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ABSTRACT:

In this paper, we will focus on the security of advanced metering infrastructure (AMI), which is one of the most crucial components of SG. AMI serves as a bridge for providing bidirectional information flow between user domain and utility domain. AMI’s main functionalities encompass power measurement facilities, assisting adaptive power pricing and demand side management, providing self-healing ability, and interfaces for other systems.

AMI is usually composed of three major types of components, namely, smart meter, data concentrator, and central system (a.k.a. AMI headend) and bidirectional communication networks among those components. AMI is exposed to various security threats such as privacy breach, energy theft, illegal monetary gain, and other malicious activities. As AMI is directly related to revenue earning, customer power consumption, and privacy, of utmost importance is securing its infrastructure. In order to protect AMI from malicious attacks, we look into the intrusion detection system (IDS) aspect of security solution.

We can define IDS as a monitoring system for detecting any unwanted entity into a targeted system (such as AMI in our context). We treat IDS as a second line security measure after the first line of primary AMI security techniques such as encryption, authorization, and authentication, Hence, changing specifications in all key IDS sensors would be expensive and cumbersome. In this paper, we choose to employ anomaly-based IDS using data mining approaches.

INTRODUCTION

Smart grid (SG) is a set of technologies that integrate modern information technologies with present power grid system. Along with many other benefits, two-way communication, updating users about their consuming behavior, controlling home appliances and other smart components remotely, and monitoring power grid’s stability are unique features of SG. To facilitate such kinds of novel features, SG needs to incorporate many new devices and services. For communicating, monitoring, and controlling of these devices/services, there may also be a need for many new protocols and standards. However, the combination of all these new devices, services, protocols, and standards make SG a very complex system that is vulnerable to increased security threats—like any other complex systems are. In particular, because of its bidirectional, interoperable, and software-oriented nature, SG is very prone to cyber attacks. If proper security measures are not taken, a cyber attack on SG can potentially bring about a huge catastrophic impact on the whole grid and, thus, to the society. Thus, cyber security in SG is treated as one of the vital issues by the National Institute of Standards and Technology and the Federal Energy Regulatory Commission.

In this paper, we will focus on the security of advanced metering infrastructure (AMI), which is one of the most crucial components of SG. AMI serves as a bridge for providing bidirectional information flow between user domain and utility domain [2]. AMI’s main functionalities encompass power measurement facilities, assisting adaptive power pricing and demand side management, providing self-healing ability, and interfaces for other systems. AMI is usually composed of three major types of components, namely, smart meter, data concentrator, and central system (a.k.a. AMI headend) and bidirectional communication networks among those components. Being a complex system in itself, AMI is exposed to various security threats such as privacy breach, energy theft, illegal monetary gain, and other malicious activities. As AMI is directly related to revenue earning, customer power consumption, and privacy, of utmost importance is securing its infrastructure.

LITRATURE SURVEY

EFFICIENT AUTHENTICATION SCHEME FOR DATA AGGREGATION IN SMART GRID WITH FAULT TOLERANCE AND FAULT DIAGNOSIS

PUBLISH: IEEE Power Energy Soc. Conf. ISGT, 2012, pp. 1–8.

AUTOHR: D. Li, Z. Aung, J. R. Williams, and A. Sanchez

EXPLANATION:

Authentication schemes relying on per-packet signature and per-signature verification introduce heavy cost for computation and communication. Due to its constraint resources, smart grid’s authentication requirement cannot be satisfied by this scheme. Most importantly, it is a must to underscore smart grid’s demand for high availability. In this paper, we present an efficient and robust approach to authenticate data aggregation in smart grid via deploying signature aggregation, batch verification and signature amortization schemes to less communication overhead, reduce numbers of signing and verification operations, and provide fault tolerance. Corresponding fault diagnosis algorithms are contributed to pinpoint forged or error signatures. Both experimental result and performance evaluation demonstrate our computational and communication gains.

CYBER SECURITY ISSUES FOR ADVANCED METERING INFRASTRUCTURE (AMI)

PUBLISH: IEEE Power Energy Soc. Gen. Meet. – Convers. Del. Electr. Energy 21st Century, 2008, pp. 1–5.

AUTOHR: F. M. Cleveland

EXPLANATION:

Advanced Metering Infrastructure (AMI) is becoming of increasing interest to many stakeholders, including utilities, regulators, energy markets, and a society concerned about conserving energy and responding to global warming. AMI technologies, rapidly overtaking the earlier Automated Meter Reading (AMR) technologies, are being developed by many vendors, with portions being developed by metering manufacturers, communications providers, and back-office Meter Data Management (MDM) IT vendors. In this flurry of excitement, very little effort has yet been focused on the cyber security of AMI systems. The comment usually is “Oh yes, we will encrypt everything – that will make everything secure.” That comment indicates unawareness of possible security threats of AMI – a technology that will reach into a large majority of residences and virtually all commercial and industrial customers. What if, for instance, remote connect/disconnect were included as one AMI capability – a function of great interest to many utilities as it avoids truck rolls. What if a smart kid hacker in his basement cracked the security of his AMI system, and sent out 5 million disconnect commands to all customer meters on the AMI system.

INTRUSION DETECTION FOR ADVANCED METERING INFRASTRUCTURES: REQUIREMENTS AND ARCHITECTURAL DIRECTIONS

PUBLISH: IEEE Int. Conf. SmartGridComm, 2010, pp. 350–355.

AUTOHR: R. Berthier, W. H. Sanders, and H. Khurana,

EXPLANATION:

The security of Advanced Metering Infrastructures (AMIs) is of critical importance. The use of secure protocols and the enforcement of strong security properties have the potential to prevent vulnerabilities from being exploited and from having costly consequences. However, as learned from experiences in IT security, prevention is one aspect of a comprehensive approach that must also include the development of a complete monitoring solution. In this paper, we explore the practical needs for monitoring and intrusion detection through a thorough analysis of the different threats targeting an AMI. In order to protect AMI from malicious attacks, we look into the intrusion detection system (IDS) aspect of security solution. We can define IDS as a monitoring system for detecting any unwanted entity into a targeted system (such as AMI in our context). We treat IDS as a second line security measure after the first line of primary AMI security techniques such as encryption, authorization, and authentication, such as [3]. However, Cleveland [4] stressed that these first line security solutions alone are not sufficient for securing AMI.

MOA: MASSIVE ONLINE ANALYSIS, A FRAMEWORK FOR STREAM CLASSIFICATION AND CLUSTERING

PUBLISH: JMLR Workshop Conf. Proc., Workshop Appl. Pattern Anal., 2010, vol. 11, pp. 44–50.

AUTOHR: A. Bifet, G. Holmes, B. Pfahringer, P. Kranen, H. Kremer, T. Jansen, and T. Seidl

EXPLANATION:

In today’s applications, massive, evolving data streams are ubiquitous. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for both classification and clustering as well as tools for evaluation. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily compare several algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows the creation of benchmark scenarios through storing and sharing setting files.

SECURING ADVANCED METERING INFRASTRUCTURE USING INTRUSION DETECTION SYSTEM WITH DATA STREAM MINING

PUBLISH: Proc. PAISI, 2012, vol. 7299, pp. 96–111

AUTOHR: M. A. Faisal, Z. Aung, J. Williams, and A. Sanchez

EXPLANATION:

Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is responsible for collecting, measuring, analyzing energy usage data, and transmitting these data to the data concentrator and then to a central system in the utility side. Therefore, the security of AMI is one of the most demanding issues in the smart grid implementation. In this paper, we propose an intrusion detection system (IDS) architecture for AMI which will act as a complimentary with other security measures. This IDS architecture consists of three local IDSs placed in smart meters, data concentrators, and central system (AMI headend). For detecting anomaly, we use data stream mining approach on the public KDD CUP 1999 data set for analysis the requirement of the three components in AMI. From our result and analysis, it shows stream data mining technique shows promising potential for solving security issues in AMI.

DATA STREAM MINING ARCHITECTURE FOR NETWORK INTRUSION DETECTION

PUBLISH: IEEE Int. Conf. IRI, 2004, pp. 363–368

AUTOHR: N. C. N. Chu, A. Williams, R. Alhajj, and K. Barker

EXPLANATION:

In this paper, we propose a stream mining architecture which is based on a single-pass approach. Our approach can be used to develop efficient, effective, and active intrusion detection mechanisms which satisfy the near real-time requirements of processing data streams on a network with minimal overhead. The key idea is that new patterns can now be detected on-the-fly. They are flagged as network attacks or labeled as normal traffic, based on the current network trend, thus reducing the false alarm rates prevalent in active network intrusion systems and increasing the low detection rate which characterizes passive approaches.

RESEARCH ON DATA MINING TECHNOLOGIES APPLYING INTRUSION DETECTION

PUBLISH: Proc. IEEE ICEMMS, 2010, pp. 230–233

AUTOHR: Z. Qun and H. Wen-Jie

EXPLANATION:

Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm; for anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules . Finally, has analysed the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.

AN EMBEDDED INTRUSION DETECTION SYSTEM MODEL FOR APPLICATION PROGRAM

PUBLISH: IEEE PACIIA, 2008, vol. 2, pp. 910–912.

AUTOHR: S. Wu and Y. Chen

EXPLANATION:

Intrusion detection is an effective security mechanism developed in the recent decade. Because of its wide applicability, intrusion detection becomes the key part of the security mechanism. The modern technologies and models in intrusion detection field are categorized and studied. The characters of current practical IDS are introduced. The theories and realization of IDS based on applications are presented. The basic ideas concerned with how to design and realize the embedded IDS for application are proposed.

ACCURACY UPDATED ENSEMBLE FOR DATA STREAMS WITH CONCEPT DRIFT

PUBLISH: Proc. 6th Int. Conf. HAIS Part II, 2011, pp. 155–163.

AUTOHR: D. Brzeziñski and J. Stefanowski

EXPLANATION:

In this paper we study the problem of constructing accurate block-based ensemble classifiers from time evolving data streams. AWE is the best-known representative of these ensembles. We propose a new algorithm called Accuracy Updated Ensemble (AUE), which extends AWE by using online component classifiers and updating them according to the current distribution. Additional modifications of weighting functions solve problems with undesired classifier excluding seen in AWE. Experiments with several evolving data sets show that, while still requiring constant processing time and memory, AUE is more accurate than AWE.

ACTIVE LEARNING WITH EVOLVING STREAMING DATA

PUBLISH: Proc. ECML-PKDD Part III, 2011, pp. 597–612.

AUTOHR: I. liobaitë, A. Bifet, B. Pfahringer, and G. Holmes

EXPLANATION:

In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. In this paper we develop two active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.

LEARNING FROM TIME-CHANGING DATA WITH ADAPTIVE WINDOWING

PUBLISH: Proc. SIAM Int. Conf. SDM, 2007, pp. 443–448.

AUTOHR: A. Bifet and R. Gavaldà,

EXPLANATION:

We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. Using ideas from data stream algorithmics, we develop a time- and memory-efficient version of this algorithm, called ADWIN2. We show how to combine ADWIN2 with the Naïve Bayes (NB) predictor, in two ways: one, using it to monitor the error rate of the current model and declare when revision is necessary and, two, putting it inside the NB predictor to maintain up-to-date estimations of conditional probabilities in the data. We test our approach using synthetic and real data streams and compare them to both fixed-size and variable-size window strategies with good results.