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Each piece of evidence contributes information at its own source-specific level of abstraction. In other words, the evidence may support a set of situations without committing to any single one.

Uncertainties pervade rules that relate observed evidence and situations.

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FIGURE 7.2 Belief network components and message passing.

and thus for the domain as a whole. When evidence regarding a variable is obtained, it is applied to the corresponding node, which then updates its own total belief distribution, computed as the product of the prior distribution and the new evidence. The node then sends belief revision messages to its parent and children nodes. For example, Figure 7.2b shows the posting of evidence to X and the four resulting belief revision messages. Nodes A, B, C, and D then update their respective belief distributions. If they were connected to further nodes, they would also send out belief revision messages, thus continuing the cycle. The process eventually terminates because no node sends a message back out over the link through which it received a message.

The major feature of the belief network formalism is that it renders probabilistic inference computationally tractable. In addition, it allows causal reasoning from causes to effects and diagnostic reasoning from effects to causes to be freely mixed during inference. Belief network models can be either manually constructed on the basis of knowledge extracted from domain experts or, if sufficient data exist, learned automatically (i.e., built) by sampling the data. Within the artificial intelligence community, belief networks are fast becoming the primary method of reasoning under uncertainty (Binder et al., 1995).

Note that the evidence propagation process involves both top-down (predictive) and bottom-up (diagnostic) inferencing. At each stage, the evidence observed thus far designates a set of likely nodes. Each such node is then used as a source of prediction about additional nodes, directing the information extraction process in a top-down manner. This process makes the underlying inference scheme transparent, as explanations can be traced mechanically along the activated pathways. Pearl's (1986) algorithm is used to compute the belief values given the evidence.

To illustrate how a belief network model could be used to model the situation assessment of an S-2 staff, Figure 7.3 shows a hypothetical battle scenario in which an enemy motorized rifle division will invade its neighbor to the west. The

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FIGURE 7.3 Hypothetical battle scenario. See text for discussion.

X's and dashed lines represent the friendly S-2's a priori, best hypotheses about possible enemy objectives and paths to those objectives, based on initial intelligence preparation of the battlefield, knowledge of enemy fighting doctrine, and knowledge of more global contexts for this local engagement—for example, that the enemy has already attacked on other fronts, or that high-value strategic assets (e.g., an oil refinery) exist just west of the blue positions. Although not depicted in the figure, the friendly force will, of course, have deployed many and varied sensor assets, such as radio emission monitoring sites, deeply deployed ground-based surveillance units, scout helicopters, and theater-wide surveillance assets (e.g., JSTARS). As the enemy begins to move, these sensors will produce a stream of intelligence reports concerning enemy actions. These reports will be processed by the S-2 staff, who will incrementally adjust degrees of belief in the various hypotheses about enemy positions, dispositions, and so on represented in the system.

April 10, 2017
Proofpoint Staff

[]

Overview

This weekend saw multiple reports of a new zero-day vulnerability that affected all versions of Microsoft Word. Today, Proofpoint researchers observed the document exploit being used in a large email campaign distributing the Dridex banking Trojan. This campaign was sent to millions of recipients across numerous organizations primarily in Australia.

This represents a significant level of agility and innovation for Dridex actors who have primarily relied on macro-laden documents attached to emails. While a focus on exploiting the human factor - that is, the tendency of people to click and inadvertently install malware on their devices in socially engineered attacks - remains a key trend in the current threat landscape, attackers are opportunists, making use of available tools to distribute malware efficiently and effectively. This is the first campaign we have observed that leverages the newly disclosed Microsoft zero-day.

Analysis

Emails in this campaign used an attached Microsoft Word RTF (Rich Text Format) document. Messages purported to be from "<[device]@[recipient's domain]>". [device] may be "copier", "documents", "noreply", "no-reply", or "scanner". The subject line in all cases read "Scan Data" and included attachments named "Scan_123456.doc" or "Scan_123456.pdf", where "123456" was replaced with random digits. Note that while this campaign does not rely on sophisticated social engineering, the spoofed email domains and common practice of emailing digitized versions of documents make the lures fairly convincing.

A sample email is shown in Figure 1 below.

When recipients open the document, the exploit -- if successful -- is used to carry out a series of actions that lead to the installation of Dridex botnet ID 7500 on the user’s system. During our testing (for example on Office 2010) the vulnerable system was fully exploited despite the fact that users were presented a dialog about the document containing “links that may refer to other files” (user interaction was not required). The dialog is shown in Figure 2:

Many combinations of Microsoft Word and Windows support " Wrap Around Embroided Denim Mini Skirt Love Harmony Sale Ebay Quality Free Shipping Free Shipping Pay With Paypal Cheap For Nice Free Shipping With Credit Card WDFCwqr7Ez
" for documents downloaded from the internet or opened directly from the email. In these cases, the user needs to “Enable Editing” before the exploit runs. However, most users are accustomed to enabling editing.

Conclusion

Although document exploits are being used less frequently in the wild, with threat actors favoring social engineering, macros, and other elements that exploit "the human factor," this campaign is a good reminder that actors will shift tactics as necessary to capitalize on new opportunities to increase the effectiveness of their efforts.

Microsoft patched this exploit ( CVE-2017-0199 ) on April 11, 2017. Because of the widespread effectiveness and rapid weaponization of this exploit, it is critical that users and organizations apply the patch as soon as possible.

Indicators of Compromise (IOCs)

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