Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

Dynamic adversarial mining - effectively applying machine learning in adversarial non-stationary environments.

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the ‘Dynamic Adversarial Mining’ problem, and the presented work provides the foundation for this new interdisciplinary area of research, at the crossroads of Machine Learning, Cybersecurity, and Streaming Data Mining. We start with a white hat analysis of the vulnerabilities of classification systems to exploratory attack. The proposed ‘Seed-Explore-Exploit’ framework provides characterization and modeling of attacks, ranging from simple random evasion attacks to sophisticated reverse engineering. It is observed that, even systems having prediction accuracy close to 100%, can be easily evaded with more than 90% precision. This evasion can be performed without any information about the underlying classifier, training dataset, or the domain of application. Attacks on machine learning systems cause the data to exhibit non stationarity (i.e., the training and the testing data have different distributions). It is necessary to detect these changes in distribution, called concept drift, as they could cause the prediction performance of the model to degrade over time. However, the detection cannot overly rely on labeled data to compute performance explicitly and monitor a drop, as labeling is expensive and time consuming, and at times may not be a possibility altogether. As such, we propose the ‘Margin Density Drift Detection (MD3)’ algorithm, which can reliably detect concept drift from unlabeled data only. MD3 provides high detection accuracy with a low false alarm rate, making it suitable for cybersecurity applications; where excessive false alarms are expensive and can lead to loss of trust in the warning system. Additionally, MD3 is designed as a classifier independent and streaming algorithm for usage in a variety of continuous never-ending learning systems. We then propose a ‘Dynamic Adversarial Mining’ based learning framework, for learning in non-stationary and adversarial environments, which provides ‘security by design’. The proposed ‘Predict-Detect’ classifier framework, aims to provide: robustness against attacks, ease of attack detection using unlabeled data, and swift recovery from attacks. Ideas of feature hiding and obfuscation of feature importance are proposed as strategies to enhance the learning framework\u27s security. Metrics for evaluating the dynamic security of a system and recover-ability after an attack are introduced to provide a practical way of measuring efficacy of dynamic security strategies. The framework is developed as a streaming data methodology, capable of continually functioning with limited supervision and effectively responding to adversarial dynamics. The developed ideas, methodology, algorithms, and experimental analysis, aim to provide a foundation for future work in the area of ‘Dynamic Adversarial Mining’, wherein a holistic approach to machine learning based security is motivated

Denial of Service in Web-Domains: Building Defenses Against Next-Generation Attack Behavior

The existing state-of-the-art in the field of application layer Distributed Denial of Service (DDoS) protection is generally designed, and thus effective, only for static web domains. To the best of our knowledge, our work is the first that studies the problem of application layer DDoS defense in web domains of dynamic content and organization, and for next-generation bot behaviour. In the first part of this thesis, we focus on the following research tasks: 1) we identify the main weaknesses of the existing application-layer anti-DDoS solutions as proposed in research literature and in the industry, 2) we obtain a comprehensive picture of the current-day as well as the next-generation application-layer attack behaviour and 3) we propose novel techniques, based on a multidisciplinary approach that combines offline machine learning algorithms and statistical analysis, for detection of suspicious web visitors in static web domains. Then, in the second part of the thesis, we propose and evaluate a novel anti-DDoS system that detects a broad range of application-layer DDoS attacks, both in static and dynamic web domains, through the use of advanced techniques of data mining. The key advantage of our system relative to other systems that resort to the use of challenge-response tests (such as CAPTCHAs) in combating malicious bots is that our system minimizes the number of these tests that are presented to valid human visitors while succeeding in preventing most malicious attackers from accessing the web site. The results of the experimental evaluation of the proposed system demonstrate effective detection of current and future variants of application layer DDoS attacks

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Artificial Intelligence in Computer Networks : Role of AI in Network Security

Artificial Intelligence (AI) in computer networks has been emerging for the last decade, there are revolutionary inventions that have created automation and digitalization in the fields of the Internet. The layout of computer networks works in layers of topologies with the help of AI, a virtual layer of software has been added that runs predictive algorithms of Artificial Neural Networks (ANNs) with the help of Machine Learning (ML) and Deep Learning (DL). This thesis describes the relation between AI algorithms and duplication of human cognitive behavior in emerging technologies. The advantages of AI in computer networks include automation, digitalization, Internet of Things (IoT), centralization of data, etc. At the same time, the biggest disadvantage is the ethical violation of privacy and the security of data. It is further discussed in the thesis that Artificial Intelligence uses many security protocols, including Next-Generation Firewalls, to prevent security violations. The Software Network Analysis (SNA) and Software Defined Networks (SDN) play an important role in Artificial Intelligence in computer Networks. This thesis aims to analyze the relationship between the development of AI algorithms and the duplication of the human cognitive behavior in various emerging technologies. Software Network Analysis (SNA) and Software Defined Networks (SDN) are critical components of computer network artificial intelligence. The purpose of this dissertation is to investigate the relationship between AI algorithms and network security. The thesis analyzes 2 main aspects, the role of Artificial Intelligence in Computer Networks and how Artificial Intelligence is helping in securing computer networks to deal with the modern network threats. Security today has become one of the main concerns, everyday a production networks receives arounds thousands of attacks of different scales, and proper network security measures are not configured and taken, a lot can be compromised. Network virtualization, Cloud Computing, has seen exponentially growth in few past years, because of the trend of less human interaction, and minimizing of doing repeated tasks over and over. Data in today’s world is now more important than it has been in decades earlier, this is because today everything is moving towards digitalization, proper Information Security policies are derived and implemented all over the world to ensure the protection of Data. Europe has its own General Data Protection Regulation (GDPR) which ensures that every company who deals with data is to implement certain measures to ensure the data is protected which also involves implementing the right network security measures so that the right people have the access to the sensitive information. This thesis covers the overall impact of Artificial Intelligence in Computer Networks and Network Security

From Understanding Telephone Scams to Implementing Authenticated Caller ID Transmission

abstract: The telephone network is used by almost every person in the modern world. With the rise of Internet access to the PSTN, the telephone network today is rife with telephone spam and scams. Spam calls are significant annoyances for telephone users, unlike email spam, spam calls demand immediate attention. They are not only significant annoyances but also result in significant financial losses in the economy. According to complaint data from the FTC, complaints on illegal calls have made record numbers in recent years. Americans lose billions to fraud due to malicious telephone communication, despite various efforts to subdue telephone spam, scam, and robocalls. In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Improving the resilience of cyber-physical systems under strategic adversaries

Renewable energy resources challenge traditional energy system operations by substituting the stability and predictability of fossil fuel based generation with the unreliability and uncertainty of wind and solar power. Rising demand for green energy drives grid operators to integrate sensors, smart meters, and distributed control to compensate for this uncertainty and improve the operational efficiency of the grid. Real-time negotiations enable producers and consumers to adjust power loads during shortage periods, such as an unexpected outage or weather event, and to adapt to time-varying energy needs. While such systems improve grid performance, practical implementation challenges can derail the operation of these distributed cyber-physical systems. Network disruptions introduce instability into control feedback systems, and strategic adversaries can manipulate power markets for financial gain. This dissertation analyzes the impact of these outages and adversaries on cyber-physical systems and provides methods for improving resilience, with an emphasis on distributed energy systems. First, a financial model of an interdependent energy market lays the groundwork for profit-oriented attacks and defenses, and a game theoretic strategy optimizes attack plans and defensive investments in energy systems with multiple independent actors. Then attacks and defenses are translated from a theoretical context to a real-time energy market via denial of service (DoS) outages and moving target defenses. Analysis on two market mechanisms shows how adversaries can disrupt market operation, destabilize negotiations, and extract profits by attacking network links and disrupting communication. Finally, a low-cost DoS defense technique demonstrates a method that energy systems may use to defend against attacks

Detecting spam relays by SMTP traffic characteristics using an autonomous detection system

Spam emails are flooding the Internet. Research to prevent spam is an ongoing concern. SMTP traffic was collected from different sources in real networks and analyzed to determine the difference regarding SMTP traffic characteristics of legitimate email clients, legitimate email servers and spam relays. It is found that SMTP traffic from legitimate sites and non-legitimate sites are different and could be distinguished from each other. Some methods, which are based on analyzing SMTP traffic characteristics, were purposed to identify spam relays in the network in this thesis. An autonomous combination system, in which machine learning technologies were employed, was developed to identify spam relays in this thesis. This system identifies spam relays in real time before spam emails get to an end user by using SMTP traffic characteristics never involving email real content. A series of tests were conducted to evaluate the performance of this system. And results show that the system can identify spam relays with a high spam relay detection rate and an acceptable ratio of false positive errors

Towards Least Privilege Principle: Limiting Unintended Accesses in Software Systems.

Adhering to the least privilege principle involves ensuring that only legitimate subjects have access rights to objects. Sometimes, this is hard because of permission irrevocability, changing security requirements, infeasibility of access control mechanisms, and permission creeps. If subjects turn rogue, the accesses can be abused. This thesis examines three scenarios where accesses are commonly abused and lead to security issues, and proposes three systems, SEAL, DeGap, and Expose to detect and, where practical, eliminate unintended accesses. Firstly, we examine abuse of email addresses, whose leakages are irreversible. Also, users can only hope that businesses requiring their email addresses for validating affiliations do not misuse them. SEAL uses semi-private aliases, which permits gradual and selective controls while providing privacy for affiliation validations. Secondly, access control mechanisms may be ineffective as subject roles change and administrative oversights lead to permission gaps, which should be removed expeditiously. Identifying permission gaps can be hard since another reference point besides granted permissions is often unavailable. DeGap uses access logs to estimate the gaps while using a common logic for various system services. DeGap also recommends configuration changes towards reducing the gaps. Lastly, unintended software code re-use can lead to intellectual property theft and license violations. Determining whether an application uses a library can be difficult. Compiler optimizations, function inlining, and lack of symbols make using syntactic methods a challenge, while pure semantic analysis is slow. Given a library and a set of applications, Expose combines syntactic and semantic analysis to efficiently help identify applications that re-use the library.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99976/1/bengheng_1.pd

On Enhancing Security of Password-Based Authentication

Password has been the dominant authentication scheme for more than 30 years, and it will not be easily replaced in the foreseeable future. However, password authentication has long been plagued by the dilemma between security and usability, mainly due to human memory limitations. For example, a user often chooses an easy-to-guess (weak) password since it is easier to remember. The ever increasing number of online accounts per user even exacerbates this problem. In this dissertation, we present four research projects that focus on the security of password authentication and its ecosystem. First, we observe that personal information plays a very important role when a user creates a password. Enlightened by this, we conduct a study on how users create their passwords using their personal information based on a leaked password dataset. We create a new metric---Coverage---to quantify the personal information in passwords. Armed with this knowledge, we develop a novel password cracker named Personal-PCFG (Probabilistic Context-Free Grammars) that leverages personal information for targeted password guessing. Experiments show that Personal-PCFG is much more efficient than the original PCFG in cracking passwords. The second project aims to ease the password management hassle for a user. Password managers are introduced so that users need only one password (master password) to access all their other passwords. However, the password manager induces a single point of failure and is potentially vulnerable to data breach. To address these issues, we propose BluePass, a decentralized password manager that features a dual-possession security that involves a master password and a mobile device. In addition, BluePass enables a hand-free user experience by retrieving passwords from the mobile device through Bluetooth communications. In the third project, we investigate an overlooked aspect in the password lifecycle, the password recovery procedure. We study the password recovery protocols in the Alexa top 500 websites, and report interesting findings on the de facto implementation. We observe that the backup email is the primary way for password recovery, and the email becomes a single point of failure. We assess the likelihood of an account recovery attack, analyze the security policy of major email providers, and propose a security enhancement protocol to help securing password recovery emails by two factor authentication. \newline Finally, we focus on a more fundamental level, user identity. Password-based authentication is just a one-time checking to ensure that a user is legitimate. However, a user\u27s identity could be hijacked at any step. For example, an attacker can leverage a zero-day vulnerability to take over the root privilege. Thus, tracking the user behavior is essential to examine the identity legitimacy. We develop a user tracking system based on OS-level logs inside an enterprise network, and apply a variety of techniques to generate a concise and salient user profile for identity examination