208 research outputs found
DeepSQLi: Deep Semantic Learning for Testing SQL Injection
Security is unarguably the most serious concern for Web applications, to
which SQL injection (SQLi) attack is one of the most devastating attacks.
Automatically testing SQLi vulnerabilities is of ultimate importance, yet is
unfortunately far from trivial to implement. This is because the existence of a
huge, or potentially infinite, number of variants and semantic possibilities of
SQL leading to SQLi attacks on various Web applications. In this paper, we
propose a deep natural language processing based tool, dubbed DeepSQLi, to
generate test cases for detecting SQLi vulnerabilities. Through adopting deep
learning based neural language model and sequence of words prediction, DeepSQLi
is equipped with the ability to learn the semantic knowledge embedded in SQLi
attacks, allowing it to translate user inputs (or a test case) into a new test
case, which is semantically related and potentially more sophisticated.
Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art
SQLi testing automation tool, on six real-world Web applications that are of
different scales, characteristics and domains. Empirical results demonstrate
the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such
that more SQLi vulnerabilities can be identified by using a less number of test
cases, whilst running much faster
Evaluation of Web Security Mechanisms Using Vulnerability & Attack Injection
In this paper we propose a methodology and a prototype tool to evaluate web application security mechanisms. The methodology is based on the idea that injecting realistic vulnerabilities in a web application and attacking them automatically can be used to support the assessment of existing security mechanisms and tools in custom setup scenarios. To provide true to life results, the proposed vulnerability and attack injection methodology relies on the study of a large number of vulnerabilities in real web applications.
In addition to the generic methodology, the paper describes the implementation of the Vulnerability & Attack Injector Tool (VAIT) that allows the automation of the entire process. We used this tool to run a set of experiments that demonstrate the feasibility and the effectiveness of the proposed methodology. The experiments include the evaluation of coverage and false positives of an intrusion detection system for SQL Injection attacks and the assessment of the effectiveness of two top commercial web application vulnerability scanners. Results show that the injection of vulnerabilities and attacks is indeed an effective way to evaluate security mechanisms and to point out not only their weaknesses but also ways for their improvemen
The effects of security protocols on cybercrime at Ahmadu Bello University, Zaria, Nigeria.
Masters Degree. University of KwaZulu-Natal, Durban.The use of Information Communication Technology (ICT) within the educational
sector is increasing rapidly. University systems are becoming increasingly
dependent on computerized information systems (CIS) in order to carry out their
daily routine. Moreover, CIS no longer process staff records and financial data
only, as they once did. Nowadays, universities use CIS to assist in automating
the overall system. This automation includes the use of multiple databases, data
detail periodicity (i.e. gender, race/ethnicity, enrollment, degrees granted, and
program major), record identification (e.g. social security number ‘SSN’), linking
to other databases (i.e. linking unit record data with external databases such as
university and employment data).
The increasing demand and exposure to Internet resources and infrastructure by
individuals and universities have made IT infrastructure easy targets for
cybercriminals who employ sophisticated attacks such as Advanced Persistent
Threats, Distributed Denial of Service attacks and Botnets in order to steal
confidential data, identities of individuals and money. Hence, in order to stay in
business, universities realise that it is imperative to secure vital Information
Systems from easily being exploited by emerging and existing forms of
cybercrimes. This study was conducted to determine and evaluate the various
forms of cybercrimes and their consequences on the university network at
Ahmadu Bello University, Zaria. The study was also aimed at proposing means
of mitigating cybercrimes and their effects on the university network. Hence, an
exploratory research design supported by qualitative research approach was
used in this study. Staff of the Institute of Computing, Information and
Communication technology (ICICT) were interviewed. The findings of the study
present different security measures, and security tools that can be used to
effectively mitigate cybercrimes. It was found that social engineering, denial of
service attacks, website defacement were among the types of cybercrimes
occurring on the university network. It is therefore recommended that behavioural
approach in a form of motivation of staff behaviour, salary increases, and cash
incentive to reduce cybercrime perpetrated by these staff
Survey on detecting and preventing web application broken access control attacks
Web applications are an essential component of the current wide range of digital services proposition including financial and governmental services as well as social networking and communications. Broken access control vulnerabilities pose a huge risk to that echo system because they allow the attacker to circumvent the allocated permissions and rights and perform actions that he is not authorized to perform. This paper gives a broad survey of the current research progress on approaches used to detect access control vulnerabilities exploitations and attacks in web application components. It categorizes these approaches based on their key techniques and compares the different detection methods in addition to evaluating their strengths and weaknesses. We also spotted and elaborated on some exciting research gaps found in the current literature, Finally, the paper summarizes the general detection approaches and suggests potential research directions for the future
Automatically Repairing Web Application Firewalls Based on Successful SQL Injection Attacks
Testing and fixing WAFs are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%)
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A Comprehensive Survey of Voice over IP Security Research
We present a comprehensive survey of Voice over IP security academic research, using a set of 245 publications forming a closed cross-citation set. We classify these papers according to an extended version of the VoIP Security Alliance (VoIPSA) Threat Taxonomy. Our goal is to provide a roadmap for researchers seeking to understand existing capabilities and to identify gaps in addressing the numerous threats and vulnerabilities present in VoIP systems. We discuss the implications of our findings with respect to vulnerabilities reported in a variety of VoIP products. We identify two specific problem areas (denial of service, and service abuse) as requiring significant more attention from the research community. We also find that the overwhelming majority of the surveyed work takes a black box view of VoIP systems that avoids examining their internal structure and implementation. Such an approach may miss the mark in terms of addressing the main sources of vulnerabilities, i.e., implementation bugs and misconfigurations. Finally, we argue for further work on understanding cross-protocol and cross-mechanism vulnerabilities (emergent properties), which are the byproduct of a highly complex system-of-systems and an indication of the issues in future large-scale systems
A Machine Learning-Driven Evolutionary Approach for Testing Web Application Firewalls
Web application firewalls (WAF) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submit them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolve them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implemented ML-Driven in a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns
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