compromise network configuration spread viruses and spyware

... TRẢ LỜI BÀI TẬP ORACLE CHƯƠNG 26 ORACLE VÀ CẤU HÌNH MẠNG (ORACLE NETWORK CONFIGURATION) 1. Listener là gì? Cáùc Service chính nào được dùng để phục cho việc truy xuất...

... giữa sáng kiến và hiệu quả của nó SAN solution Hợp nhất việc lưu trữ • SAN (Storage Area Network) và NAS (Network Attached Storage) là hai cách để tổ chức lại một hệ thống vào trong một nguồn ... chủ thông qua một card adapter, và thiết bị lưu trữ chỉ phục vụ cho một server duy nhất • NAS (network attached storage): là một dạng mạng LAN cơ bản chạy trên máy chủ file server sử dụng giao ... thiết bị mà không phá vỡ tiến trình của ứng dụng Các công nghệ mới • Giao thức iCSI • FCoE • Infiniband • Ảo hóa • Phần mềm quản lý nguồn lưu trữ • Sự hội tụ của SAN/NAS Lợi ích của SAN • Cải thiện...

... to data that your modem sends and receives to get email addresses from your incoming and outgoing email, Web pages, and files in your Internet cache folder. Many viruses also include their own ... might have. Spyware is also known as adware, and although it usually isn't destructive, it downloads files to your drive and is often responsible for crashes. The first and possibly ... need to be careful, especially with email attachments. You know what viruses are, but what are trojan horses and spyware? Trojan horses are small programs that permit hackers to take control...

... memory and infect at later stages • Some viruses have trigger events to activate and corrupt systems • Some viruses have bugs that replicate and perform activities like file deletion and increasing ... Computer Viruses Viruses V irus writers can have various reasons for creatin g and g spreading malware • Research projects Viruses have been written as: • Research projects •Pranks •Vandalism • ... are not infected Ethical Hacking and Countermeasures Version 6 Mod le IX Mod u le IX Viruses and Worms Characteristics of a Virus Virus resides in the memory and replicates itself while the program...

... of changes. changes. Optimization Intra- and Interdomain Routing Distance Vector Routing and RIP Link State Routing and OSPF Path Vector Routing and BGP Topics discussed in this section: Topics ... corresponding network address. 2. The second mask (/25) is applied to the destination address. The result is 180.70.65.128, which matches the corresponding network address. The next-hop address and ... router R1, using the configuration in Figure 22.6. Example 22.1 Solution Table 22.1 shows the corresponding table. 22.20 The second local ISP has divided its block into 4 blocks and has assigned...

... Presence or absence of viral envelope Viruses infecting vertebrates are divided into 14 RNA families and 7 DNA families (Ending: -viridae) Mumps, Measles, Influenza, and Poliomyelitis Herpesvirus • Classification: ... Range • Mostly species – and even cell type – specific • Exception: Zoonotic viruses are transmissible from animals (arthropods, vertebrates) to man (zoonosis) – Arboviruses (West Nile virus), ... cancer, metastasizes • Proto-oncogenes and oncogenes are regulatory genes • Properties of normal and transformed cells • Only about 15% of human tumors are due to viruses • Examples of human tumors:...

... Several new antivirals and vaccine candidates are being investigated. CORONAVIRUSES NL63 AND HKU1 In addition to coronaviruses 229E and OC43, the new coronaviruses NL63 and HKU1 were identified ... viruses and their subgroups have been discovered: influenza A viruses H5N1 and H1N1, human metapneumovirus, coronaviruses SARS, NL63 and HKU1, human bocavirus, human rhinoviruses C and D and potential ... respiratory viruses or viral strains include influenza A virus H5N1 and H1N1, MPV, SARS-, NL63- and HKU1-CoV, HBoV, HRV-C and –D and the possible respiratory pathogens, KI- and, WU-PyV and TTV...

... USE AND MAINTENANCE February 2003 ANALYSIS WITHOUT COMPROMISE Technology Evaluation and Comparison Report www.butlergroup.com Network Security The Benefits and Pitfalls of Contemporary Network ... 4,5,6; Macintosh OS8/9 and OS X; Solaris SPARC and Intel; FreeBSD/Intel; HP-UK and HP-PA; Compaq Tru64; IBM AIX; SCO OpenServer and Unixware; OpenVMS/VAX and Alpha; OS2; and DOS. MailMonitor covers: ... checksums and signatures are correct. Sophos Anti-Virus is pre-configured for all known viruses and product updates. As a minimum, the CID needs to be specified, and configuration for a network of...

... fundamentally  different networks,datanetworks and synchronous network comprisedofswitches.Theinternetisconsidereda data network.  Since the current data network consists of ... Non‐repudiation–Ensuretheuserdoesnot refutethatheusedthe network  An effective network security plan is developed withtheunderstandingofsecurityissues,potential attackers,neededlevelofsecurity, and factorsthat makea network vulnerabletoattack[1].Thesteps involved ... the network.  Understanding the security issues of the internet greatly assists in developing new security technologies and approaches for networks with internetaccess and internetsecurityitself.  The...

... OSPF, RIP, and HSRP. The cracker injects bogus networking re -configuration commands that affect routers, switches, and intelligent hubs. A whole network can be brought down in this manner and require ... accessibility to information resources. Network configuration and reconfiguration is easier, faster, and less expensive. However, wireless technology also creates new threats and alters the existing information ... method the network is scanned and mapped for all access points and WLAN nodes. Then this is compared with previous network map. Commonly available network mapping tools like netstumbler and wavelan-tool...

... Knowledge Network 32 A Knowledge-Swapping Community 40 Organizational Knowledge Networking 45 Summary 59 Chapter 3 Strategy and Planning for the Knowledge Network 61 Strategy and Change 62 Planning and ... relation- ship between culture and technology in Chapter 7, “Choosing and Using Tech- nology,” and in some of our guides to knowledge network implementation in Chapter 8, “Initiating and Supporting Internal ... preindustrial world, complex skills and trades were passed on from par- ent to child and from master to apprentice by direct demonstration and hands- on instruction. Schools and universities were attended...

... Regenerate and retransmit data signals. – Maintain information about what pathways exist through the network and internetwork. 15 and internetwork. – Notify other devices of errors and communication ... locations. Voice and data on separate networks or converged networks Using Layered Protocols Intermediary Devices and their Role on the Network  Processes running on the intermediary network devices ... electromagnetic waves. Protocol Suites and Standards 31  Early days – proprietary network equipment and protocols.  Now – Industry standards  Institute of Electrical and Electronics Engineers (IEEE)...

... Since the dynamics of the human arm or the robotic manipulator is nonlinear, the $\ovalbox{\tt\smallREJECT}’$ problem to find the unique trajectory which minimizes $C_{T}$ is a nonlinear optimization problem. The central nervous system does not seem to adopt the iterative algorithm which we proposed in [18]. It was reported that some neural -network models can solve difficult optimization problems such as the traveling $salesma_{-}n$ problem or early visions by minimizing “energy” through the network dynamics. We [11] proposed a neural -network model, which automatically generates the torque which minimizes $C_{T}$ without explicit handling of the cost function. This network can be regarded as one example of autonomous motor pattern generators such as a neural oscillator for rhythmic movements. We $recently^{o}developed$ the $mode1^{r}toa^{r}repetitive^{s}networkfor^{-}1earning^{t}ofthe^{1}vector$ field $\ovalbox{\tt\smallREJECT}$ ’ 32 the synaptic plasticity. Expanding on these previous models and adaptive filter model of the cerebellum [4], we proposed a neural network model for the control of and learning of voluntary movement [9]. In our model, the association cortex sends the desired movement pattern expressed in the body coordinates, to the motor cortex, where the motor command, that is torque to be generated by muscles, is then somehow computed. The actual motor pattern is measured by proprioceptors and sent back to the motor cortex via the transcortical loop. Then, feedback control can be performed utilizing error in the movement trajectory. However, feedback delays and small gains both limit controllable speeds of motions. The cerebrocerebellum-parvocellular part of the red nucleus system receives synaptic inputs from wide areas of the cerebral cortex and does not receive peripheral sensory input. That is, it monitors both the desired trajectory and the motor command but it does not receive information about the actual movement. Within the cerebrocerebellum– parvocellular red nucleus system, an intemal neural model of the inverse-dynamics of the musculoskeletal system is acquired. The inverse-dynamics of the musculoskeletal system is defined as the nonlinear system whose input and output are inverted (trajectory is the input and motor command is the output). Once the inverse-dynamics model is acquired by motor}earning, it can compute a good motor command directly from the desired trajectory. Learning of inverse-dynamics model by feedback motor command as an error signal The simplest learning approach for acquiring the inverse dynamics model of a controlled object is shown in Fig. $4a$ . In Fig. 4 the controlled object is called as a manipulator. As shown in Fig. $4a$ , the manipulator receives the torque input $T(t)$ and outputs the resulting trajectory $\theta(t)$ . The inverse dynamics model is set in the opposite input-output direction to that‘ of the manipulator, as shown by the arrow. That is, it receives the trajectory as an - $l7$ $-$ 3 $\vee$ $\dot{\cdot}$ In this network, nonlinear transformation was made only of cascade of linear weighted summation and sigmoid nonlinearity. That is, we did not use any a priori knowledge about the dynamical structure of the controlled object. The learning went well and the network has some extent of generalization capability. In the learning, we still used the feedback torque command as the error signal. Summary In order to control voluntary movements, the central nervous system must solve the fol- lowing three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of trajectory from visual coordi- nates to body coordinates and (3) generation of motor command. Based on physiological information and previous models, computational theories are proposed for the first two problems, and a hierarchical neural network model is introduced to deal with motor com- mand. Combination of the second and the third approach was found to be very efficient for learning trajectory control of an industrial robotic manipulator [14]. References [1] Allen, G.I. and Tsukahara, N.(1974). Physiol. Rev. 54, 957-1006. [2] Atkeson, C.G. and Hollerbach, J.M.(1985). J Neurosci. 5,2318-2330. [3] Flash, T. and Hogan, N.(1985). J. Neurosci. 5, 1688-1703. [4] Fujita, M.(1982). Biol. Cybern. 45, 195-206. [5] Ito, M.(1970). Intern. J. Neurol. 7, 162-176. [6] Jordan, M.I. and Rosenbaum, D.A.(1988). COINS Technical Report $8\delta- ... problems in Fig. 2. M. Jordan explained this reason in the many to one inverse kinematics problem associated with motor control of redundant manipulators with excess degrees of freedom $[6,7]$ . Fig. $4b$ shows the alternative computational approach which we proposed and called as feedback error learning. This block diagram includes the motor cortex (feedback gain $K$ and summation of feedback and feedforward commands), the transcortical loop (neg- ative feedback loop) and the cerebrocerebellum-parvocellular red nucleus system (inverse dynamics model). The total torque $T(t)$ fed to an actuator of the manipulator is a sum of the feedback torque $T_{f}(t)$ and the feedforward torque $T_{1}(t)$ , which is calculated by the inverse-dynamics model. The inverse-dynamics model receives the desired trajectory $\theta_{d}$ represented in the body coordinates such as joint angles or muscle lengths, and monitors the feedback torque $T_{f}(t)$ as the error signal. The feedback error learning scheme has several advantages over other motor learning 34 schemes including direct inverse modeling. First, the teaching signal or the desired output for the neural network controller is not required. Instead, the feedback torque is used as the error signal. Second, the control and learning are done simultaneously. Third, back- propagation of the error signal through the controlled object or through a forward model of the controlled object [6] is not necessary. Fourth, the learning is goal directed. Finally, it can resolve the ill-posedness in the second and the third problems in Fig. 2 because of good characteristics inherent in the feedback controller. It is expected that the feedback signal tends to zero as leaming proceeds. We call this learning scheme as feedback error learn $ing$ emphasizing the importance of using the feedback torque (motor command) as the error signal of the heterosynaptic learning. There are two possibilities about how the central nervous system computes nonlinear transformations required for making an inverse dynamics model of a nonlinear controlled object. One is that they are computed by nonlinear ... k$ Trajectories derived from the minimum torque-change model are quite different from $\backslashi$ those of the minimum jerk model under the following behavioral situations. (i) Big hor- to determine the unique trajectory which minimizes $C_{T}$ . Uno et al. [18] overcame this difficulty by developing an iterative scheme, so the unique trajectory and the associated motor command (torque) can be determined simultaneously. That is, the three problems of trajectory formation, coordinates transformation and generation of motor command are. solved simultaneously by this algorithm. Mathematically, the iterative learning scheme can be regarded as a Newton-like...