Visible to the public Biblio

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2019-01-21
Umar, K., Sultan, A. B., Zulzalil, H., Admodisastro, N., Abdullah, M. T..  2018.  Formulation of SQL Injection Vulnerability Detection as Grammar Reachability Problem. 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M). :179–184.

Data dependency flow have been reformulated as Context Free Grammar (CFG) reachability problem, and the idea was explored in detection of some web vulnerabilities, particularly Cross Site Scripting (XSS) and Access Control. However, reformulation of SQL Injection Vulnerability (SQLIV) detection as grammar reachability problem has not been investigated. In this paper, concepts of data dependency flow was used to reformulate SQLIVs detection as a CFG reachability problem. The paper, consequently defines reachability analysis strategy for SQLIVs detection.

Lee, W. van der, Verwer, S..  2018.  Vulnerability Detection on Mobile Applications Using State Machine Inference. 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :1–10.

Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.

Danyk, Y., Shestakov, V..  2018.  The detection of hybrid vulnerabilities and effects on the basis of analyzing the information activity in cyberspace. 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). :574–577.

The report presents the results of the investigations into the effects of the information hybrid threats through cyberspace on social, technical, socio and technical systems. The composition of the system of early efficient detection of the above hybrids is suggested. The results of the structural and parametric synthesis of the system are described. The recommendations related to the system implementation are given.

Leal, A. G., Teixeira, Í C..  2018.  Development of a suite of IPv6 vulnerability scanning tests using the TTCN-3 language. 2018 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.

With the transition from IPv4 IPv6 protocol to improve network communications, there are concerns about devices and applications' security that must be dealt at the beginning of implementation or during its lifecycle. Automate the vulnerability assessment process reduces management overhead, enabling better management of risks and control of the vulnerabilities. Consequently, it reduces the effort needed for each test and it allows the increase of the frequency of application, improving time management to perform all the other complicated tasks necessary to support a secure network. There are several researchers involved in tests of vulnerability in IPv6 networks, exploiting addressing mechanisms, extension headers, fragmentation, tunnelling or dual-stack networks (using both IPv4 and IPv6 at the same time). Most existing tools use the programming languages C, Java, and Python instead of a language designed specifically to create a suite of tests, which reduces maintainability and extensibility of the tests. This paper presents a solution for IPv6 vulnerabilities scan tests, based on attack simulations, combining passive analysis (observing the manifestation of behaviours of the system under test) and an active one (stimulating the system to become symptomatic). Also, it describes a prototype that simulates and detects denial-of-service attacks on the ICMPv6 Protocol from IPv6. Also, a detailed report is created with the identified vulnerability and the possible existing solutions to mitigate such a gap, thus assisting the process of vulnerability management.

Warzyński, A., Kołaczek, G..  2018.  Intrusion detection systems vulnerability on adversarial examples. 2018 Innovations in Intelligent Systems and Applications (INISTA). :1–4.

Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. Anomaly-based intrusion detection in networks refers to the problem of finding untypical events in the observed network traffic that do not conform to the expected normal patterns. It is assumed that everything that is untypical/anomalous could be dangerous and related to some security events. To detect anomalies many security systems implements a classification or clustering algorithms. However, recent research proved that machine learning models might misclassify adversarial events, e.g. observations which were created by applying intentionally non-random perturbations to the dataset. Such weakness could increase of false negative rate which implies undetected attacks. This fact can lead to one of the most dangerous vulnerabilities of intrusion detection systems. The goal of the research performed was verification of the anomaly detection systems ability to resist this type of attack. This paper presents the preliminary results of tests taken to investigate existence of attack vector, which can use adversarial examples to conceal a real attack from being detected by intrusion detection systems.

2018-06-07
Nashaat, M., Ali, K., Miller, J..  2017.  Detecting Security Vulnerabilities in Object-Oriented PHP Programs. 2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM). :159–164.

PHP is one of the most popular web development tools in use today. A major concern though is the improper and insecure uses of the language by application developers, motivating the development of various static analyses that detect security vulnerabilities in PHP programs. However, many of these approaches do not handle recent, important PHP features such as object orientation, which greatly limits the use of such approaches in practice. In this paper, we present OOPIXY, a security analysis tool that extends the PHP security analyzer PIXY to support reasoning about object-oriented features in PHP applications. Our empirical evaluation shows that OOPIXY detects 88% of security vulnerabilities found in micro benchmarks. When used on real-world PHP applications, OOPIXY detects security vulnerabilities that could not be detected using state-of-the-art tools, retaining a high level of precision. We have contacted the maintainers of those applications, and two applications' development teams verified the correctness of our findings. They are currently working on fixing the bugs that lead to those vulnerabilities.

2018-05-02
Chen, Jia, Feng, Yu, Dillig, Isil.  2017.  Precise Detection of Side-Channel Vulnerabilities Using Quantitative Cartesian Hoare Logic. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :875–890.
This paper presents Themis, an end-to-end static analysis tool for finding resource-usage side-channel vulnerabilities in Java applications. We introduce the notion of epsilon-bounded non-interference, a variant and relaxation of Goguen and Meseguer's well-known non-interference principle. We then present Quantitative Cartesian Hoare Logic (QCHL), a program logic for verifying epsilon-bounded non-interference. Our tool, Themis, combines automated reasoning in CHL with lightweight static taint analysis to improve scalability. We evaluate Themis on well known Java applications and demonstrate that Themis can find unknown side-channel vulnerabilities in widely-used programs. We also show that Themis can verify the absence of vulnerabilities in repaired versions of vulnerable programs and that Themis compares favorably against Blazer, a state-of-the-art static analysis tool for finding timing side channels in Java applications.
2018-04-04
Yost, W., Jaiswal, C..  2017.  MalFire: Malware firewall for malicious content detection and protection. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). :428–433.

The online portion of modern life is growing at an astonishing rate, with the consequence that more of the user's critical information is stored online. This poses an immediate threat to privacy and security of the user's data. This work will cover the increasing dangers and security risks of adware, adware injection, and malware injection. These programs increase in direct proportion to the number of users on the Internet. Each of these programs presents an imminent threat to a user's privacy and sensitive information, anytime they utilize the Internet. We will discuss how current ad blockers are not the actual solution to these threats, but rather a premise to our work. Current ad blocking tools can be discovered by the web servers which often requires suppression of the ad blocking tool. Suppressing the tool creates vulnerabilities in a user's system, but even when the tool is active their system is still susceptible to peril. It is possible, even when an ad blocking tool is functioning, for it to allow adware content through. Our solution to the contemporary threats is our tool, MalFire.

Ullah, I., Mahmoud, Q. H..  2017.  A hybrid model for anomaly-based intrusion detection in SCADA networks. 2017 IEEE International Conference on Big Data (Big Data). :2160–2167.

Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.

Ficco, M., Venticinque, S., Rak, M..  2017.  Malware Detection for Secure Microgrids: CoSSMic Case Study. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :336–341.

Information and communication technologies are extensively used to monitor and control electric microgrids. Although, such innovation enhance self healing, resilience, and efficiency of the energy infrastructure, it brings emerging security threats to be a critical challenge. In the context of microgrid, the cyber vulnerabilities may be exploited by malicious users for manipulate system parameters, meter measurements and price information. In particular, malware may be used to acquire direct access to monitor and control devices in order to destabilize the microgrid ecosystem. In this paper, we exploit a sandbox to analyze security vulnerability to malware of involved embedded smart-devices, by monitoring at different abstraction levels potential malicious behaviors. In this direction, the CoSSMic project represents a relevant case study.

Narwal, P., Singh, S. N., Kumar, D..  2017.  Game-theory based detection and prevention of DoS attacks on networking node in open stack private cloud. 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). :481–486.

Security at virtualization level has always been a major issue in cloud computing environment. A large number of virtual machines that are hosted on a single server by various customers/client may face serious security threats due to internal/external network attacks. In this work, we have examined and evaluated these threats and their impact on OpenStack private cloud. We have also discussed the most popular DOS (Denial-of-Service) attack on DHCP server on this private cloud platform and evaluated the vulnerabilities in an OpenStack networking component, Neutron, due to which this attack can be performed through rogue DHCP server. Finally, a solution, a game-theory based cloud architecture, that helps to detect and prevent DOS attacks in OpenStack has been proposed.

Zekri, M., Kafhali, S. E., Aboutabit, N., Saadi, Y..  2017.  DDoS attack detection using machine learning techniques in cloud computing environments. 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). :1–7.

Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.

Ran, L., Lu, L., Lin, H., Han, M., Zhao, D., Xiang, J., Yu, H., Ma, X..  2017.  An Experimental Study of Four Methods for Homology Analysis of Firmware Vulnerability. 2017 International Conference on Dependable Systems and Their Applications (DSA). :42–50.

In the production process of embedded device, due to the frequent reuse of third-party libraries or development kits, there are large number of same vulnerabilities that appear in more than one firmware. Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis, the widely used methods are mainly Binary difference analysis, Normalized compression distance, String feature matching and Fuzz hash. But when we use these methods for homology analysis, we found that the detection result is not ideal and there is a high false positive rate. Focusing on this problem, we analyzed the application scenarios of these four methods and their limitations by combining different methods and different types of files and the experiments show that the combination of methods and files have a better performance in homology analysis.

Lin, Y., Abur, A..  2017.  Identifying security vulnerabilities of weakly detectable network parameter errors. 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :295–301.
This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.
Liang, J., Sankar, L., Kosut, O..  2017.  Vulnerability analysis and consequences of false data injection attack on power system state estimation. 2017 IEEE Power Energy Society General Meeting. :1–1.
An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.
Lan, T., Wang, W., Huang, G. M..  2017.  False data injection attack in smart grid topology control: Vulnerability and countermeasure. 2017 IEEE Power Energy Society General Meeting. :1–5.
Cyber security is a crucial factor for modern power system as many applications are heavily relied on the result of state estimation. Therefore, it is necessary to assess and enhance cyber security for new applications in power system. As an emerging technology, smart grid topology control has been investigated in stability and reliability perspectives while the associated cyber security issue is not studied before. In successful false data injection attack (FDIA) against AC state estimation, attacker could alter online stability check result by decreasing real power flow measurement on the switching target line to undermine physical system stability in topology control. The physical impact of FDIA on system control operation and stability are illustrated. The vulnerability is discussed on perfect FDIA and imperfect FDIA against residue based bad data detection and corresponding countermeasure is proposed to secure critical substations in the system. The vulnerability and countermeasure are demonstrated on IEEE 24 bus reliability test system (RTS).
Majumder, R., Som, S., Gupta, R..  2017.  Vulnerability prediction through self-learning model. 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). :400–402.

Vulnerability being the buzz word in the modern time is the most important jargon related to software and operating system. Since every now and then, software is developed some loopholes and incompleteness lie in the development phase, so there always remains a vulnerability of abruptness in it which can come into picture anytime. Detecting vulnerability is one thing and predicting its occurrence in the due course of time is another thing. If we get to know the vulnerability of any software in the due course of time then it acts as an active alarm for the developers to again develop sound and improvised software the second time. The proposal talks about the implementation of the idea using the artificial neural network, where different data sets are being given as input for being used for further analysis for successful results. As of now, there are models for studying the vulnerabilities in the software and networks, this paper proposal in addition to the current work, will throw light on the predictability of vulnerabilities over the due course of time.

Zhang, B., Ye, J., Feng, C., Tang, C..  2017.  S2F: Discover Hard-to-Reach Vulnerabilities by Semi-Symbolic Fuzz Testing. 2017 13th International Conference on Computational Intelligence and Security (CIS). :548–552.
Fuzz testing is a popular program testing technique. However, it is difficult to find hard-to-reach vulnerabilities that are nested with complex branches. In this paper, we propose semi-symbolic fuzz testing to discover hard-to-reach vulnerabilities. Our method groups inputs into high frequency and low frequency ones. Then symbolic execution is utilized to solve only uncovered branches to mitigate the path explosion problem. Especially, in order to play the advantages of fuzz testing, our method locates critical branch for each low frequency input and corrects the generated test cases to comfort the branch condition. We also implemented a prototype\textbackslashtextbarS2F, and the experimental results show that S2F can gain 17.70% coverage performance and discover more hard-to-reach vulnerabilities than other vulnerability detection tools for our benchmark.
Wu, F., Wang, J., Liu, J., Wang, W..  2017.  Vulnerability detection with deep learning. 2017 3rd IEEE International Conference on Computer and Communications (ICCC). :1298–1302.
Vulnerability detection is an import issue in information system security. In this work, we propose the deep learning method for vulnerability detection. We present three deep learning models, namely, convolution neural network (CNN), long short term memory (LSTM) and convolution neural network — long short term memory (CNN-LSTM). In order to test the performance of our approach, we collected 9872 sequences of function calls as features to represent the patterns of binary programs during their execution. We apply our deep learning models to predict the vulnerabilities of these binary programs based on the collected data. The experimental results show that the prediction accuracy of our proposed method reaches 83.6%, which is superior to that of traditional method like multi-layer perceptron (MLP).
2017-11-27
Bruillard, P., Nowak, K., Purvine, E..  2016.  Anomaly Detection Using Persistent Homology. 2016 Cybersecurity Symposium (CYBERSEC). :7–12.

Many aspects of our daily lives now rely on computers, including communications, transportation, government, finance, medicine, and education. However, with increased dependence comes increased vulnerability. Therefore recognizing attacks quickly is critical. In this paper, we introduce a new anomaly detection algorithm based on persistent homology, a tool which computes summary statistics of a manifold. The idea is to represent a cyber network with a dynamic point cloud and compare the statistics over time. The robustness of persistent homology makes for a very strong comparison invariant.

Settanni, G., Shovgenya, Y., Skopik, F., Graf, R., Wurzenberger, M., Fiedler, R..  2016.  Correlating cyber incident information to establish situational awareness in Critical Infrastructures. 2016 14th Annual Conference on Privacy, Security and Trust (PST). :78–81.

Protecting Critical Infrastructures (CIs) against contemporary cyber attacks has become a crucial as well as complex task. Modern attack campaigns, such as Advanced Persistent Threats (APTs), leverage weaknesses in the organization's business processes and exploit vulnerabilities of several systems to hit their target. Although their life-cycle can last for months, these campaigns typically go undetected until they achieve their goal. They usually aim at performing data exfiltration, cause service disruptions and can also undermine the safety of humans. Novel detection techniques and incident handling approaches are therefore required, to effectively protect CI's networks and timely react to this type of threats. Correlating large amounts of data, collected from a multitude of relevant sources, is necessary and sometimes required by national authorities to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of an attack. In this paper we propose three novel methods for security information correlation designed to discover relevant insights and support the establishment of cyber situational awareness.

Pang, Y., Xue, X., Namin, A. S..  2016.  Early Identification of Vulnerable Software Components via Ensemble Learning. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). :476–481.

Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.

Meng, Q., Shameng, Wen, Chao, Feng, Chaojing, Tang.  2016.  Predicting buffer overflow using semi-supervised learning. 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). :1959–1963.

As everyone knows vulnerability detection is a very difficult and time consuming work, so taking advantage of the unlabeled data sufficiently is needed and helpful. According the above reality, in this paper a method is proposed to predict buffer overflow based on semi-supervised learning. We first employ Antlr to extract AST from C/C++ source files, then according to the 22 buffer overflow attributes taxonomies, a 22-dimension vector is extracted from every function in AST, at last, the vector is leveraged to train a classifier to predict buffer overflow vulnerabilities. The experiment and evaluation indicate our method is correct and efficient.

Kuze, N., Ishikura, S., Yagi, T., Chiba, D., Murata, M..  2016.  Detection of vulnerability scanning using features of collective accesses based on information collected from multiple honeypots. NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. :1067–1072.

Attacks against websites are increasing rapidly with the expansion of web services. An increasing number of diversified web services make it difficult to prevent such attacks due to many known vulnerabilities in websites. To overcome this problem, it is necessary to collect the most recent attacks using decoy web honeypots and to implement countermeasures against malicious threats. Web honeypots collect not only malicious accesses by attackers but also benign accesses such as those by web search crawlers. Thus, it is essential to develop a means of automatically identifying malicious accesses from mixed collected data including both malicious and benign accesses. Specifically, detecting vulnerability scanning, which is a preliminary process, is important for preventing attacks. In this study, we focused on classification of accesses for web crawling and vulnerability scanning since these accesses are too similar to be identified. We propose a feature vector including features of collective accesses, e.g., intervals of request arrivals and the dispersion of source port numbers, obtained with multiple honeypots deployed in different networks for classification. Through evaluation using data collected from 37 honeypots in a real network, we show that features of collective accesses are advantageous for vulnerability scanning and crawler classification.

Chopade, P., Zhan, J., Bikdash, M..  2016.  Micro-Community detection and vulnerability identification for large critical networks. 2016 IEEE Symposium on Technologies for Homeland Security (HST). :1–7.

In this work we put forward our novel approach using graph partitioning and Micro-Community detection techniques. We firstly use algebraic connectivity or Fiedler Eigenvector and spectral partitioning for community detection. We then used modularity maximization and micro level clustering for detecting micro-communities with concept of community energy. We run micro-community clustering algorithm recursively with modularity maximization which helps us identify dense, deeper and hidden community structures. We experimented our MicroCommunity Clustering (MCC) algorithm for various types of complex technological and social community networks such as directed weighted, directed unweighted, undirected weighted, undirected unweighted. A novel fact about this algorithm is that it is scalable in nature.