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d Krit, S., Haimoud, E..  2017.  Overview of Firewalls: Types and Policies: Managing Windows Embedded Firewall Programmatically. 2017 International Conference on Engineering MIS (ICEMIS). :1–7.

Due to the increasing threat of network attacks, Firewall has become crucial elements in network security, and have been widely deployed in most businesses and institutions for securing private networks. The function of a firewall is to examine each packet that passes through it and decide whether to letting them pass or halting them based on preconfigured rules and policies, so firewall now is the first defense line against cyber attacks. However most of people doesn't know how firewall works, and the most users of windows operating system doesn't know how to use the windows embedded firewall. This paper explains how firewall works, firewalls types, and all you need to know about firewall policies, then presents a novel application (QudsWall) developed by authors that manages windows embedded firewall and make it easy to use.

D'Lima, N., Mittal, J..  2015.  Password authentication using Keystroke Biometrics. 2015 International Conference on Communication, Information Computing Technology (ICCICT). :1–6.

The majority of applications use a prompt for a username and password. Passwords are recommended to be unique, long, complex, alphanumeric and non-repetitive. These reasons that make passwords secure may prove to be a point of weakness. The complexity of the password provides a challenge for a user and they may choose to record it. This compromises the security of the password and takes away its advantage. An alternate method of security is Keystroke Biometrics. This approach uses the natural typing pattern of a user for authentication. This paper proposes a new method for reducing error rates and creating a robust technique. The new method makes use of multiple sensors to obtain information about a user. An artificial neural network is used to model a user's behavior as well as for retraining the system. An alternate user verification mechanism is used in case a user is unable to match their typing pattern.

D. Kergl.  2015.  "Enhancing Network Security by Software Vulnerability Detection Using Social Media Analysis Extended Abstract". 2015 IEEE International Conference on Data Mining Workshop (ICDMW). :1532-1533.

Detecting attacks that are based on unknown security vulnerabilities is a challenging problem. The timely detection of attacks based on hitherto unknown vulnerabilities is crucial for protecting other users and systems from being affected as well. To know the attributes of a novel attack's target system can support automated reconfiguration of firewalls and sending alerts to administrators of other vulnerable targets. We suggest a novel approach of post-incident intrusion detection by utilizing information gathered from real-time social media streams. To accomplish this we take advantage of social media users posting about incidents that affect their user accounts of attacked target systems or their observations about misbehaving online services. Combining knowledge of the attacked systems and reported incidents, we should be able to recognize patterns that define the attributes of vulnerable systems. By matching detected attribute sets with those attributes of well-known attacks, we furthermore should be able to link attacks to already existing entries in the Common Vulnerabilities and Exposures database. If a link to an existing entry is not found, we can assume to have detected an exploitation of an unknown vulnerability, i.e., a zero day exploit or the result of an advanced persistent threat. This finding could also be used to direct efforts of examining vulnerabilities of attacked systems and therefore lead to faster patch deployment.

D. L. Schales, X. Hu, J. Jang, R. Sailer, M. P. Stoecklin, T. Wang.  2015.  "FCCE: Highly scalable distributed Feature Collection and Correlation Engine for low latency big data analytics". 2015 IEEE 31st International Conference on Data Engineering. :1316-1327.

In this paper, we present the design, architecture, and implementation of a novel analysis engine, called Feature Collection and Correlation Engine (FCCE), that finds correlations across a diverse set of data types spanning over large time windows with very small latency and with minimal access to raw data. FCCE scales well to collecting, extracting, and querying features from geographically distributed large data sets. FCCE has been deployed in a large production network with over 450,000 workstations for 3 years, ingesting more than 2 billion events per day and providing low latency query responses for various analytics. We explore two security analytics use cases to demonstrate how we utilize the deployment of FCCE on large diverse data sets in the cyber security domain: 1) detecting fluxing domain names of potential botnet activity and identifying all the devices in the production network querying these names, and 2) detecting advanced persistent threat infection. Both evaluation results and our experience with real-world applications show that FCCE yields superior performance over existing approaches, and excels in the challenging cyber security domain by correlating multiple features and deriving security intelligence.

D. Y. Kao.  2015.  "Performing an APT Investigation: Using People-Process-Technology-Strategy Model in Digital Triage Forensics". 2015 IEEE 39th Annual Computer Software and Applications Conference. 3:47-52.

Taiwan has become the frontline in an emerging cyberspace battle. Cyberattacks from different countries are constantly reported during past decades. The incident of Advanced Persistent Threat (APT) is analyzed from the golden triangle components (people, process and technology) to ensure the application of digital forensics. This study presents a novel People-Process-Technology-Strategy (PPTS) model by implementing a triage investigative step to identify evidence dynamics in digital data and essential information in auditing logs. The result of this study is expected to improve APT investigation. The investigation scenario of this proposed methodology is illustrated by applying to some APT incidents in Taiwan.

D. Zhu, Z. Fan, N. Pang.  2015.  "A Dynamic Supervisory Mechanism of Process Behaviors Based on Dalvik VM". 2015 International Conference on Computational Intelligence and Communication Networks (CICN). :1203-1210.

The threats of smartphone security are mostly from the privacy disclosure and malicious chargeback software which deducting expenses abnormally. They exploit the vulnerabilities of previous permission mechanism to attack to mobile phones, and what's more, it might call hardware to spy privacy invisibly in the background. As the existing Android operating system doesn't support users the monitoring and auditing of system resources, a dynamic supervisory mechanism of process behavior based on Dalvik VM is proposed to solve this problem. The existing android system framework layer and application layer are modified and extended, and special underlying services of system are used to realize a dynamic supervisory on the process behavior of Dalvik VM. Via this mechanism, each process on the system resources and the behavior of each app process can be monitored and analyzed in real-time. It reduces the security threats in system level and positions that which process is using the system resource. It achieves the detection and interception before the occurrence or the moment of behavior so that it protects the private information, important data and sensitive behavior of system security. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach.

da Silva Andrade, Richardson B., Souto Rosa, Nelson.  2019.  MidSecThings: Assurance Solution for Security Smart Homes in IoT. 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE). :171–178.
The interest over building security-based solutions to reduce the vulnerability exploits and mitigate the risks associated with smart homes in IoT is growing. However, our investigation identified to architect and implement distributed security mechanisms is still a challenge because is necessary to handle security and privacy in IoT middleware with a strong focus. Our investigation, it was identified the significant proportion of the systems that did not address security and did not describe the security approach in any meaningful detail. The idea proposed in this work is to provide middleware aim to implement security mechanisms in smart home and contribute as how guide to beginner developers' IoT middleware. The advantages of using MidSecThings are to avoid leakage data, unavailable service, unidentification action and not authorized access over IoT devices in smart home.
Da, Gaofeng, Xu, Maochao, Xu, Shouhuai.  2014.  A New Approach to Modeling and Analyzing Security of Networked Systems. Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :6:1–6:12.

Modeling and analyzing security of networked systems is an important problem in the emerging Science of Security and has been under active investigation. In this paper, we propose a new approach towards tackling the problem. Our approach is inspired by the shock model and random environment techniques in the Theory of Reliability, while accommodating security ingredients. To the best of our knowledge, our model is the first that can accommodate a certain degree of adaptiveness of attacks, which substantially weakens the often-made independence and exponential attack inter-arrival time assumptions. The approach leads to a stochastic process model with two security metrics, and we attain some analytic results in terms of the security metrics.

Dabas, N., Singh, R. P., Kher, G., Chaudhary, V..  2017.  A novel SVD and online sequential extreme learning machine based watermark method for copyright protection. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–5.

For the increasing use of internet, it is equally important to protect the intellectual property. And for the protection of copyright, a blind digital watermark algorithm with SVD and OSELM in the IWT domain has been proposed. During the embedding process, SVD has been applied to the coefficient blocks to get the singular values in the IWT domain. Singular values are modulated to embed the watermark in the host image. Online sequential extreme learning machine is trained to learn the relationship between the original coefficient and the corresponding watermarked version. During the extraction process, this trained OSELM is used to extract the embedded watermark logo blindly as no original host image is required during this process. The watermarked image is altered using various attacks like blurring, noise, sharpening, rotation and cropping. The experimental results show that the proposed watermarking scheme is robust against various attacks. The extracted watermark has very much similarity with the original watermark and works good to prove the ownership.

Dabbaghi Varnosfaderani, Shirin, Kasprzak, Piotr, Pohl, Christof, Yahyapour, Ramin.  2019.  A Flexible and Compatible Model for Supporting Assurance Level through a Central Proxy. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :46–52.

Generally, methods of authentication and identification utilized in asserting users' credentials directly affect security of offered services. In a federated environment, service owners must trust external credentials and make access control decisions based on Assurance Information received from remote Identity Providers (IdPs). Communities (e.g. NIST, IETF and etc.) have tried to provide a coherent and justifiable architecture in order to evaluate Assurance Information and define Assurance Levels (AL). Expensive deployment, limited service owners' authority to define their own requirements and lack of compatibility between heterogeneous existing standards can be considered as some of the unsolved concerns that hinder developers to openly accept published works. By assessing the advantages and disadvantages of well-known models, a comprehensive, flexible and compatible solution is proposed to value and deploy assurance levels through a central entity called Proxy.

Daemen, Joan.  2016.  On Non-uniformity in Threshold Sharings. Proceedings of the 2016 ACM Workshop on Theory of Implementation Security. :41–41.

In threshold schemes one represents each sensitive variable by a number n of shares such that their (usually) bitwise sum equals that variable. These shares are initially generated in such a way that any subset of n-1 shares gives no information about the sensitive variable. Functions (S-boxes, mixing layers, round functions, etc.) are computed on the shares of the inputs resulting in the output as a number of shares. An essential property of a threshold implementation of a function is that each output share is computed from at most n-1 input shares. This is called incompleteness and guarantees that that computation cannot leak information about sensitive variables. The resulting output is then typically subject to some further computation, again in the form of separate, incomplete, computation on shares. For these subsequent computations to not leak information about the sensitive variables, the output of the previous stage must still be uniform. Hence, in an iterative cryptographic primitive such as a block cipher, we need a threshold implementation of the round function that yields a uniformly shared output if its input is uniformly shared. This property of the threshold implementation is called uniformity. Threshold schemes form a good protection mechanism against differential power analysis (DPA). In particular, using it allows building cryptographic hardware that is guaranteed to be unattackable with first-order DPA, assuming certain leakage models of the cryptographic hardware at hand and for a plausible definition of "first order". Constructing an incomplete threshold implementation of a non-linear function is rather straightforward. To offer resistance against first-order DPA, the number of shares equals the algebraic degree of the function plus one. However, constructing one that is at the same time incomplete and uniform may present a challenge. For instance, for the Keccak non-linear layer, incomplete 3-share threshold implementations are easy to generate but no uniform one is known. Exhaustive investigations have been performed on all small S-boxes (3 to 5 bits) and there are many S-boxes for which it is not known to build uniform threshold implementations with d+1 shares if their algebraic degree is d. Uniformity of a threshold implementation is essential in its information-theoretical proof of resistance against first-order DPA. However, given a non-uniform threshold implementation, it is not immediate how to exploit its non-uniformity in an attack. In my talk I discuss the local and global effects of non-uniformity in iterated functions and their significance on the resistance against DPA. I treat methods to quantitatively limit the amount of non-uniformity and to keep it away from where it may be harmful. These techniques are relatively cheap and can reduce non-uniformity to such a low level that it would require an astronomical amount of samples to measure it.

Daesung Choi, Sungdae Hong, Hyoung-Kee Choi.  2014.  A group-based security protocol for Machine Type Communications in LTE-Advanced. Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on. :161-162.

We propose Authentication and Key Agreement (AKA) for Machine Type Communications (MTC) in LTE-Advanced. This protocol is based on an idea of grouping devices so that it would reduce signaling congestion in the access network and overload on the single authentication server. We verified that this protocol is designed to be secure against many attacks by using a software verification tool. Furthermore, performance evaluation suggests that this protocol is efficient with respect to authentication overhead and handover delay.
 

Dahan, Mathieu, Amin, Saurabh.  2015.  Network Flow Routing under Strategic Link Disruptions. arXiv preprint arXiv:1512.09335.

This paper considers a 2-player strategic game for network routing under link disruptions. Player 1 (defender) routes flow through a network to maximize her value of effective flow while facing transportation costs. Player 2 (attacker) simultaneously disrupts one or more links to maximize her value of lost flow but also faces cost of disrupting links. This game is strategically equivalent to a zero-sum game. Linear programming duality and the max-flow min-cut theorem are applied to obtain properties that are satisfied in any mixed Nash equilibrium. In any equilibrium, both players achieve identical payoffs. While the defender's expected transportation cost decreases in attacker's marginal value of lost flow, the attacker's expected cost of attack increases in defender's marginal value of effective flow. Interestingly, the expected amount of effective flow decreases in both these parameters. These results can be viewed as a generalization of the classical max-flow with minimum transportation cost problem to adversarial environments.

Dai, D., Chen, Y., Carns, P., Jenkins, J., Ross, R..  2017.  Lightweight Provenance Service for High-Performance Computing. 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT). :117–129.

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. The need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.

Dai, F., Shi, Y., Meng, N., Wei, L., Ye, Z..  2017.  From Bitcoin to cybersecurity: A comparative study of blockchain application and security issues. 2017 4th International Conference on Systems and Informatics (ICSAI). :975–979.

With the accelerated iteration of technological innovation, blockchain has rapidly become one of the hottest Internet technologies in recent years. As a decentralized and distributed data management solution, blockchain has restored the definition of trust by the embedded cryptography and consensus mechanism, thus providing security, anonymity and data integrity without the need of any third party. But there still exists some technical challenges and limitations in blockchain. This paper has conducted a systematic research on current blockchain application in cybersecurity. In order to solve the security issues, the paper analyzes the advantages that blockchain has brought to cybersecurity and summarizes current research and application of blockchain in cybersecurity related areas. Through in-depth analysis and summary of the existing work, the paper summarizes four major security issues of blockchain and performs a more granular analysis of each problem. Adopting an attribute-based encryption method, the paper also puts forward an enhanced access control strategy.

Dai, Guoxian, Xie, Jin, Fang, Yi.  2017.  Metric-Based Generative Adversarial Network. Proceedings of the 25th ACM International Conference on Multimedia. :672–680.

Existing methods of generative adversarial network (GAN) use different criteria to distinguish between real and fake samples, such as probability [9],energy [44] energy or other losses [30]. In this paper, by employing the merits of deep metric learning, we propose a novel metric-based generative adversarial network (MBGAN), which uses the distance-criteria to distinguish between real and fake samples. Specifically, the discriminator of MBGAN adopts a triplet structure and learns a deep nonlinear transformation, which maps input samples into a new feature space. In the transformed space, the distance between real samples is minimized, while the distance between real sample and fake sample is maximized. Similar to the adversarial procedure of existing GANs, a generator is trained to produce synthesized examples, which are close to real examples, while a discriminator is trained to maximize the distance between real and fake samples to a large margin. Meanwhile, instead of using a fixed margin, we adopt a data-dependent margin [30], so that the generator could focus on improving the synthesized samples with poor quality, instead of wasting energy on well-produce samples. Our proposed method is verified on various benchmarks, such as CIFAR-10, SVHN and CelebA, and generates high-quality samples.

Dai, H., Zhu, X., Yang, G., Yi, X..  2017.  A Verifiable Single Keyword Top-k Search Scheme against Insider Attacks over Cloud Data. 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). :111–116.

With the development of cloud computing and its economic benefit, more and more companies and individuals outsource their data and computation to clouds. Meanwhile, the business way of resource outsourcing makes the data out of control from its owner and results in many security issues. The existing secure keyword search methods assume that cloud servers are curious-but-honest or partial honest, which makes them powerless to deal with the deliberately falsified or fabricated results of insider attacks. In this paper, we propose a verifiable single keyword top-k search scheme against insider attacks which can verify the integrity of search results. Data owners generate verification codes (VCs) for the corresponding files, which embed the ordered sequence information of the relevance scores between files and keywords. Then files and corresponding VCs are outsourced to cloud servers. When a data user performs a keyword search in cloud servers, the qualified result files are determined according to the relevance scores between the files and the interested keyword and then returned to the data user together with a VC. The integrity of the result files is verified by data users through reconstructing a new VC on the received files and comparing it with the received one. Performance evaluation have been conducted to demonstrate the efficiency and result redundancy of the proposed scheme.

Dai, Hong-Ning, Wang, Hao, Xiao, Hong, Zheng, Zibin, Wang, Qiu, Li, Xuran, Zhuge, Xu.  2016.  On Analyzing Eavesdropping Behaviours in Underwater Acoustic Sensor Networks. Proceedings of the 11th ACM International Conference on Underwater Networks & Systems. :53:1–53:2.
Underwater Acoustic Sensor Networks (UWASNs) have the wide of applications with the proliferation of the increasing underwater activities recently. Most of current studies are focused on designing protocols to improve the network performance of WASNs. However, the security of UWASNs is also an important concern since malicious nodes can easily wiretap the information transmitted in UWASNs due to the vulnerability of UWASNs. In this paper, we investigate one of security problems in UWASNs - eavesdropping behaviours. In particular, we propose a general model to quantitatively evaluate the probability of eavesdropping behaviour in UWASNs. Simulation results also validate the accuracy of our proposed model.
Dai, Ting, He, Jingzhu, Gu, Xiaohui, Lu, Shan, Wang, Peipei.  2018.  DScope: Detecting Real-World Data Corruption Hang Bugs in Cloud Server Systems. Proceedings of the ACM Symposium on Cloud Computing. :313-325.

Cloud server systems such as Hadoop and Cassandra have enabled many real-world data-intensive applications running inside computing clouds. However, those systems present many data-corruption and performance problems which are notoriously difficult to debug due to the lack of diagnosis information. In this paper, we present DScope, a tool that statically detects data-corruption related software hang bugs in cloud server systems. DScope statically analyzes I/O operations and loops in a software package, and identifies loops whose exit conditions can be affected by I/O operations through returned data, returned error code, or I/O exception handling. After identifying those loops which are prone to hang problems under data corruption, DScope conducts loop bound and loop stride analysis to prune out false positives. We have implemented DScope and evaluated it using 9 common cloud server systems. Our results show that DScope can detect 42 real software hang bugs including 29 newly discovered software hang bugs. In contrast, existing bug detection tools miss detecting most of those bugs.

Dai, W., Win, M. Z..  2017.  On Protecting Location Secrecy. 2017 International Symposium on Wireless Communication Systems (ISWCS). :31–36.

High-accuracy localization is a prerequisite for many wireless applications. To obtain accurate location information, it is often required to share users' positional knowledge and this brings the risk of leaking location information to adversaries during the localization process. This paper develops a theory and algorithms for protecting location secrecy. In particular, we first introduce a location secrecy metric (LSM) for a general measurement model of an eavesdropper. Compared to previous work, the measurement model accounts for parameters such as channel conditions and time offsets in addition to the positions of users. We determine the expression of the LSM for typical scenarios and show how the LSM depends on the capability of an eavesdropper and the quality of the eavesdropper's measurement. Based on the insights gained from the analysis, we consider a case study in wireless localization network and develop an algorithm that diminish the eavesdropper's capabilities by exploiting the reciprocity of channels. Numerical results show that the proposed algorithm can effectively increase the LSM and protect location secrecy.

Dai, Y. S., Xiang, Y. P., Pan, Y..  2014.  Bionic Autonomic Nervous Systems for Self-Defense Against DoS, Spyware, Malware, Virus, and Fishing. ACM Trans. Auton. Adapt. Syst.. 9:4:1–4:20.

Computing systems and networks become increasingly large and complex with a variety of compromises and vulnerabilities. The network security and privacy are of great concern today, where self-defense against different kinds of attacks in an autonomous and holistic manner is a challenging topic. To address this problem, we developed an innovative technology called Bionic Autonomic Nervous System (BANS). The BANS is analogous to biological nervous system, which consists of basic modules like cyber axon, cyber neuron, peripheral nerve and central nerve. We also presented an innovative self-defense mechanism which utilizes the Fuzzy Logic, Neural Networks, and Entropy Awareness, etc. Equipped with the BANS, computer and network systems can intelligently self-defend against both known and unknown compromises/attacks including denial of services (DoS), spyware, malware, and virus. BANS also enabled multiple computers to collaboratively fight against some distributed intelligent attacks like DDoS. We have implemented the BANS in practice. Some case studies and experimental results exhibited the effectiveness and efficiency of the BANS and the self-defense mechanism.

Dai, Z., Li, Z. Y..  2015.  Fuzzy Optimization of Automobile Supply Chain Network of Considering Risks. 2015 Seventh International Symposium on Parallel Architectures Algorithms and Programming (PAAP). :134–138.

In this paper, an optimization model of automobile supply chain network with risks under fuzzy price is put forward. The supply chain network is composed of component suppliers, plants, and distribution centers. The total costs of automobile supply chain consist of variable costs, fixed costs, and transportation costs. The objective of this study is to minimize the risks of total profits. In order to deal with this model, this paper puts forward an approximation method to transform a continuous fuzzy problem into discrete fuzzy problem. The model is solved using Cplex 12.6. The results show that Cplex 12.6 can perfectly solve this model, the expected value and lower semi-variance of total profits converge with the increasing number of discretization points, the structure of automobile supply chain network keeps unchanged with the increasing number of discretization points.

Dainotti, A., King, A., Claffy, K., Papale, F., Pescape, A..  2015.  Analysis of a #x201c;/0 #x201d; Stealth Scan From a Botnet. Networking, IEEE/ACM Transactions on. 23:341-354.

Botnets are the most common vehicle of cyber-criminal activity. They are used for spamming, phishing, denial-of-service attacks, brute-force cracking, stealing private information, and cyber warfare. Botnets carry out network scans for several reasons, including searching for vulnerable machines to infect and recruit into the botnet, probing networks for enumeration or penetration, etc. We present the measurement and analysis of a horizontal scan of the entire IPv4 address space conducted by the Sality botnet in February 2011. This 12-day scan originated from approximately 3 million distinct IP addresses and used a heavily coordinated and unusually covert scanning strategy to try to discover and compromise VoIP-related (SIP server) infrastructure. We observed this event through the UCSD Network Telescope, a /8 darknet continuously receiving large amounts of unsolicited traffic, and we correlate this traffic data with other public sources of data to validate our inferences. Sality is one of the largest botnets ever identified by researchers. Its behavior represents ominous advances in the evolution of modern malware: the use of more sophisticated stealth scanning strategies by millions of coordinated bots, targeting critical voice communications infrastructure. This paper offers a detailed dissection of the botnet's scanning behavior, including general methods to correlate, visualize, and extrapolate botnet behavior across the global Internet.
 

Dali, L., Mivule, K., El-Sayed, H..  2017.  A heuristic attack detection approach using the \#x201C;least weighted \#x201D; attributes for cyber security data. 2017 Intelligent Systems Conference (IntelliSys). :1067–1073.

The continuous advance in recent cloud-based computer networks has generated a number of security challenges associated with intrusions in network systems. With the exponential increase in the volume of network traffic data, involvement of humans in such detection systems is time consuming and a non-trivial problem. Secondly, network traffic data tends to be highly dimensional, comprising of numerous features and attributes, making classification challenging and thus susceptible to the curse of dimensionality problem. Given such scenarios, the need arises for dimensional reduction, feature selection, combined with machine-learning techniques in the classification of such data. Therefore, as a contribution, this paper seeks to employ data mining techniques in a cloud-based environment, by selecting appropriate attributes and features with the least importance in terms of weight for the classification. Often the standard is to select features with better weights while ignoring those with least weights. In this study, we seek to find out if we can make prediction using those features with least weights. The motivation is that adversaries use stealth to hide their activities from the obvious. The question then is, can we predict any stealth activity of an adversary using the least observed attributes? In this particular study, we employ information gain to select attributes with the lowest weights and then apply machine learning to classify if a combination, in this case, of both source and destination ports are attacked or not. The motivation of this investigation is if attributes that are of least importance can be used to predict if an attack could occur. Our preliminary results show that even when the source and destination port attributes are used in combination with features with the least weights, it is possible to classify such network traffic data and predict if an attack will occur or not.

Dam, Khanh Huu The, Touili, Tayssir.  2018.  Learning Malware Using Generalized Graph Kernels. Proceedings of the 13th International Conference on Availability, Reliability and Security. :28:1–28:6.
Machine learning techniques were extensively applied to learn and detect malware. However, these techniques use often rough abstractions of programs. We propose in this work to use a more precise model for programs, namely extended API call graphs, where nodes correspond to API function calls, edges specify the execution order between the API functions, and edge labels indicate the dependence relation between API functions parameters. To learn such graphs, we propose to use Generalized Random Walk Graph Kernels (combined with Support Vector Machines). We implemented our techniques and obtained encouraging results for malware detection: 96.73% of detection rate with 0.73% of false alarms.