Visible to the public Biblio

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2021-06-01
Zheng, Wenbo, Yan, Lan, Gou, Chao, Wang, Fei-Yue.  2020.  Webly Supervised Knowledge Embedding Model for Visual Reasoning. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :12442–12451.
Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-available web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.
2021-04-08
Yaseen, Q., Panda, B..  2012.  Tackling Insider Threat in Cloud Relational Databases. 2012 IEEE Fifth International Conference on Utility and Cloud Computing. :215—218.
Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.
Althebyan, Q..  2019.  A Mobile Edge Mitigation Model for Insider Threats: A Knowledgebase Approach. 2019 International Arab Conference on Information Technology (ACIT). :188—192.
Taking care of security at the cloud is a major issue that needs to be carefully considered and solved for both individuals as well as organizations. Organizations usually expect more trust from employees as well as customers in one hand. On the other hand, cloud users expect their private data is maintained and secured. Although this must be case, however, some malicious outsiders of the cloud as well as malicious insiders who are cloud internal users tend to disclose private data for their malicious uses. Although outsiders of the cloud should be a concern, however, the more serious problems come from Insiders whose malicious actions are more serious and sever. Hence, insiders' threats in the cloud should be the top most problem that needs to be tackled and resolved. This paper aims to find a proper solution for the insider threat problem in the cloud. The paper presents a Mobile Edge Computing (MEC) mitigation model as a solution that suits the specialized nature of this problem where the solution needs to be very close to the place where insiders reside. This in fact gives real-time responses to attack, and hence, reduces the overhead in the cloud.
2021-03-29
Ouiazzane, S., Addou, M., Barramou, F..  2020.  Toward a Network Intrusion Detection System for Geographic Data. 2020 IEEE International conference of Moroccan Geomatics (Morgeo). :1—7.

The objective of this paper is to propose a model of a distributed intrusion detection system based on the multi-agent paradigm and the distributed file system (HDFS). Multi-agent systems (MAS) are very suitable to intrusion detection systems as they can address the issue of geographic data security in terms of autonomy, distribution and performance. The proposed system is based on a set of autonomous agents that cooperate and collaborate with each other to effectively detect intrusions and suspicious activities that may impact geographic information systems. Our system allows the detection of known and unknown computer attacks without any human intervention (Security Experts) unlike traditional intrusion detection systems that rely on knowledge bases as a mechanism to detect known attacks. The proposed model allows a real time detection of known and unknown attacks within large networks hosting geographic data.

2021-03-04
Knyazeva, N., Khorkov, D., Vostretsova, E..  2020.  Building Knowledge Bases for Timestamp Changes Detection Mechanisms in MFT Windows OS. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :553—556.

File timestamps do not receive much attention from information security specialists and computer forensic scientists. It is believed that timestamps are extremely easy to fake, and the system time of a computer can be changed. However, operating system for synchronizing processes and working with file objects needs accurate time readings. The authors estimate that several million timestamps can be stored on the logical partition of a hard disk with the NTFS. The MFT stores four timestamps for each file object in \$STANDARDİNFORMATION and \$FILE\_NAME attributes. Furthermore, each directory in the İNDEX\_ROOT or İNDEX\_ALLOCATION attributes contains four more timestamps for each file within it. File timestamps are set and changed as a result of file operations. At the same time, some file operations differently affect changes in timestamps. This article presents the results of the tool-based observation over the creation and update of timestamps in the MFT resulting from the basic file operations. Analysis of the results is of interest with regard to computer forensic science.

2021-01-22
Golushko, A. P., Zhukov, V. G..  2020.  Application of Advanced Persistent Threat Actors` Techniques aor Evaluating Defensive Countermeasures. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :312—317.
This paper describes research results of the possibility of developing a methodology to implement systematic knowledge about adversaries` tactics and techniques into the process of determining requirements for information security system and evaluating defensive countermeasures.
2020-10-12
Rudd-Orthner, Richard N M, Mihaylova, Lyudmilla.  2019.  An Algebraic Expert System with Neural Network Concepts for Cyber, Big Data and Data Migration. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1–6.

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure.

2020-07-30
TÎTU, Mihail Aurel, POP, Alina Bianca, ŢÎŢU, Ştefan.  2018.  The correlation between intellectual property management and quality management in the modern knowledge-based economy. 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1—6.
The aim of this research paper is to highlight the intellectual property place and role within an industrial knowledge-based organization which performs design activities. The research begins by presenting the importance of integrating intellectual property policy implementation with quality policy. The research is based on the setting of objectives in the intellectual property field. This research also establishes some intellectual property strategies, and improvement measures for intellectual property protection management. The basis for these activities is correlation of the quality policy with an intellectual property policy, as well as the point of strength identified in the studied organization. The issues discussed in this scientific paper conclude on the possibility of the implementation of standards in the intellectual property field.
2020-04-06
Chen, Chia-Mei, Wang, Shi-Hao, Wen, Dan-Wei, Lai, Gu-Hsin, Sun, Ming-Kung.  2019.  Applying Convolutional Neural Network for Malware Detection. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). :1—5.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

2020-04-03
Mishra, Menaka, Upadhyay, A.K..  2019.  Need of Private and Public Sector Information Security. 2019 9th International Conference on Cloud Computing, Data Science Engineering (Confluence). :168—173.

In this research paper author surveys the need of data protection from intelligent systems in the private and public sectors. For this, she identifies that the Smart Information Security Intel processes needs to be the suggestive key policy for both sectors of governance either public or private. The information is very sensitive for any organization. When the government offices are concerned, information needs to be abstracted and encapsulated so that there is no information stealing. For this purposes, the art of skill set and new optimized technology needs to be stationed. Author identifies that digital bar-coded air port like security using conveyor belts and digital bar-coded conveyor boxes to scan switched ON articles like internet of things needs to be placed. As otherwise, there can potentially be data, articles or information stealing from the operational sites where access is unauthorized. Such activities shall need to be scrutinized, minutely. The biometric such as fingerprints, iris, voice and face recognition pattern updates in the virtual data tables must be taken to keep data entry-exit log up to-date. The information technicians of the sentinel systems must help catch the anomalies in the professional working time in private and public sectors if there is red flag as indicator. The author in this research paper shall discuss in detail what we shall station, how we shall station and what all measures we might need to undertake to safeguard the stealing of sensitive information from the organizations like administration buildings, government buildings, educational schools, hospitals, courts, private buildings, banks and all other offices nation-wide. The TO-BE new processes shall make the AS-IS office system more information secured, data protected and personnel security stronger.

2020-03-16
Udod, Kyryll, Kushnarenko, Volodymyr, Wesner, Stefan, Svjatnyj, Volodymyr.  2019.  Preservation System for Scientific Experiments in High Performance Computing: Challenges and Proposed Concept. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). 2:809–813.
Continuously growing amount of research experiments using High Performance Computing (HPC) leads to the questions of research data management and in particular how to preserve a scientific experiment including all related data for long term for its future reproduction. This paper covers some challenges and possible solutions related to the preservation of scientific experiments on HPC systems and represents a concept of the preservation system for HPC computations. Storage of the experiment itself with some related data is not only enough for its future reproduction, especially in the long term. For that case preservation of the whole experiment's environment (operating system, used libraries, environment variables, input data, etc.) via containerization technology (e.g. using Docker, Singularity) is proposed. This approach allows to preserve the entire environment, but is not always possible on every HPC system because of security issues. And it also leaves a question, how to deal with commercial software that was used within the experiment. As a possible solution we propose to run a preservation process outside of the computing system on the web-server and to replace all commercial software inside the created experiment's image with open source analogues that should allow future reproduction of the experiment without any legal issues. The prototype of such a system was developed, the paper provides the scheme of the system, its main features and describes the first experimental results and further research steps.
2020-02-10
Dostálek, Libor.  2019.  Multi-Factor Authentication Modeling. 2019 9th International Conference on Advanced Computer Information Technologies (ACIT). :443–446.
The work defines a multi-factor authentication model in case the application supports multiple authentication factors. The aim of this modeling is to find acceptable authentication methods sufficient to access specifically qualified information. The core of the proposed model is risk-based authentication. Results of simulations of some key scenarios often used in practice are also presented.
2019-12-16
Karve, Shreya, Nagmal, Arati, Papalkar, Sahil, Deshpande, S. A..  2018.  Context Sensitive Conversational Agent Using DNN. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). :475–478.
We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.
Alam, Mehreen.  2018.  Neural Encoder-Decoder based Urdu Conversational Agent. 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :901–905.
Conversational agents have very much become part of our lives since the renaissance of neural network based "neural conversational agents". Previously used manually annotated and rule based methods lacked the scalability and generalization capabilities of the neural conversational agents. A neural conversational agent has two parts: at one end an encoder understands the question while the other end a decoder prepares and outputs the corresponding answer to the question asked. Both the parts are typically designed using recurrent neural network and its variants and trained in an end-to-end fashion. Although conversation agents for other languages have been developed, Urdu language has seen very less progress in building of conversational agents. Especially recent state of the art neural network based techniques have not been explored yet. In this paper, we design an attention driven deep encoder-decoder based neural conversational agent for Urdu language. Overall, we make following contributions we (i) create a dataset of 5000 question-answer pairs, and (ii) present a new deep encoder-decoder based conversational agent for Urdu language. For our work, we limit the knowledge base of our agent to general knowledge regarding Pakistan. Our best model has the BLEU score of 58 and gives syntactically and semantically correct answers in majority of the cases.
2019-12-09
Cococcioni, Marco.  2018.  Computational Intelligence in Maritime Security and Defense: Challenges and Opportunities. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1964-1967.

Computational Intelligence (CI) has a great potential in Security & Defense (S&D) applications. Nevertheless, such potential seems to be still under exploited. In this work we first review CI applications in the maritime domain, done in the past decades by NATO Nations. Then we discuss challenges and opportunities for CI in S&D. Finally we argue that a review of the academic training of military officers is highly recommendable, in order to allow them to understand, model and solve new problems, using CI techniques.

2019-05-01
Naik, N., Shang, C., Shen, Q., Jenkins, P..  2018.  Vigilant Dynamic Honeypot Assisted by Dynamic Fuzzy Rule Interpolation. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1731–1738.

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

2019-03-04
Lin, Y., Qi, Z., Wu, H., Yang, Z., Zhang, J., Wenyin, L..  2018.  CoderChain: A BlockChain Community for Coders. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :246–247.
An online community based on blockchain is proposed for software developers to share, assess, and learn codes and other codes or software related knowledge. It involves three modules or roles, namely: developer (or coder, or more generally, knowledge contributor), code (or knowledge contribution), and jury (or assessor, who is usually a developer with advanced skills), in addition to the blockchain based database. Each full node of the blockchain hosts a copy of all activities of developers in such community, including uploading contributions, assessing others' contributions, and conducting transactions. Smart contracts are applicable to automate transactions after code assessment or other related activities. The system aims to assess and improve the value of codes accurately, stimulate the creativity of the developers, and improve software development efficiency, so as to establish a virtuous cycle of a software development community.
Zeinali, M., Hadavi, M. A..  2018.  Threat Extraction Method Based on UML Software Description. 2018 15th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :1–8.
Threat modeling is one of the best practices to secure software development. A primary challenge for using this practice is how to extract threats. Existing threat extraction methods to this purpose are mainly based on penetration tests or vulnerability databases. This imposes a non-automated timeconsuming process, which fully relies on the human knowledge and expertise. In this paper, a method is presented, which can extract the threats to a software system based on the existing description of the software behavior. We elaborately describe software behavior with sequence diagrams enriched by security relevant attributes. To enrich a sequence diagram, some attributes and their associated values are added to the diagram elements and the communication between them. We have also developed a threat knowledge base from reliable sources such as CWE and CAPEC lists. Every threat in the knowledge base is described according to its occurrence conditions in the software. To extract threats of a software system, the enriched sequence diagrams describing the software behavior are matched with the threat rules in our knowledge base using a simple inference process. Results in a set of potential threats for the software system. The proposed method is applied on a software application to extract its threats. Our case study indicates the effectiveness of the proposed method compared to other existing methods.
Iqbal, A., Mahmood, F., Shalaginov, A., Ekstedt, M..  2018.  Identification of Attack-based Digital Forensic Evidences for WAMPAC Systems. 2018 IEEE International Conference on Big Data (Big Data). :3079–3087.
Power systems domain has generally been very conservative in terms of conducting digital forensic investigations, especially so since the advent of smart grids. This lack of research due to a multitude of challenges has resulted in absence of knowledge base and resources to facilitate such an investigation. Digitalization in the form of smart grids is upon us but in case of cyber-attacks, attribution to such attacks is challenging and difficult if not impossible. In this research, we have identified digital forensic artifacts resulting from a cyber-attack on Wide Area Monitoring, Protection and Control (WAMPAC) systems, which will help an investigator attribute an attack using the identified evidences. The research also shows the usage of sandboxing for digital forensics along with hardware-in-the-loop (HIL) setup. This is first of its kind effort to identify and acquire all the digital forensic evidences for WAMPAC systems which will ultimately help in building a body of knowledge and taxonomy for power system forensics.
2019-01-16
Akhtar, U., Lee, S..  2018.  Adaptive Cache Replacement in Efficiently Querying Semantic Big Data. 2018 IEEE International Conference on Web Services (ICWS). :367–370.
This paper addresses the problem of querying Knowledge bases (KBs) that store semantic big data. For efficiently querying data the most important factor is cache replacement policy, which determines the overall query response. As cache is limited in size, less frequently accessed data should be removed to provide more space to hot triples (frequently accessed). So, to achieve a similar performance to RDBMS, we proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Moreover, performance bottleneck of triplestore, makes realworld application difficult. To achieve a closer performance similar to RDBMS, we have proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Our proposed algorithm effectively replaces cache with high accuracy. To implement cache replacement policy, we have applied exponential smoothing, a forecast method, to collect most frequently accessed triples. The evaluation result shows that the proposed scheme outperforms the existing cache replacement policies, such as LRU (least recently used) and LFU (least frequently used), in terms of higher hit rates and less time overhead.
2018-01-10
Thaler, S., Menkonvski, V., Petkovic, M..  2017.  Towards a neural language model for signature extraction from forensic logs. 2017 5th International Symposium on Digital Forensic and Security (ISDFS). :1–6.
Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.
Barreira, R., Pinheiro, V., Furtado, V..  2017.  A framework for digital forensics analysis based on semantic role labeling. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :66–71.
This article describes a framework for semantic annotation of texts that are submitted for forensic analysis, based on Frame Semantics, and a knowledge base of Forensic Frames - FrameFOR. We demonstrate through experimental evaluations that the application of the Semantic Role Labeling (SRL) techniques and Natural Language Processing (NLP) in digital forensic increases the performance of the forensic experts in terms of agility, precision and recall.
Meltsov, V. Y., Lesnikov, V. A., Dolzhenkova, M. L..  2017.  Intelligent system of knowledge control with the natural language user interface. 2017 International Conference "Quality Management,Transport and Information Security, Information Technologies" (IT QM IS). :671–675.
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The paper considers the possibility and necessity of using in modern control and training systems with a natural language interface methods and mechanisms, characteristic for knowledge processing systems. This symbiosis assumes the introduction of specialized inference machines into the testing systems. For the effective operation of such an intelligent interpreter, it is necessary to “translate” the user's answers into one of the known forms of the knowledge representation, for example, into the expressions (rules) of the first-order predicate calculus. A lexical processor, performing morphological, syntactic and semantic analysis, solves this task. To simplify further work with the rules, the Skolem-transformation is used, which allows to get rid of quantifiers and to present semantic structures in the form of sequents (clauses, disjuncts). The basic principles of operation of the inference machine are described, which is the main component of the developed intellectual subsystem. To improve the performance of the machine, one of the fastest methods was chosen - a parallel method of deductive inference based on the division of clauses. The parallelism inherent in the method, and the use of the dataflow architecture, allow parallel computations in the output machine to be implemented without additional effort on the part of the programmer. All this makes it possible to reduce the time for comparing the sequences stored in the knowledge base by several times as compared to traditional inference mechanisms that implement various versions of the principle of resolutions. Formulas and features of the technique of numerical estimation of the user's answers are given. In general, the development of the human-computer dialogue capabilities in test systems- through the development of a specialized module for processing knowledge, will increase the intelligence of such systems and allow us to directly consider the semantics of sentences, more accurately determine the relevance of the user's response to standard knowledge and, ultimately, get rid of the skeptical attitude of many managers to machine testing systems.
2017-12-12
Santos, E. E., Santos, E., Korah, J., Thompson, J. E., Murugappan, V., Subramanian, S., Zhao, Yan.  2017.  Modeling insider threat types in cyber organizations. 2017 IEEE International Symposium on Technologies for Homeland Security (HST). :1–7.

Insider threats can cause immense damage to organizations of different types, including government, corporate, and non-profit organizations. Being an insider, however, does not necessarily equate to being a threat. Effectively identifying valid threats, and assessing the type of threat an insider presents, remain difficult challenges. In this work, we propose a novel breakdown of eight insider threat types, identified by using three insider traits: predictability, susceptibility, and awareness. In addition to presenting this framework for insider threat types, we implement a computational model to demonstrate the viability of our framework with synthetic scenarios devised after reviewing real world insider threat case studies. The results yield useful insights into how further investigation might proceed to reveal how best to gauge predictability, susceptibility, and awareness, and precisely how they relate to the eight insider types.

2017-03-29
Nisha, Dave, M..  2016.  Storage as a parameter for classifying dynamic key management schemes proposed for WSNs. 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT). :51–56.

Real world applications of Wireless Sensor Networks such as border control, healthcare monitoring and target tracking require secure communications. Thus, during WSN setup, one of the first requirements is to distribute the keys to the sensor nodes which can be later used for securing the messages exchanged between sensors. The key management schemes in WSN secure the communication between a pair or a group of nodes. However, the storage capacity of the sensor nodes is limited which makes storage requirement as an important parameter for the evaluation of key management schemes. This paper classifies the existing key management schemes proposed for WSNs into three categories: storage inefficient, storage efficient and highly storage efficient key management schemes.