Biblio

The modern malware poses serious security threats because of its evolved capability of using staged and persistent attack while remaining undetected over a long period of time to perform a number of malicious activities. The challenge for malicious actors is to gain initial control of the victim's machine by bypassing all the security controls. The most favored bait often used by attackers is to deceive users through a trusting or interesting email containing a malicious attachment or a malicious link. To make the email credible and interesting the cybercriminals often perform reconnaissance activities to find background information on the potential target. To this end, the value of information found on the discarded or stolen storage devices is often underestimated or ignored. In this paper, we present the partial results of analysis of one such hard disk that was purchased from the open market. The data found on the disk contained highly sensitive personal and organizational data. The results from the case study will be useful in not only understanding the involved risk but also creating awareness of related threats.

Security analysts implement various security mechanisms to protect systems from attackers. Even though these mechanisms try to secure systems, a talented attacker may use these same techniques to launch a sophisticated attack. This paper discuss about such an attack called as user account Denial of Service (DoS) where an attacker uses user account lockout features of the application to lockout all user accounts causing an enterprise wide DoS. The attack has being simulated usingastealthy attack mechanism called as Advanced Persistent Threats (APT) using a XMPP based botnet. Through the simulation, researchers discuss about the patterns associated with the attack which can be used to detect the attack in real time and how the attack can be prevented from the perspective of developers, system engineers and security analysts.

Advanced Persistent Threat (APT), unlike traditional hacking attempts, carries out specific attacks on a specific target to illegally collect information and data from it. These targeted attacks use special-crafted malware and infrequent activity to avoid detection, so that hackers can retain control over target systems unnoticed for long periods of time. In order to detect these stealthy activities, a large-volume of traffic data generated in a period of time has to be analyzed. We proposed a scalable solution, Ctracer to detect stealthy command and control channel in a large-volume of traffic data. APT uses multiple command and control (C&C) channel and change them frequently to avoid detection, but there are common signatures in those C&C sessions. By identifying common network signature, Ctracer is able to group the C&C sessions. Therefore, we can detect an APT and all the C&C session used in an APT attack. The Ctracer is evaluated in a large enterprise for four months, twenty C&C servers, three APT attacks are reported. After investigated by the enterprise's Security Operations Center (SOC), the forensic report shows that there is specific enterprise targeted APT cases and not ever discovered for over 120 days.

The Domain Name System (DNS) is widely seen as a vital protocol of the modern Internet. For example, popular services like load balancers and Content Delivery Networks heavily rely on DNS. Because of its important role, DNS is also a desirable target for malicious activities such as spamming, phishing, and botnets. To protect networks against these attacks, a number of DNS-based security approaches have been proposed. The key insight of our study is to measure the effectiveness of security approaches that rely on DNS in large-scale networks. For this purpose, we answer the following questions, How often is DNS used? Are most of the Internet flows established after contacting DNS? In this study, we collected data from the University of Auckland campus network with more than 33,000 Internet users and processed it to find out how DNS is being used. Moreover, we studied the flows that were established with and without contacting DNS. Our results show that less than 5 percent of the observed flows use DNS. Therefore, we argue that those security approaches that solely depend on DNS are not sufficient to protect large-scale networks.

Cover time measures the time (or number of steps) required for a mobile agent to visit each node in a network (graph) at least once. A short cover time is important for search or foraging applications that require mobile agents to quickly inspect or monitor nodes in a network, such as providing situational awareness or security. Speed can be achieved if details about the graph are known or if the agent maintains a history of visited nodes, however, these requirements may not be feasible for agents with limited resources, they are difficult in dynamic graph topologies, and they do not easily scale to large networks. This paper introduces a set-based form of heading (directional bias) that allows an agent to more efficiently explore any connected graph, static or dynamic. When deciding the next node to visit, agents are discouraged from visiting nodes that neighbor both their previous and current locations. Modifying a traditional movement method, e.g., random walk, with this concept encourages an agent to move toward nodes that are less likely to have been previously visited, reducing cover time. Simulation results with grid, scale-free, and minimum distance graphs demonstrate heading can consistently reduce cover time as compared to non-heading movement techniques.
 

In wireless networks, spoofing attack is one of the most common and challenging attacks. Due to these attacks the overall network performance would be degraded. In this paper, a medoid based clustering approach has been proposed to detect a multiple spoofing attacks in wireless networks. In addition, a Enhanced Partitioning Around Medoid (EPAM) with average silhouette has been integrated with the clustering mechanism to detect a multiple spoofing attacks with a higher accuracy rate. Based on the proposed method, the received signal strength based clustering approach has been adopted for medoid clustering for detection of attacks. In order to prevent the multiple spoofing attacks, dynamic MAC address allocation scheme using MD5 hashing technique is implemented. The experimental results shows, the proposed method can detect spoofing attacks with high accuracy rate and prevent the attacks. Thus the overall network performance is improved with high accuracy rate.
 

The E-mail messaging is one of the most popular uses of the Internet and the multiple Internet users can exchange messages within short span of time. Although the security of the E-mail messages is an important issue, no such security is supported by the Internet standards. One well known scheme, called PGP (Pretty Good Privacy) is used for personal security of E-mail messages. There is an attack on CFB Mode Encryption as used by OpenPGP. To overcome the attacks and to improve the security a new model is proposed which is "Secure Mail using Visual Cryptography". In the secure mail using visual cryptography the message to be transmitted is converted into a gray scale image. Then (2, 2) visual cryptographic shares are generated from the gray scale image. The shares are encrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform and authenticated using Public Key based Image Authentication method. One of the shares is send to a server and the second share is send to the receipent's mail box. The two shares are transmitted through two different transmission medium so man in the middle attack is not possible. If an adversary has only one out of the two shares, then he has absolutely no information about the message. At the receiver side the two shares are fetched, decrypted and stacked to generate the grey scale image. From the grey scale image the message is reconstructed.
 

Data is one of the most valuable assets for organization. It can facilitate users or organizations to meet their diverse goals, ranging from scientific advances to business intelligence. Due to the tremendous growth of data, the notion of big data has certainly gained momentum in recent years. Cloud computing is a key technology for storing, managing and analyzing big data. However, such large, complex, and growing data, typically collected from various data sources, such as sensors and social media, can often contain personally identifiable information (PII) and thus the organizations collecting the big data may want to protect their outsourced data from the cloud. In this paper, we survey our research towards development of efficient and effective privacy-enhancing (PE) techniques for management and analysis of big data in cloud computing.We propose our initial approaches to address two important PE applications: (i) privacy-preserving data management and (ii) privacy-preserving data analysis under the cloud environment. Additionally, we point out research issues that still need to be addressed to develop comprehensive solutions to the problem of effective and efficient privacy-preserving use of data.
 

Security companies have recently realised that mining massive amounts of security data can help generate actionable intelligence and improve their understanding of Internet attacks. In particular, attack attribution and situational understanding are considered critical aspects to effectively deal with emerging, increasingly sophisticated Internet attacks. This requires highly scalable analysis tools to help analysts classify, correlate and prioritise security events, depending on their likely impact and threat level. However, this security data mining process typically involves a considerable amount of features interacting in a non-obvious way, which makes it inherently complex. To deal with this challenge, we introduce MR-TRIAGE, a set of distributed algorithms built on MapReduce that can perform scalable multi-criteria data clustering on large security data sets and identify complex relationships hidden in massive datasets. The MR-TRIAGE workflow is made of a scalable data summarisation, followed by scalable graph clustering algorithms in which we integrate multi-criteria evaluation techniques. Theoretical computational complexity of the proposed parallel algorithms are discussed and analysed. The experimental results demonstrate that the algorithms can scale well and efficiently process large security datasets on commodity hardware. Our approach can effectively cluster any type of security events (e.g., spam emails, spear-phishing attacks, etc) that are sharing at least some commonalities among a number of predefined features.
 

Variable Precision Rough Set (VPRS) model is one of the most important extensions of the Classical Rough Set (RS) theory. It employs a majority inclusion relation mechanism in order to make the Classical RS model become more fault tolerant, and therefore the generalization of the model is improved. This paper can be viewed as an extension of previous investigations on attribution reduction problem in VPRS model. In our investigation, we illustrated with examples that the previously proposed reduct definitions may spoil the hidden classification ability of a knowledge system by ignoring certian essential attributes in some circumstances. Consequently, by proposing a new β-consistent notion, we analyze the relationship between the structures of Decision Table (DT) and different definitions of reduct in VPRS model. Then we give a new notion of β-complement reduct that can avoid the defects of reduct notions defined in previous literatures. We also supply the method to obtain the β- complement reduct using a decision table splitting algorithm, and finally demonstrate the feasibility of our approach with sample instances.
 

Billions of dollars of services and goods are sold through email marketing. Subject lines have a strong influence on open rates of the e-mails, as the consumers often open e-mails based on the subject. Traditionally, the e-mail-subject lines are compiled based on the best assessment of the human editors. We propose a method to help the editors by predicting subject line open rates by learning from past subject lines. The method derives different types of features from subject lines based on keywords, performance of past subject lines and syntax. Furthermore, we evaluate the contribution of individual subject-line keywords to overall open rates based on an iterative method-namely Attribution Scoring - and use this for improved predictions. A random forest based model is trained to combine these features to predict the performance. We use a dataset of more than a hundred thousand different subject lines with many billions of impressions to train and test the method. The proposed method shows significant improvement in prediction accuracy over the baselines for both new as well as already used subject lines.
 

This paper propose a fast human detection algorithm of video surveillance in emergencies. Firstly through the background subtraction based on the single Guassian model and frame subtraction, we get the target mask which is optimized by Gaussian filter and dilation. Then the interest points of head is obtained from figures with target mask and edge detection. Finally according to detecting these pionts we can track the head and count the number of people with the frequence of moving target at the same place. Simulation results show that the algorithm can detect the moving object quickly and accurately.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

Mutation analysis generates tests that distinguish variations, or mutants, of an artifact from the original. Mutation analysis is widely considered to be a powerful approach to testing, and hence is often used to evaluate other test criteria in terms of mutation score, which is the fraction of mutants that are killed by a test set. But mutation analysis is also known to provide large numbers of redundant mutants, and these mutants can inflate the mutation score. While mutation approaches broadly characterized as reduced mutation try to eliminate redundant mutants, the literature lacks a theoretical result that articulates just how many mutants are needed in any given situation. Hence, there is, at present, no way to characterize the contribution of, for example, a particular approach to reduced mutation with respect to any theoretical minimal set of mutants. This paper's contribution is to provide such a theoretical foundation for mutant set minimization. The central theoretical result of the paper shows how to minimize efficiently mutant sets with respect to a set of test cases. We evaluate our method with a widely-used benchmark.

Despite all the current controversies, the success of the email service is still valid. The ease of use of its various features contributed to its widespread adoption. In general, the email system provides for all its users the same set of features controlled by a single monolithic policy. Such solutions are efficient but limited because they grant no place for the concept of usage which denotes a user's intention of communication: private, professional, administrative, official, military. The ability to efficiently send emails from mobile devices creates new interesting opportunities. We argue that the context (location, time, device, operating system, access network...) of the email sender appears as a new dimension we have to take into account to complete the picture. Context is clearly orthogonal to usage because a same usage may require different features depending of the context. It is clear that there is no global policy meeting requirements of all possible usages and contexts. To address this problem, we propose to define a correspondence model which for a given usage and context allows to derive a correspondence type encapsulating the exact set of required features. With this model, it becomes possible to define an advanced email system which may cope with multiple policies instead of a single monolithic one. By allowing a user to select the exact policy coping with her needs, we argue that our approach reduces the risk-taking allowing the email system to slide from a trusted one to a confident one.

An improved harmony search algorithm is presented for solving continuous optimization problems in this paper. In the proposed algorithm, an elimination principle is developed for choosing from the harmony memory, so that the harmonies with better fitness will have more opportunities to be selected in generating new harmonies. Two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process with the different search spaces of the optimization problems. Numerical results of 12 benchmark problems show that the proposed algorithm performs more effectively than the existing HS variants in finding better solutions.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

The use of multiple independent spanning trees (ISTs) for data broadcasting in networks provides a number of advantages, including the increase of fault-tolerance, bandwidth and security. Thus, the designs of multiple ISTs on several classes of networks have been widely investigated. In this paper, we give an algorithm to construct ISTs on enhanced hypercubes Qn,k, which contain folded hypercubes as a subclass. Moreover, we show that these ISTs are near optimal for heights and path lengths. Let D(Qn,k) denote the diameter of Qn,k. If n - k is odd or n - k ∈ {2; n}, we show that all the heights of ISTs are equal to D(Qn,k) + 1, and thus are optimal. Otherwise, we show that each path from a node to the root in a spanning tree has length at most D(Qn,k) + 2. In particular, no more than 2.15 percent of nodes have the maximum path length. As a by-product, we improve the upper bound of wide diameter (respectively, fault diameter) of Qn,k from these path lengths.

The home computer user is often said to be the weakest link in computer security. They do not always follow security advice, and they take actions, as in phishing, that compromise themselves. In general, we do not understand why users do not always behave safely, which would seem to be in their best interest. This paper reviews the literature of surveys and studies of factors that influence security decisions for home computer users. We organize the review in four sections: understanding of threats, perceptions of risky behavior, efforts to avoid security breaches and attitudes to security interventions. We find that these studies reveal a lot of reasons why current security measures may not match the needs or abilities of home computer users and suggest future work needed to inform how security is delivered to this user group.