrand deposits around single story building forms, both as standalone and within an orthogonal array and cul-de-sac relationships.

A survey on latest botnet attack and defense

A botnet is a group of compromised computers, which are remotely controlled by hackers to launch various network attacks, such as DDoS attack and information phishing. Botnet has become a popular and productive tool behind many cyber attacks. Recently, the owners of some botnets, such as storm worm, torpig and conflicker, are employing fluxing techniques to evade detection. Therefore, the understanding of their fluxing tricks is critical to the success of defending from botnet attacks. Motivated by this, we survey the latest botnet attacks and defenses in this paper. We begin with introducing the principles of fast fluxing (FF) and domain fluxing (DF), and explain how these techniques were employed by botnet owners to fly under the radar. Furthermore, we investigate the state-of-art research on fluxing detection. We also compare and evaluate those fluxing detection methods by multiple criteria. Finally, we discuss future directions on fighting against botnet based attacks.

Attack vector analysis and privacy-preserving social network data publishing

This paper addresses the problem of privacy-preserving data publishing for social network. Research on protecting the privacy of individuals and the confidentiality of data in social network has recently been receiving increasing attention. Privacy is an important issue when one wants to make use of data that involves individuals' sensitive information, especially in a time when data collection is becoming easier and sophisticated data mining techniques are becoming more efficient. In this paper, we discuss various privacy attack vectors on social networks. We present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This study provides a summary of the current state-of-the-art, based on which we expect to see advances in social networks data publishing for years to come.

Improvement of DDoS attack detection and web access anonymity

The thesis has covered a range of algorithms that help to improve the security of web services. The research focused on the problems of DDoS attack and traffic analysis attack against service availability and information privacy respectively. Finally, this research significantly advantaged DDoS attack detection and web access anonymity.

Trust mechanisms in wireless sensor networks : attack analysis and countermeasures

As the trust issue in wireless sensor networks is emerging as one important factor in security schemes, it is necessary to analyze how to resist attacks with a trust scheme. In this paper we categorize various types of attacks and countermeasures related to trust schemes in WSNs. Furthermore, we provide the development of trust mechanisms, give a short summarization of classical trust methodologies and emphasize the challenges of trust scheme in WSNs. An extensive literature survey is presented by summarizing state-of-the-art trust mechanisms in two categories: secure routing and secure data. Based on the analysis of attacks and the existing research, an open field and future direction with trust mechanisms in WSNs is provided.

Web application protection against SQL injection attack

SQL injection vulnerabilities poses a severe threat to web applications as an SQL Injection Attack (SQLIA) could adopt new obfuscation techniques to evade and thwart countermeasures such as Intrusion Detection Systems (IDS). SQLIA gains access to the back-end database of vulnerable websites, allowing hackers to execute SQL commands in a web application resulting in financial fraud and website defacement. The lack of existing models in providing protections against SQL injection has motivated this paper to present a new and enhanced model against web database intrusions that use SQLIA techniques. In this paper, we propose a novel concept of negative tainting along with SQL keyword analysis for preventing SQLIA and described our that we implemented. We have tested our proposed model on all types of SQLIA techniques by generating SQL queries containing legitimate SQL commands and SQL Injection Attack. Evaluations have been performed using three different applications. The results show that our model protects against 100% of tested attacks before even reaching the database layer.

CBF : A packet filtering method for DDoS attack defense in cloud environment

Distributed Denial-of-Service attack (DDoS) is a major threat for cloud environment. Traditional defending approaches cannot be easily applied in cloud security due to their relatively low efficiency, large storage, to name a few. In view of this challenge, a Confidence-Based Filtering method, named CBF, is investigated for cloud computing environment, in this paper. Concretely speaking, the method is deployed by two periods, i.e., non-attack period and attack period. More specially, legitimate packets are collected at non-attack period, for extracting attribute pairs to generate a nominal profile. With the nominal profile, the CBF method is promoted by calculating the score of a particular packet at attack period, to determine whether to discard it or not. At last, extensive simulations are conducted to evaluate the feasibility of the CBF method. The result shows that CBF has a high scoring speed, a small storage requirement and an acceptable filtering accuracy, making it suitable for real-time filtering in cloud environment.

Patchwork-based stereo audio watermarking against de-synchronization attack

Audio watermarking schemes using patchworkbased algorithm have good robustness against majority of the watermarking attacks. However, these watermarking schemes are vulnerable to de-synchronization attack. This paper proposes a patchwork-based watermarking scheme for stereo audio signals to address this problem. To improve the robustness, the proposed method exploits the similarities of both channels in the stereo audio signals. Given a stereo audio signal, we first compute the discrete cosine transform (DCT) of both channels, which gives two sets of DCT coefficients. Then DCT segments are formed form DCT coefficients belong to a certain frequency range. The DCT segment formation is determined by a pseudonoise (PN) sequence which acts as a secret key. Then watermark bits are embedded into DCT segments by modifying the DCT coefficients. In the decoding process the secret key is used to extract the watermark bits embedded in the DCT segments. Simulation results illustrate the effectiveness of the proposed method against de-synchronization attack, compared to latest patchwork-based audio watermarking scheme. Besides, the proposed algorithm also gives better robustness against other conventional attacks.

A modified moment-based image watermarking method robust to cropping attack

Developing a watermarking method that is robust to cropping attack is a challenging task in image watermarking. The moment-based watermarking schemes show good robustness to common signal processing attacks and some geometric attacks but are sensitive to cropping attack. In this paper, we modify the moment-based approach to deal with cropping attack. Firstly, we find the probability density function (pdf) of the pixel value distribution from the original image. Secondly, we reshape and normalize the pdf of the pixel value distribution (PPVD) to form a two dimensional image. Then, the moment invariants are calculated from the PPVD image. Since PPVD is insensitive to cropping, the proposed method is robust to cropping attack. Besides, it also has high robustness against other common attacks. Experimental results demonstrate the effectiveness of the proposed method.

An attack-resistant hybrid data-privatization method with low information loss

We examine a recent proposal for data-privatization by testing it against well-known attacks, we show that all of these attacks successfully retrieve a relatively large (and unacceptable) portion of the original data. We then indicate how the data-privatization method examined can be modified to assist it to withstand these attacks and compare the performance of the two approaches. We also show that the new method has better privacy and lower information loss than the former method.