An optimal transportation routing approach using GIS-based dynamic traffic flows

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively

Forensic identification and detection of hidden and obfuscated malware

The revolution in online criminal activities and malicious software (malware) has posed a serious challenge in malware forensics. Malicious attacks have become more organized and purposefully directed. With cybercrimes escalating to great heights in quantity as well as in sophistication and stealth, the main challenge is to detect hidden and obfuscated malware. Malware authors use a variety of obfuscation methods and specialized stealth techniques of information hiding to embed malicious code, to infect systems and to thwart any attempt to detect them, specifically with the use of commercially available anti-malware engines. This has led to the situation of zero-day attacks, where malware inflict systems even with existing security measures. The aim of this thesis is to address this situation by proposing a variety of novel digital forensic and data mining techniques to automatically detect hidden and obfuscated malware. Anti-malware engines use signature matching to detect malware where signatures are generated by human experts by disassembling the file and selecting pieces of unique code. Such signature based detection works effectively with known malware but performs poorly with hidden or unknown malware. Code obfuscation techniques, such as packers, polymorphism and metamorphism, are able to fool current detection techniques by modifying the parent code to produce offspring copies resulting in malware that has the same functionality, but with a different structure. These evasion techniques exploit the drawbacks of traditional malware detection methods, which take current malware structure and create a signature for detecting this malware in the future. However, obfuscation techniques aim to reduce vulnerability to any kind of static analysis to the determent of any reverse engineering process. Furthermore, malware can be hidden in file system slack space, inherent in NTFS file system based partitions, resulting in malware detection that even more difficult.Doctor of Philosoph

Machine learning based botnet identification traffic

The continued growth of the Internet has resulted in the increasing sophistication of toolkit and methods to conduct computer attacks and intrusions that are easy to use and publicly available to download, such as Zeus botnet toolkit. Botnets are responsible for many cyber-attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of existence botnet toolkits release updates for new features, development and support. This presents challenges in the detection and prevention of bots. Current botnet detection approaches mostly ineffective as botnets change their Command and Control (C&C) server structures, centralized (e.g., IRC, HTTP), distributed (e.g., P2P), and encryption deterrent. In this paper, based on real world data sets we present our preliminary research on predicting the new bots before they launch their attack. We propose a rich set of features of network traffic using Classification of Network Information Flow Analysis (CONIFA) framework to capture regularities in C&C communication channels and malicious traffic. We present a case study of applying the approach to a popular botnet toolkit, Zeus. The experimental evaluation suggest that it is possible to detect effectively botnets during the botnet C&C communication generated from new updated Zeus botnet toolkit by building the classifier using machine learning from an earlier version and before they launch their attacks using traffic behaviors. Also, show that there is similarity in C&C structures various Botnet toolkit versions and that the network characteristics of botnet C&C traffic is different from legitimate network traffic. Such methods could reduce many different resources needed to identify C&C communication channels and malicious traffic

GOM: New Genetic Optimizing Model for broadcasting tree in MANET

Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE

Towards a Feature Rich Model for Predicting Spam Emails containing Malicious Attachments and URLs

Malicious content in spam emails is increasing in the form of attachments and URLs. Malicious attachments and URLs attempt to deliver software that can compromise the security of a computer. These malicious attachments also try to disguise their content to avoid virus scanners used by most email services to screen for such risks. Malicious URLs add another layer of disguise, where the email content tries to entice the recipient to click on a URL that links to a malicious Web site or downloads a malicious attachment. In this paper, based on two real world data sets we present our preliminary research on predicting the kind of spam email most likely to contain these highly dangerous spam emails. We propose a rich set of features for the content of emails to capture regularities in emails containing malicious content. We show these features can predict malicious attachments within an area under the precious recall curve (AUC-PR) up to 95.2%, and up to 68.1% for URLs. Our work can help reduce reliance on virus scanners and URL blacklists, which often do not update as quickly as the malicious content it attempts to identify. Such methods could reduce the many different resources now needed to identify malicious content

From ZeuS to Zitmo : trends in banking malware

In the crimeware world, financial botnets are a global threat to banking organizations. Such malware purposely performs financial fraud and steals critical information from clients' computers. A common example of banking malware is the ZeuS botnet. Recently, variants of this malware have targeted mobile platforms, as The-ZeuS-in-the-Mobile or Zitmo. With the rise in mobile systems, platform security is becoming a major concern across the mobile world, with rising incidence of compromising Android devices. In similar vein, there have been mobile botnet attacks on iPhones, Blackberry and Symbian devices. In this setting, we report on trends and developments of ZeuS and its variants

Organizations and cyber crime: An analysis of the nature of groups engaged in cyber crime

This paper explores the nature of groups engaged in cyber crime. It briefly outlines the definition and scope of cyber crime, theoretical and empirical challenges in addressing what is known about cyber offenders, and the likely role of organized crime groups. The paper gives examples of known cases that illustrate individual and group behaviour, and motivations of typical offenders, including state actors. Different types of cyber crime and different forms of criminal organization are described drawing on the typology suggested by McGuire (2012). It is apparent that a wide variety of organizational structures are involved in cyber crime. Enterprise or profit-oriented activities, and especially cyber crime committed by state actors, appear to require leadership, structure, and specialisation. By contrast, protest activity tends to be less organized, with weak (if any) chain of command

Towards understanding malware behaviour by the extraction of API calls

One of the recent trends adopted by malware authors is to use packers or software tools that instigate code obfuscation in order to evade detection by antivirus scanners. With evasion techniques such as polymorphism and metamorphism malware is able to fool current detection techniques. Thus, security researchers and the anti-virus industry are facing a herculean task in extracting payloads hidden within packed executables. It is a common practice to use manual unpacking or static unpacking using some software tools and analyse the application programming interface (API) calls for malware detection. However, extracting these features from the unpacked executables for reverse obfuscation is labour intensive and requires deep knowledge of low-level programming that includes kernel and assembly language. This paper presents an automated method of extracting API call features and analysing them in order to understand their use for malicious purpose. While some research has been conducted in arriving at file birthmarks using API call features and the like, there is a scarcity of work that relates to features in malcodes. To address this gap, we attempt to automatically analyse and classify the behavior of API function calls based on the malicious intent hidden within any packed program. This paper uses four-step methodology for developing a fully automated system to arrive at six main categories of suspicious behavior of API call features. © 2010 IEEE

Digital forensic techniques for static analysis of NTFS images

Static analysis of the Windows NTS File System (NTFS) which is the standard and most commonly used file system could provide useful information for digital forensics. However, since the NFTS disk image records every event in the system, forensic tools need to process an enormous amount of information related to user / kernel environment, buffer overflows, trace conditions, network stack and other related subsystems. This leads to imperfect forensic tools that are practical for implementation but not comprehensive and effective. This research discusses the analysis technique to detect data hidden based on the internal structure of the NTFS file system in the boot sector. Further, it attempts to unearth the vulnerabilities of NTFS disk image and weaknesses of the current forensic techniques. The paper argues that a comprehensive tool with improved techniques is warranted for a successful forensic analysis