here is high expectation recently for machine-tomachine (M2M) communications over wired and wireless links. Various applications of M2M have already started to emerge in various sectors such as healthcare, vehicular ad hoc networks, smart home technologies, and so on [1] . The evolution of M2M has also begun in developing a smart power grid framework, referred to as the smart grid (SG) [2, 3] . An electric grid having smart or intelligent capability allows power providers, distributors, and consumers -operating requirements and capabilities. Through this awareness, smart grid is able to produce, distribute, and consume power in the most effective manner. This type of communication takes place only among machines such as sensors, smart meters, and other equipment. Therefore, the M2M communications in SG must be private and secure since many of the autonomic functions that will run over it will be critical. Smart grid is usually portrayed as having numerous electrical appliances connected to one another in a complex manner so that they can report back on information such as power consumption and other monitoring signals. This promises higher efficiency in the power distribution networks (i.e., greater availability of power to homes and factories at lower cost), and will allow distributed power generation such as local solar and wind generators. It will reach into home-based devices; therefore, in addition to scalability and fast communication, serious attention should be paid to SG security [4] . Since SG communication is going to be based on current networking technologies, the same security concerns often encountered in conventional networks will also be prevalent in SG. In fact, cyber threats such as distributed denial of service (DDoS) attacks are likely to have more impact on SG communication because of the involvement of so much electrical equipment on the consumer side. By means of early forecasting of malicious threats to SG, it may be possible to take quick measures to protect the appliances from being compromised by the attacker. To establish such a forecasting framework, however, we need to consider the fact that humans are not supposed to interfere with M2M communication. Instead, the machines within the SG should have an adequate framework to predict or warn about malicious events, abnormalities, and failures a priori. The machines in the SG can be found in different types of networks, such as home, building, and neighborhood-wide networks. The smart meters deployed in these different networks may be utilized to form an information sharing network. Such information sharing can provide an important resource for developing global and timely assessments of emerging malicious threats against SG communication. While smart meters should authenticate other smart meters and devices, the authentication scheme itself can be targeted by DDoS attackers. In this article, we consider the spread of a worm in an SG that compromises a number of machines (i.e., smart meters, electrical appliances), which start sending malicious authentication requests to victim smart meters. Such cases observed in an SG network are notified to its hierarchically above network, which in turn conveys the information to  Smart grid presents the largest growth potential in the machine-to-machine market today. Spurred by the recent advances in M2M technologies, the smart meters/sensors used in smart grid are expected not to require human intervention in characterizing power requirements and energy distribution. These numerous sensors are able to report back information such as power consumption and other monitoring signals. However, SG, as it comprises an energy control and distribution system, requires fast response to malicious events such as distributed denial of service attacks against smart meters. In this article, we model the malicious and/or abnormal events, which may compromise the security and privacy of smart grid users, as a Gaussian process. Based on this model, a novel early warning system is proposed for anticipating malicious events in the SG network. With the warning system, the SG control center can forecast such malicious events, thereby enabling SG to react beforehand and mitigate the possible impact of malicious activity. We verify the effectiveness of the proposed early warning system through computer-based simulations.
For example, Fadlullah et al. REF studied the detection of malicious activities in smart grid communication and proposed an early warning system.
An early warning system against malicious activities for smart grid communications
IEEE Network
IEEE Network
Computer Science