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Musca, Constantin, Mirica, Emma, Deaconescu, Razvan.  2013.  Detecting and Analyzing Zero-Day Attacks Using Honeypots. 2013 19th International Conference on Control Systems and Computer Science. :543–548.
Computer networks are overwhelmed by self propagating malware (worms, viruses, trojans). Although the number of security vulnerabilities grows every day, not the same thing can be said about the number of defense methods. But the most delicate problem in the information security domain remains detecting unknown attacks known as zero-day attacks. This paper presents methods for isolating the malicious traffic by using a honeypot system and analyzing it in order to automatically generate attack signatures for the Snort intrusion detection/prevention system. The honeypot is deployed as a virtual machine and its job is to log as much information as it can about the attacks. Then, using a protected machine, the logs are collected remotely, through a safe connection, for analysis. The challenge is to mitigate the risk we are exposed to and at the same time search for unknown attacks.
Stavrou, E..  2018.  Enhancing Cyber Situational Awareness: A New Perspective of Password Auditing Tools. 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). :1-4.

Password auditing can enhance the cyber situational awareness of defenders, e.g. cyber security/IT professionals, with regards to the strength of text-based authentication mechanisms utilized in an organization. Auditing results can proactively indicate if weak passwords exist in an organization, decreasing the risks of compromisation. Password cracking is a typical and time-consuming way to perform password auditing. Given that defenders perform password auditing within a specific evaluation timeframe, the cracking process needs to be optimized to yield useful results. Existing password cracking tools do not provide holistic features to optimize the process. Therefore, the need arises to build new password auditing toolkits to assist defenders to achieve their task in an effective and efficient way. Moreover, to maximize the benefits of password auditing, a security policy should be utilized. Currently the efforts focus on the specification of password security policies, providing rules on how to construct passwords. This work proposes the functionality that should be supported by next-generation password auditing toolkits and provides guidelines to drive the specification of a relevant password auditing policy.

Vysotska, V., Lytvyn, V., Hrendus, M., Kubinska, S., Brodyak, O..  2018.  Method of Textual Information Authorship Analysis Based on Stylometry. 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). 2:9-16.

The paper dwells on the peculiarities of stylometry technologies usage to determine the style of the author publications. Statistical linguistic analysis of the author's text allows taking advantage of text content monitoring based on Porter stemmer and NLP methods to determine the set of stop words. The latter is used in the methods of stylometry to determine the ownership of the analyzed text to a specific author in percentage points. There is proposed a formal approach to the definition of the author's style of the Ukrainian text in the article. The experimental results of the proposed method for determining the ownership of the analyzed text to a particular author upon the availability of the reference text fragment are obtained. The study was conducted on the basis of the Ukrainian scientific texts of a technical area.

Li, Q., Xu, B., Li, S., Liu, Y., Cui, D..  2017.  Reconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems. 2017 36th Chinese Control Conference (CCC). :7571–7576.

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.

Kondo, D., Silverston, T., Tode, H., Asami, T., Perrin, O..  2017.  Risk analysis of information-leakage through interest packets in NDN. 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :360–365.

Information-leakage is one of the most important security issues in the current Internet. In Named-Data Networking (NDN), Interest names introduce novel vulnerabilities that can be exploited. By setting up a malware, Interest names can be used to encode critical information (steganography embedded) and to leak information out of the network by generating anomalous Interest traffic. This security threat based on Interest names does not exist in IP network, and it is essential to solve this issue to secure the NDN architecture. This paper performs risk analysis of information-leakage in NDN. We first describe vulnerabilities with Interest names and, as countermeasures, we propose a name-based filter using search engine information, and another filter using one-class Support Vector Machine (SVM). We collected URLs from the data repository provided by Common Crawl and we evaluate the performances of our per-packet filters. We show that our filters can choke drastically the throughput of information-leakage, which makes it easier to detect anomalous Interest traffic. It is therefore possible to mitigate information-leakage in NDN network and it is a strong incentive for future deployment of this architecture at the Internet scale.

Li, F., Jiang, M., Zhang, Z..  2017.  An adaptive sparse representation model by block dictionary and swarm intelligence. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). :200–203.

The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients and also to improve the sparsity, we propose a new SR model called adaptive sparse representation classifier(ASRC). In ASRC, a sparse coefficient strengthened item is added in the objective function. The model is solved by the artificial bee colony (ABC) algorithm with variable step to speed up the convergence. Also, a partition strategy for large scale dictionary is adopted to lighten bee's load and removes the irrelevant groups. Through different data sets, we empirically demonstrate the property of the new model and its recognition performance.

Bao, D., Yang, F., Jiang, Q., Li, S., He, X..  2017.  Block RLS algorithm for surveillance video processing based on image sparse representation. 2017 29th Chinese Control And Decision Conference (CCDC). :2195–2200.

Block recursive least square (BRLS) algorithm for dictionary learning in compressed sensing system is developed for surveillance video processing. The new method uses image blocks directly and iteratively to train dictionaries via BRLS algorithm, which is different from classical methods that require to transform blocks to columns first and then giving all training blocks at one time. Since the background in surveillance video is almost fixed, the residual of foreground can be represented sparsely and reconstructed with background subtraction directly. The new method and framework are applied in real image and surveillance video processing. Simulation results show that the new method achieves better representation performance than classical ones in both image and surveillance video.

Shahid, U., Farooqi, S., Ahmad, R., Shafiq, Z., Srinivasan, P., Zaffar, F..  2017.  Accurate Detection of Automatically Spun Content via Stylometric Analysis. 2017 IEEE International Conference on Data Mining (ICDM). :425–434.

Spammers use automated content spinning techniques to evade plagiarism detection by search engines. Text spinners help spammers in evading plagiarism detectors by automatically restructuring sentences and replacing words or phrases with their synonyms. Prior work on spun content detection relies on the knowledge about the dictionary used by the text spinning software. In this work, we propose an approach to detect spun content and its seed without needing the text spinner's dictionary. Our key idea is that text spinners introduce stylometric artifacts that can be leveraged for detecting spun documents. We implement and evaluate our proposed approach on a corpus of spun documents that are generated using a popular text spinning software. The results show that our approach can not only accurately detect whether a document is spun but also identify its source (or seed) document - all without needing the dictionary used by the text spinner.

Sudar, C., Arjun, S. K., Deepthi, L. R..  2017.  Time-Based One-Time Password for Wi-Fi Authentication and Security. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :1212–1216.

In the age of IOT, as more and more devices are getting connected to the internet through wireless networks, a better security infrastructure is required to protect these devices from massive attacks. For long SSIDs and passwords have been used to authenticate and secure Wi-Fi networks. But the SSID and password combination is vulnerable to security exploits like phishing and brute-forcing. In this paper, a completely automated Wi-Fi authentication system is proposed, that generates Time-based One-Time Passwords (TOTP) to secure Wi-Fi networks. This approach aims to black box the process of connecting to a Wi-Fi network for the user and the process of generating periodic secure passwords for the network without human intervention.

Brodeur, S., Rouat, J..  2017.  Optimality of inference in hierarchical coding for distributed object-based representations. 2017 15th Canadian Workshop on Information Theory (CWIT). :1–5.

Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important for an efficient representation of compositionality in object-based representations. With the perspective of feature learning as a data compression operation, we propose a new greedy inference algorithm for hierarchical sparse coding. Convolutional matching pursuit with a L0-norm constraint was used to encode the input signal into compact and non-redundant codes distributed across levels of the hierarchy. Simple and complex synthetic datasets of temporal signals were created to evaluate the encoding efficiency and compare with the theoretical lower bounds on the information rate for those signals. Empirical evidence have shown that the algorithm is able to infer near-optimal codes for simple signals. However, it failed for complex signals with strong overlapping between objects. We explain the inefficiency of convolutional matching pursuit that occurred in such case. This brings new insights about the NP-hard optimization problem related to using L0-norm constraint in inferring optimally compact and distributed object-based representations.

Devyatkin, D., Smirnov, I., Ananyeva, M., Kobozeva, M., Chepovskiy, A., Solovyev, F..  2017.  Exploring linguistic features for extremist texts detection (on the material of Russian-speaking illegal texts). 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :188–190.

In this paper we present results of a research on automatic extremist text detection. For this purpose an experimental dataset in the Russian language was created. According to the Russian legislation we cannot make it publicly available. We compared various classification methods (multinomial naive Bayes, logistic regression, linear SVM, random forest, and gradient boosting) and evaluated the contribution of differentiating features (lexical, semantic and psycholinguistic) to classification quality. The results of experiments show that psycholinguistic and semantic features are promising for extremist text detection.

Althamary, I. A., El-Alfy, E. S. M..  2017.  A more secure scheme for CAPTCHA-based authentication in cloud environment. 2017 8th International Conference on Information Technology (ICIT). :405–411.

Cloud computing is a remarkable model for permitting on-demand network access to an elastic collection of configurable adaptive resources and features including storage, software, infrastructure, and platform. However, there are major concerns about security-related issues. A very critical security function is user authentication using passwords. Although many flaws have been discovered in password-based authentication, it remains the most convenient approach that people continue to utilize. Several schemes have been proposed to strengthen its effectiveness such as salted hashes, one-time password (OTP), single-sign-on (SSO) and multi-factor authentication (MFA). This study proposes a new authentication mechanism by combining user's password and modified characters of CAPTCHA to generate a passkey. The modification of the CAPTCHA depends on a secret agreed upon between the cloud provider and the user to employ different characters for some characters in the CAPTCHA. This scheme prevents various attacks including short-password attack, dictionary attack, keylogger, phishing, and social engineering. Moreover, it can resolve the issue of password guessing and the use of a single password for different cloud providers.

Fraunholz, D., Zimmermann, M., Anton, S. D., Schneider, J., Schotten, H. Dieter.  2017.  Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology. 2017 7th International Conference on Cloud Computing, Data Science Engineering - Confluence. :416–421.

Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.

Sankalpa, I., Dhanushka, T., Amarasinghe, N., Alawathugoda, J., Ragel, R..  2016.  On implementing a client-server setting to prevent the Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH) attacks. 2016 Manufacturing Industrial Engineering Symposium (MIES). :1–5.

Compression is desirable for network applications as it saves bandwidth. Differently, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to the “Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH)” attack on web traffic protected by the TLS protocol. The general guidance to prevent this attack is to disable HTTP compression, preserving confidentiality but sacrificing bandwidth. As a more sophisticated countermeasure, fixed-dictionary compression was introduced in 2015 enabling compression while protecting high-value secrets, such as cookies, from attacks. The fixed-dictionary compression method is a cryptographically sound countermeasure against the BREACH attack, since it is proven secure in a suitable security model. In this project, we integrate the fixed-dictionary compression method as a countermeasure for BREACH attack, for real-world client-server setting. Further, we measure the performance of the fixed-dictionary compression algorithm against the DEFLATE compression algorithm. The results evident that, it is possible to save some amount of bandwidth, with reasonable compression/decompression time compared to DEFLATE operations. The countermeasure is easy to implement and deploy, hence, this would be a possible direction to mitigate the BREACH attack efficiently, rather than stripping off the HTTP compression entirely.

Z. Zhu, M. B. Wakin.  2015.  "Wall clutter mitigation and target detection using Discrete Prolate Spheroidal Sequences". 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). :41-45.

We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS's) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique.

S. Chen, F. Xi, Z. Liu, B. Bao.  2015.  "Quadrature compressive sampling of multiband radar signals at sub-Landau rate". 2015 IEEE International Conference on Digital Signal Processing (DSP). :234-238.

Sampling multiband radar signals is an essential issue of multiband/multifunction radar. This paper proposes a multiband quadrature compressive sampling (MQCS) system to perform the sampling at sub-Landau rate. The MQCS system randomly projects the multiband signal into a compressive multiband one by modulating each subband signal with a low-pass signal and then samples the compressive multiband signal at Landau-rate with output of compressive measurements. The compressive inphase and quadrature (I/Q) components of each subband are extracted from the compressive measurements respectively and are exploited to recover the baseband I/Q components. As effective bandwidth of the compressive multiband signal is much less than that of the received multiband one, the sampling rate is much less than Landau rate of the received signal. Simulation results validate that the proposed MQCS system can effectively acquire and reconstruct the baseband I/Q components of the multiband signals.

Lomotey, R.K., Deters, R..  2014.  Terms Mining in Document-Based NoSQL: Response to Unstructured Data. Big Data (BigData Congress), 2014 IEEE International Congress on. :661-668.

Unstructured data mining has become topical recently due to the availability of high-dimensional and voluminous digital content (known as "Big Data") across the enterprise spectrum. The Relational Database Management Systems (RDBMS) have been employed over the past decades for content storage and management, but, the ever-growing heterogeneity in today's data calls for a new storage approach. Thus, the NoSQL database has emerged as the preferred storage facility nowadays since the facility supports unstructured data storage. This creates the need to explore efficient data mining techniques from such NoSQL systems since the available tools and frameworks which are designed for RDBMS are often not directly applicable. In this paper, we focused on topics and terms mining, based on clustering, in document-based NoSQL. This is achieved by adapting the architectural design of an analytics-as-a-service framework and the proposal of the Viterbi algorithm to enhance the accuracy of the terms classification in the system. The results from the pilot testing of our work show higher accuracy in comparison to some previously proposed techniques such as the parallel search.

Babour, A., Khan, J.I..  2014.  Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon. Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on. 1:392-399.

In this paper we propose a twitter sentiment analytics that mines for opinion polarity about a given topic. Most of current semantic sentiment analytics depends on polarity lexicons. However, many key tone words are frequently bipolar. In this paper we demonstrate a technique which can accommodate the bipolarity of tone words by context sensitive tone lexicon learning mechanism where the context is modeled by the semantic neighborhood of the main target. Performance analysis shows that ability to contextualize the tone word polarity significantly improves the accuracy.

Hauger, W.K., Olivier, M.S..  2014.  The role of triggers in database forensics. Information Security for South Africa (ISSA), 2014. :1-7.

An aspect of database forensics that has not received much attention in the academic research community yet is the presence of database triggers. Database triggers and their implementations have not yet been thoroughly analysed to establish what possible impact they could have on digital forensic analysis methods and processes. Conventional database triggers are defined to perform automatic actions based on changes in the database. These changes can be on the data level or the data definition level. Digital forensic investigators might thus feel that database triggers do not have an impact on their work. They are simply interrogating the data and metadata without making any changes. This paper attempts to establish if the presence of triggers in a database could potentially disrupt, manipulate or even thwart forensic investigations. The database triggers as defined in the SQL standard were studied together with a number of database trigger implementations. This was done in order to establish what aspects might have an impact on digital forensic analysis. It is demonstrated in this paper that some of the current database forensic analysis methods are impacted by the possible presence of certain types of triggers in a database. Furthermore, it finds that the forensic interpretation and attribution processes should be extended to include the handling and analysis of database triggers if they are present in a database.

Putra, M.S.A., Budiman, G., Novamizanti, L..  2014.  Implementation of steganography using LSB with encrypted and compressed text using TEA-LZW on Android. Computer, Control, Informatics and Its Applications (IC3INA), 2014 International Conference on. :93-98.

The development of data communications enabling the exchange of information via mobile devices more easily. Security in the exchange of information on mobile devices is very important. One of the weaknesses in steganography is the capacity of data that can be inserted. With compression, the size of the data will be reduced. In this paper, designed a system application on the Android platform with the implementation of LSB steganography and cryptography using TEA to the security of a text message. The size of this text message may be reduced by performing lossless compression technique using LZW method. The advantages of this method is can provide double security and more messages to be inserted, so it is expected be a good way to exchange information data. The system is able to perform the compression process with an average ratio of 67.42 %. Modified TEA algorithm resulting average value of avalanche effect 53.8%. Average result PSNR of stego image 70.44 dB. As well as average MOS values is 4.8.

Shakeri, S., Leus, G..  2014.  Underwater ultra-wideband fingerprinting-based sparse localization. Signal Processing Advances in Wireless Communications (SPAWC), 2014 IEEE 15th International Workshop on. :140-144.

In this work, a new fingerprinting-based localization algorithm is proposed for an underwater medium by utilizing ultra-wideband (UWB) signals. In many conventional underwater systems, localization is accomplished by utilizing acoustic waves. On the other hand, electromagnetic waves haven't been employed for underwater localization due to the high attenuation of the signal in water. However, it is possible to use UWB signals for short-range underwater localization. In this work, the feasibility of performing localization for an underwater medium is illustrated by utilizing a fingerprinting-based localization approach. By employing the concept of compressive sampling, we propose a sparsity-based localization method for which we define a system model exploiting the spatial sparsity.

Naini, R., Moulin, P..  2014.  Fingerprint information maximization for content identification. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. :3809-3813.

This paper presents a novel design of content fingerprints based on maximization of the mutual information across the distortion channel. We use the information bottleneck method to optimize the filters and quantizers that generate these fingerprints. A greedy optimization scheme is used to select filters from a dictionary and allocate fingerprint bits. We test the performance of this method for audio fingerprinting and show substantial improvements over existing learning based fingerprints.

Moussallam, M., Daudet, L..  2014.  A general framework for dictionary based audio fingerprinting. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. :3077-3081.

Fingerprint-based Audio recognition system must address concurrent objectives. Indeed, fingerprints must be both robust to distortions and discriminative while their dimension must remain to allow fast comparison. This paper proposes to restate these objectives as a penalized sparse representation problem. On top of this dictionary-based approach, we propose a structured sparsity model in the form of a probabilistic distribution for the sparse support. A practical suboptimal greedy algorithm is then presented and evaluated on robustness and recognition tasks. We show that some existing methods can be seen as particular cases of this algorithm and that the general framework allows to reach other points of a Pareto-like continuum.

Chouzenoux, E., Pesquet, J.-C., Florescu, A..  2014.  A multi-parameter optimization approach for complex continuous sparse modelling. Digital Signal Processing (DSP), 2014 19th International Conference on. :817-820.

The main focus of this work is the estimation of a complex valued signal assumed to have a sparse representation in an uncountable dictionary of signals. The dictionary elements are parameterized by a real-valued vector and the available observations are corrupted with an additive noise. By applying a linearization technique, the original model is recast as a constrained sparse perturbed model. The problem of the computation of the involved multiple parameters is addressed from a nonconvex optimization viewpoint. A cost function is defined including an arbitrary Lipschitz differentiable data fidelity term accounting for the noise statistics, and an ℓ0-like penalty. A proximal algorithm is then employed to solve the resulting nonconvex and nonsmooth minimization problem. Experimental results illustrate the good practical performance of the proposed approach when applied to 2D spectrum analysis.