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Alessio Bonti

Bio: Alessio Bonti is an academic researcher from Deakin University. The author has contributed to research in topics: Computer science & Routing protocol. The author has an hindex of 6, co-authored 13 publications receiving 340 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper recreate some of the current attacks that attackers may initiate as HTTP and XML, and introduces the use of a back propagation neutral network, called Cloud Protector, which was trained to detect and filter such attack traffic.

239 citations

Journal ArticleDOI
Longxiang Gao1, Ming Li1, Alessio Bonti1, Wanlei Zhou1, Shui Yu1 
TL;DR: A multidimensional routing protocol (M-Dimension) for the human-associated delay-tolerant networks which uses local information derived from multiple dimensions to identify a mobile node more accurately and significantly increases the average success ratio with a competitive end-to-end delay when compared with other multicast DTNs routing protocols.
Abstract: Human-associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and can be viewed from multiple dimensions including geographic and social aspects. The combination of these different dimensions enables us to comprehend delay-tolerant networks and consequently use this multidimensional information to improve overall network efficiency. Alongside the geographic dimension of the network, which is concerned with geographic topology of routing, social dimensions such as social characters can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network. We propose a multidimensional routing protocol (M-Dimension) for the human-associated delay-tolerant networks which uses local information derived from multiple dimensions to identify a mobile node more accurately. The importance of each dimension has been measured by the weight function and it is used to calculate the best route. The greedy routing strategy is applied to select an intermediary node to forward message. We compare M-Dimension to the existing benchmark routing protocols via MIT reality Data Set and INFOCOM 2006 Data Set, which are real human-associated mobile network trace files. The results of our simulations show that M-Dimension significantly increases the average success ratio with a competitive end-to-end delay when compared with other multicast DTNs routing protocols.

34 citations

Journal Article
Fasheng Yi, Shui Yu, Wanlei Zhou, Jing Hai, Alessio Bonti1 
TL;DR: A novel filtering scheme based on source information in this paper to defend against various source IP address spoofing, which works independently at the potential victim side, and accumulates the source information of its clients.
Abstract: IP address spoofing is employed by a lot of DDoS attack tools. Most of the current research on DDoS attack packet filtering depends on cooperation among routers, which is hard to achieve in real campaigns. Therefore, in the paper, we propose a novel filtering scheme based on source information in this paper to defend against various source IP address spoofing. The proposed method works independently at the potential victim side, and accumulates the source information of its clients, for instance, source IP addresses, hops from the server during attacks free period. When a DDoS attack alarm is raised, we can filter out the attack packets based on the accumulated knowledge of the legitimate clients. We divide the source IP addresses into n (1 ≤ n ≤ 32) segments in our proposed algorithm; as a result, we can therefore release the challenge storage and speed up the procedure of information retrieval. The system which is proposed by us and the experiments indicated that the proposed method works effectively and efficiently.

29 citations

Journal ArticleDOI
TL;DR: A comprehensive, integrated, and interactive application for financial planning, which defines a person's life in terms of financial items and highlights the interactions among individualfinancial items and their impact on personal wealth.
Abstract: An increasingly complex landscape of financial products, in combination with a falling provision of social security, increased life expectancy, and persisting financial illiteracy, calls for a reevaluation of personal financial planning. Currently, personal financial advice is offered predominantly from an institution's point of view, neglecting the personal point of view. We introduce a comprehensive, integrated, and interactive application for financial planning, which defines a person's life in terms of financial items and highlights the interactions among individual financial items and their impact on personal wealth. As individuals make decisions, their financial items interact according to a set of rules. Over time, events occur and global influences determine the growth rates of financial items. Our financial life model, which is accompanied by an interactive visualization and comprehensive analytics, enables financial planning in terms of life choices. Consequently, financial planning will occur from a personal point of view rather than a corporate perspective. This paper introduces our Smarter Financial Life project as well as an early gamified prototype, which has been presented at an Australian conference.

28 citations

Journal ArticleDOI
Longxiang Gao1, Ming Li1, Alessio Bonti1, Wanlei Zhou1, Shui Yu1 
TL;DR: A multi-dimensional routing protocol (M-Dimension) for the human associated delay-tolerant network which uses the local information derived from multiple dimensions to identify a mobile node more accurately and is very competitive when End-to-End Delay of packet delivery is used in comparison to other multi-cast DTN routing protocols.

12 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 2012

3,692 citations

Journal ArticleDOI
TL;DR: This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject and proposes a taxonomy for their classification, addressing several key topics, namely vulnerabilities, threats, and attacks.
Abstract: In the last few years, the appealing features of cloud computing have been fueling the integration of cloud environments in the industry, which has been consequently motivating the research on related technologies by both the industry and the academia. The possibility of paying-as-you-go mixed with an on-demand elastic operation is changing the enterprise computing model, shifting on-premises infrastructures to off-premises data centers, accessed over the Internet and managed by cloud hosting providers. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. Besides of the issues derived from Web technologies and the Internet, clouds introduce new issues that should be cleared out first in order to further allow the number of cloud deployments to increase. This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject. It addresses several key topics, namely vulnerabilities, threats, and attacks, proposing a taxonomy for their classification. It also contains a thorough review of the main concepts concerning the security state of cloud environments and discusses several open research topics.

423 citations

Journal ArticleDOI
TL;DR: Two new information metrics such as the generalized entropy metric and the information distance metric are proposed to detect low-rate DDoS attacks by measuring the difference between legitimate traffic and attack traffic.
Abstract: A low-rate distributed denial of service (DDoS) attack has significant ability of concealing its traffic because it is very much like normal traffic. It has the capacity to elude the current anomaly-based detection schemes. An information metric can quantify the differences of network traffic with various probability distributions. In this paper, we innovatively propose using two new information metrics such as the generalized entropy metric and the information distance metric to detect low-rate DDoS attacks by measuring the difference between legitimate traffic and attack traffic. The proposed generalized entropy metric can detect attacks several hops earlier (three hops earlier while the order α = 10 ) than the traditional Shannon metric. The proposed information distance metric outperforms (six hops earlier while the order α = 10) the popular Kullback-Leibler divergence approach as it can clearly enlarge the adjudication distance and then obtain the optimal detection sensitivity. The experimental results show that the proposed information metrics can effectively detect low-rate DDoS attacks and clearly reduce the false positive rate. Furthermore, the proposed IP traceback algorithm can find all attacks as well as attackers from their own local area networks (LANs) and discard attack traffic.

351 citations

Journal ArticleDOI
TL;DR: An extensive review on cloud computing with the main focus on gaps and security concerns is presented, which identifies the top security threats and their existing solutions and investigates the challenges/obstacles in implementing threat remediation.

288 citations