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Kalan, Reza Shokri, Sayit, Muge, Clayman, Stuart.  2019.  Optimal Cache Placement and Migration for Improving the Performance of Virtualized SAND. 2019 IEEE Conference on Network Softwarization (NetSoft). :78–83.

Nowadays, video streaming over HTTP is one of the most dominant Internet applications, using adaptive video techniques. Network assisted approaches have been proposed and are being standardized in order to provide high QoE for the end-users of such applications. SAND is a recent MPEG standard where DASH Aware Network Elements (DANEs) are introduced for this purpose. As web-caches are one of the main components of the SAND architecture, the location and the connectivity of these web-caches plays an important role in the user's QoE. The nature of SAND and DANE provides a good foundation for software controlled virtualized DASH environments, and in this paper, we propose a cache location algorithm and a cache migration algorithm for virtualized SAND deployments. The optimal locations for the virtualized DANEs is determined by an SDN controller and migrates it based on gathered statistics. The performance of the resulting system shows that, when SDN and NFV technologies are leveraged in such systems, software controlled virtualized approaches can provide an increase in QoE.

Yu, Jing, Fu, Yao, Zheng, Yanan, Wang, Zheng, Ye, Xiaojun.  2019.  Test4Deep: An Effective White-Box Testing for Deep Neural Networks. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :16–23.
Current testing for Deep Neural Networks (DNNs) focuses on quantity of test cases but ignores diversity. To the best of our knowledge, DeepXplore is the first white-box framework for Deep Learning testing by triggering differential behaviors between multiple DNNs and increasing neuron coverage to improve diversity. Since it is based on multiple DNNs facing problems that (1) the framework is not friendly to a single DNN, (2) if incorrect predictions made by all DNNs simultaneously, DeepXplore cannot generate test cases. This paper presents Test4Deep, a white-box testing framework based on a single DNN. Test4Deep avoids mistakes of multiple DNNs by inducing inconsistencies between predicted labels of original inputs and that of generated test inputs. Meanwhile, Test4Deep improves neuron coverage to capture more diversity by attempting to activate more inactivated neurons. The proposed method was evaluated on three popular datasets with nine DNNs. Compared to DeepXplore, Test4Deep produced average 4.59% (maximum 10.49%) more test cases that all found errors and faults of DNNs. These test cases got 19.57% more diversity increment and 25.88% increment of neuron coverage. Test4Deep can further be used to improve the accuracy of DNNs by average up to 5.72% (maximum 7.0%).
Yin, Mingyong, Wang, Qixu, Cao, Mingsheng.  2019.  An Attack Vector Evaluation Method for Smart City Security Protection. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–7.
In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.
Yee, George O. M..  2019.  Designing Good Security Metrics. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:580–585.
This paper begins with an introduction to security metrics, describing the need for security metrics, followed by a discussion of the nature of security metrics, including the challenges found with some security metrics used in the past. The paper then discusses what makes a good security metric and proposes a rigorous step-by-step method that can be applied to design good security metrics, and to test existing security metrics to see if they are good metrics. Application examples are included to illustrate the method.
Papakonstantinou, Nikolaos, Linnosmaa, Joonas, Alanen, Jarmo, Bashir, Ahmed Z., O'Halloran, Bryan, Van Bossuyt, Douglas L..  2019.  Early Hybrid Safety and Security Risk Assessment Based on Interdisciplinary Dependency Models. 2019 Annual Reliability and Maintainability Symposium (RAMS). :1–7.
Safety and security of complex critical infrastructures are very important for economic, environmental and social reasons. The complexity of these systems introduces difficulties in the identification of safety and security risks that emerge from interdisciplinary interactions and dependencies. The discovery of safety and security design weaknesses late in the design process and during system operation can lead to increased costs, additional system complexity, delays and possibly undesirable compromises to address safety and security weaknesses.
Meijer, Carlo, van Gastel, Bernard.  2019.  Self-Encrypting Deception: Weaknesses in the Encryption of Solid State Drives. 2019 IEEE Symposium on Security and Privacy (SP). :72–87.
We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.
Malik, Yasir, Campos, Carlos Renato Salim, Jaafar, Fehmi.  2019.  Detecting Android Security Vulnerabilities Using Machine Learning and System Calls Analysis. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :109–113.
Android operating systems have become a prime target for cyber attackers due to security vulnerabilities in the underlying operating system and application design. Recently, anomaly detection techniques are widely studied for security vulnerabilities detection and classification. However, the ability of the attackers to create new variants of existing malware using various masking techniques makes it harder to deploy these techniques effectively. In this research, we present a robust and effective vulnerabilities detection approach based on anomaly detection in a system calls of benign and malicious Android application. The anomaly in our study is type, frequency, and sequence of system calls that represent a vulnerability. Our system monitors the processes of benign and malicious application and detects security vulnerabilities based on the combination of parameters and metrics, i.e., type, frequency and sequence of system calls to classify the process behavior as benign or malign. The detection algorithm detects the anomaly based on the defined scoring function f and threshold ρ. The system refines the detection process by applying machine learning techniques to find a combination of system call metrics and explore the relationship between security bugs and the pattern of system calls detected. The experiment results show the detection rate of the proposed algorithm based on precision, recall, and f-score for different machine learning algorithms.
Belej, Olexander, Nestor, Natalia, Polotai, Orest, Sadeckii, Jan.  2019.  Features of Application of Data Transmission Protocols in Wireless Networks of Sensors. 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT). :317–322.
This article discusses the vulnerabilities and complexity of designing secure IoT-solutions, and then presents proven approaches to protecting devices and gateways. Specifically, security mechanisms such as device authentication (including certificate-based authentication), device authentication, and application a verification of identification are described. The authors consider a protocol of message queue telemetry transport for speech and sensor networks on the Internet, its features, application variants, and characteristic procedures. The principle of "publishersubscriber" is considered. An analysis of information elements and messages is carried out. The urgency of the theme is due to the rapid development of "publisher-subscriber" architecture, for which the protocol is most characteristic.
Rindell, Kalle, Holvitie, Johannes.  2019.  Security Risk Assessment and Management as Technical Debt. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
The endeavor to achieving software security consists of a set of risk-based security engineering processes during software development. In iterative software development, the software design typically evolves as the project matures, and the technical environment may undergo considerable changes. This increases the work load of identifying, assessing and managing the security risk by each iteration, and after every change. Besides security risk, the changes also accumulate technical debt, an allegory for postponed or sub-optimally performed work. To manage the security risk in software development efficiently, and in terms and definitions familiar to software development organizations, the concept of technical debt is extended to contain security debt. To accommodate new technical debt with potential security implications, a security debt management approach is introduced. The selected approach is an extension to portfolio-based technical debt management framework. This includes identifying security risk in technical debt, and also provides means to expose debt by security engineering techniques that would otherwise remained hidden. The proposed approach includes risk-based extensions to prioritization mechanisms in existing technical debt management systems. Identification, management and repayment techniques are presented to identify, assess, and mitigate the security debt.
Ionita, Drd. Irene.  2019.  Cybersecurity concerns on real time monitoring in electrical transmission and distribution systems (SMART GRIDS). 2019 54th International Universities Power Engineering Conference (UPEC). :1–4.
The virtual world does not observe national borders, has no uniform legal system, and does not have a common perception of security and privacy issues. It is however, relatively homogenous in terms of technology.A cyberattack on an energy delivery system can have significant impacts on the availability of a system to perform critical functions as well as the integrity of the system and the confidentiality of sensitive information.
Izurieta, Clemente, Prouty, Mary.  2019.  Leveraging SecDevOps to Tackle the Technical Debt Associated with Cybersecurity Attack Tactics. 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). :33–37.
Context: Managing technical debt (TD) associated with external cybersecurity attacks on an organization can significantly improve decisions made when prioritizing which security weaknesses require attention. Whilst source code vulnerabilities can be found using static analysis techniques, malicious external attacks expose the vulnerabilities of a system at runtime and can sometimes remain hidden for long periods of time. By mapping malicious attack tactics to the consequences of weaknesses (i.e. exploitable source code vulnerabilities) we can begin to understand and prioritize the refactoring of the source code vulnerabilities that cause the greatest amount of technical debt on a system. Goal: To establish an approach that maps common external attack tactics to system weaknesses. The consequences of a weakness associated with a specific attack technique can then be used to determine the technical debt principal of said violation; which can be measured in terms of loss of business rather than source code maintenance. Method: We present a position study that uses Jaccard similarity scoring to examine how 11 malicious attack tactics can relate to Common Weakness Enumerations (CWEs). Results: We conduct a study to simulate attacks, and generate dependency graphs between external attacks and the technical consequences associated with CWEs. Conclusion: The mapping of cyber security attacks to weaknesses allows operational staff (SecDevOps) to focus on deploying appropriate countermeasures and allows developers to focus on refactoring the vulnerabilities with the greatest potential for technical debt.
Todorov, Vassil, Taha, Safouan, Boulanger, Frédéric, Hernandez, Armando.  2019.  Improved Invariant Generation for Industrial Software Model Checking of Time Properties. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). :334–341.
Modern automotive embedded software is mostly designed using model-based design tools such as Simulink or SCADE, and source code is generated automatically from the models. Formal proof using symbolic model checking has been integrated in these tools and can provide a higher assurance by proving safety-critical properties. Our experience shows that proving properties involving time is rather challenging when they involve long durations and timers. These properties are generally not inductive and even advanced techniques such as PDR/IC3 are unable to handle them on production models in reasonable time. In this paper, we first present our industrial use case and comment on the results obtained with the existing model checkers. Then we present our invariant generator and methodology for selecting invariants according to physical dimensions. They enable the proof of properties with long-running timers. Finally, we discuss their implementation and benchmarks.
Koutroumpouchos, Nikos, Ntantogian, Christoforos, Menesidou, Sofia-Anna, Liang, Kaitai, Gouvas, Panagiotis, Xenakis, Christos, Giannetsos, Thanassis.  2019.  Secure Edge Computing with Lightweight Control-Flow Property-based Attestation. 2019 IEEE Conference on Network Softwarization (NetSoft). :84–92.
The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively verify software- and device-integrity in order to detect run-time modifications. Towards this direction, remote attestation has been proposed as a promising defense mechanism. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. However, this family of solutions do not capture the real-time requirements of industrial IoT applications and suffer from scalability and efficiency issues. In this paper, we present a lightweight dynamic control-flow property-based attestation architecture (CFPA) that can be applied on both resource-constrained edge and cloud devices and services. It is a first step towards a new line of security mechanisms that enables the provision of control-flow attestation of only those specific, critical software components that are comparatively small, simple and limited in function, thus, allowing for a much more efficient verification. Our goal is to enhance run-time software integrity and trustworthiness with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security do not hinder the deployment of intelligent edge computing systems.
Zheng, Junjun, Okamura, Hiroyuki, Dohi, Tadashi.  2019.  Security Evaluation of a VM-Based Intrusion-Tolerant System with Pull-Type Patch Management. 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE). :156–163.
Computer security has gained more and more attention in a public over the last years, since computer systems are suffering from significant and increasing security threats that cause security breaches by exploiting software vulnerabilities. The most efficient way to ensure the system security is to patch the vulnerable system before a malicious attack occurs. Besides the commonly-used push-type patch management, the pull-type patch management is also adopted. The main issues in the pull-type patch management are two-fold; when to check the vulnerability information and when to apply a patch? This paper considers the security patch management for a virtual machine (VM) based intrusion tolerant system (ITS), where the system undergoes the patch management with a periodic vulnerability checking strategy, and evaluates the system security from the availability aspect. A composite stochastic reward net (SRN) model is applied to capture the attack behavior of adversary and the defense behaviors of system. Two availability measures; interval availability and point-wise availability are formulated to quantify the system security via phase expansion. The proposed approach and metrics not only enable us to quantitatively assess the system security, but also provide insights on the patch management. In numerical experiments, we evaluate effects of the intrusion rate and the number of vulnerability checking on the system security.
Chechik, Marsha.  2019.  Uncertain Requirements, Assurance and Machine Learning. 2019 IEEE 27th International Requirements Engineering Conference (RE). :2–3.
From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. Governing bodies and standards organizations have responded to this trend by creating regulations and standards to address issues such as safety, security and privacy. In this environment, the compliance of software development to standards and regulations has emerged as a key requirement. Compliance claims and arguments are often captured in assurance cases, with linked evidence of compliance. Evidence can come from testcases, verification proofs, human judgement, or a combination of these. That is, we try to build (safety-critical) systems carefully according to well justified methods and articulate these justifications in an assurance case that is ultimately judged by a human. Yet software is deeply rooted in uncertainty making pragmatic assurance more inductive than deductive: most of complex open-world functionality is either not completely specifiable (due to uncertainty) or it is not cost-effective to do so, and deductive verification cannot happen without specification. Inductive assurance, achieved by sampling or testing, is easier but generalization from finite set of examples cannot be formally justified. And of course the recent popularity of constructing software via machine learning only worsens the problem - rather than being specified by predefined requirements, machine-learned components learn existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, this ability is extremely useful for hard-to specify concepts, e.g., the definition of a pedestrian in a pedestrian detection component of a vehicle. On the other, safety assessment and assurance of such components becomes very challenging. In this talk, I focus on two specific approaches to arguing about safety and security of software under uncertainty. The first one is a framework for managing uncertainty in assurance cases (for "conventional" and "machine-learned" systems) by systematically identifying, assessing and addressing it. The second is recent work on supporting development of requirements for machine-learned components in safety-critical domains.
Muka, Romina, Haugli, Fredrik Bakkevig, Vefsnmo, Hanne, Heegaard, Poul E..  2019.  Information Inconsistencies in Smart Distribution Grids under Different Failure Causes modelled by Stochastic Activity Networks. 2019 AEIT International Annual Conference (AEIT). :1–6.
The ongoing digitalization of the power distribution grid will improve the operational support and automation which is believed to increase the system reliability. However, in an integrated and interdependent cyber-physical system, new threats appear which must be understood and dealt with. Of particular concern, in this paper, is the causes of an inconsistent view between the physical system (here power grid) and the Information and Communication Technology (ICT) system (here Distribution Management System). In this paper we align the taxonomy used in International Electrotechnical Commission (power eng.) and International Federation for Information Processing (ICT community), define a metric for inconsistencies, and present a modelling approach using Stochastic Activity Networks to assess the consequences of inconsistencies. The feasibility of the approach is demonstrated in a simple use case.
Auer, Lukas, Skubich, Christian, Hiller, Matthias.  2019.  A Security Architecture for RISC-V based IoT Devices. 2019 Design, Automation Test in Europe Conference Exhibition (DATE). :1154–1159.
New IoT applications are demanding for more and more performance in embedded devices while their deployment and operation poses strict power constraints. We present the security concept for a customizable Internet of Things (IoT) platform based on the RISC-V ISA and developed by several Fraunhofer Institutes. It integrates a range of peripherals with a scalable computing subsystem as a three dimensional System-in-Package (3D-SiP). The security features aim for a medium security level and target the requirements of the IoT market. Our security architecture extends given implementations to enable secure deployment, operation, and update. Core security features are secure boot, an authenticated watchdog timer, and key management. The Universal Sensor Platform (USeP) SoC is developed for GLOBALFOUNDRIES' 22FDX technology and aims to provide a platform for Small and Medium-sized Enterprises (SMEs) that typically do not have access to advanced microelectronics and integration know-how, and are therefore limited to Commercial Off-The-Shelf (COTS) products.
Talukder, Md Arabin Islam, Shahriar, Hossain, Qian, Kai, Rahman, Mohammad, Ahamed, Sheikh, Wu, Fan, Agu, Emmanuel.  2019.  DroidPatrol: A Static Analysis Plugin For Secure Mobile Software Development. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:565–569.

While the number of mobile applications are rapidly growing, these applications are often coming with numerous security flaws due to the lack of appropriate coding practices. Security issues must be addressed earlier in the development lifecycle rather than fixing them after the attacks because the damage might already be extensive. Early elimination of possible security vulnerabilities will help us increase the security of our software and mitigate or reduce the potential damages through data losses or service disruptions caused by malicious attacks. However, many software developers lack necessary security knowledge and skills required at the development stage, and Secure Mobile Software Development (SMSD) is not yet well represented in academia and industry. In this paper, we present a static analysis-based security analysis approach through design and implementation of a plugin for Android Development Studio, namely DroidPatrol. The proposed plugins can support developers by providing list of potential vulnerabilities early.

Rahman, Akond, Parnin, Chris, Williams, Laurie.  2019.  The Seven Sins: Security Smells in Infrastructure as Code Scripts. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). :164–175.

Practitioners use infrastructure as code (IaC) scripts to provision servers and development environments. While developing IaC scripts, practitioners may inadvertently introduce security smells. Security smells are recurring coding patterns that are indicative of security weakness and can potentially lead to security breaches. The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code (IaC) scripts through an empirical study of security smells in IaC scripts. We apply qualitative analysis on 1,726 IaC scripts to identify seven security smells. Next, we implement and validate a static analysis tool called Security Linter for Infrastructure as Code scripts (SLIC) to identify the occurrence of each smell in 15,232 IaC scripts collected from 293 open source repositories. We identify 21,201 occurrences of security smells that include 1,326 occurrences of hard-coded passwords. We submitted bug reports for 1,000 randomly-selected security smell occurrences. We obtain 212 responses to these bug reports, of which 148 occurrences were accepted by the development teams to be fixed. We observe security smells can have a long lifetime, e.g., a hard-coded secret can persist for as long as 98 months, with a median lifetime of 20 months.

Ding, Steven H. H., Fung, Benjamin C. M., Charland, Philippe.  2019.  Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization. 2019 IEEE Symposium on Security and Privacy (SP). :472–489.

Reverse engineering is a manually intensive but necessary technique for understanding the inner workings of new malware, finding vulnerabilities in existing systems, and detecting patent infringements in released software. An assembly clone search engine facilitates the work of reverse engineers by identifying those duplicated or known parts. However, it is challenging to design a robust clone search engine, since there exist various compiler optimization options and code obfuscation techniques that make logically similar assembly functions appear to be very different. A practical clone search engine relies on a robust vector representation of assembly code. However, the existing clone search approaches, which rely on a manual feature engineering process to form a feature vector for an assembly function, fail to consider the relationships between features and identify those unique patterns that can statistically distinguish assembly functions. To address this problem, we propose to jointly learn the lexical semantic relationships and the vector representation of assembly functions based on assembly code. We have developed an assembly code representation learning model \textbackslashemphAsm2Vec. It only needs assembly code as input and does not require any prior knowledge such as the correct mapping between assembly functions. It can find and incorporate rich semantic relationships among tokens appearing in assembly code. We conduct extensive experiments and benchmark the learning model with state-of-the-art static and dynamic clone search approaches. We show that the learned representation is more robust and significantly outperforms existing methods against changes introduced by obfuscation and optimizations.

Cheng, Xiao, Wang, Haoyu, Hua, Jiayi, Zhang, Miao, Xu, Guoai, Yi, Li, Sui, Yulei.  2019.  Static Detection of Control-Flow-Related Vulnerabilities Using Graph Embedding. 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS). :41–50.

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.

Yao, Chuhao, Wang, Jiahong, Kodama, Eiichiro.  2019.  A Spam Review Detection Method by Verifying Consistency among Multiple Review Sites. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :2825–2830.

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.

Taib, Abidah Mat, Othman, Nor Arzami, Hamid, Ros Syamsul, Halim, Iman Hazwam Abd.  2019.  A Learning Kit on IPv6 Deployment and Its Security Challenges for Neophytes. 2019 21st International Conference on Advanced Communication Technology (ICACT). :419–424.
Understanding the IP address depletion and the importance of handling security issues in IPv6 deployment can make IT personnel becomes more functional and helpful to the organization. It also applied to the management people who are responsible for approving the budget or organization policy related to network security. Unfortunately, new employees or fresh graduates may not really understand the challenge related to IPv6 deployment. In order to be equipped with appropriate knowledge and skills, these people may require a few weeks of attending workshops or training. Thus, of course involving some implementation cost as well as sacrificing allocated working hours. As an alternative to save cost and to help new IT personnel become quickly educated and familiar with IPv6 deployment issues, this paper presented a learning kit that has been designed to include self-learning features that can help neophytes to learn about IPv6 at their own pace. The kit contains some compact notes, brief security model and framework as well as a guided module with supporting quizzes to maintain a better understanding of the topics. Since IPv6 is still in the early phase of implementation in most of developing countries, this kit can be an additional assisting tool to accelerate the deployment of IPv6 environment in any organization. The kit also can be used by teachers and trainers as a supporting tool in the classroom. The pre-alpha testing has attracted some potential users and the findings proved their acceptance. The kit has prospective to be further enhanced and commercialized.
Saadeh, Huda, Almobaideen, Wesam, Sabri, Khair Eddin, Saadeh, Maha.  2019.  Hybrid SDN-ICN Architecture Design for the Internet of Things. 2019 Sixth International Conference on Software Defined Systems (SDS). :96–101.
Internet of Things (IoT) impacts the current network with many challenges due to the variation, heterogeneity of its devices and running technologies. For those reasons, monitoring and controlling network efficiently can rise the performance of the network and adapts network techniques according to environment measurements. This paper proposes a new privacy aware-IoT architecture that combines the benefits of both Information Centric Network (ICN) and Software Defined Network (SDN) paradigms. In this architecture controlling functionalities are distributed over multiple planes: operational plane which is considered as smart ICN data plane with Controllers that control local clusters, tactical plane which is an Edge environment to take controlling decisions based on small number of clusters, and strategic plane which is a cloud controlling environment to make long-term decision that affects the whole network. Deployment options of this architecture is discussed and SDN enhancement due to in-network caching is evaluated.
Bardia, Vivek, Kumar, C.R.S..  2017.  Process trees amp; service chains can serve us to mitigate zero day attacks better. 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI). :280–284.
With technology at our fingertips waiting to be exploited, the past decade saw the revolutionizing Human Computer Interactions. The ease with which a user could interact was the Unique Selling Proposition (USP) of a sales team. Human Computer Interactions have many underlying parameters like Data Visualization and Presentation as some to deal with. With the race, on for better and faster presentations, evolved many frameworks to be widely used by all software developers. As the need grew for user friendly applications, more and more software professionals were lured into the front-end sophistication domain. Application frameworks have evolved to such an extent that with just a few clicks and feeding values as per requirements we are able to produce a commercially usable application in a few minutes. These frameworks generate quantum lines of codes in minutes which leaves a contrail of bugs to be discovered in the future. We have also succumbed to the benchmarking in Software Quality Metrics and have made ourselves comfortable with buggy software's to be rectified in future. The exponential evolution in the cyber domain has also attracted attackers equally. Average human awareness and knowledge has also improved in the cyber domain due to the prolonged exposure to technology for over three decades. As the attack sophistication grows and zero day attacks become more popular than ever, the suffering end users only receive remedial measures in spite of the latest Antivirus, Intrusion Detection and Protection Systems installed. We designed a software to display the complete services and applications running in users Operating System in the easiest perceivable manner aided by Computer Graphics and Data Visualization techniques. We further designed a study by empowering the fence sitter users with tools to actively participate in protecting themselves from threats. The designed threats had impressions from the complete threat canvas in some form or other restricted to systems functioning. Network threats and any sort of packet transfer to and from the system in form of threat was kept out of the scope of this experiment. We discovered that end users had a good idea of their working environment which can be used exponentially enhances machine learning for zero day threats and segment the unmarked the vast threat landscape faster for a more reliable output.