Network intrusion

Various cyber protection devices are currently available for use in protecting utility computer systems. These protection devices include anti-virus and pest eradication software, firewalls, and network intrusion hardware/software. These products are discussed in this section. 

Network intrusion detection and prevention systems are software- and hardware-based programs designed to detect unauthorized attacks on a computer network system. 

While other applications, such as firewalls and anti-virus software, share similar objectives with network intrusion systems, network intrusion systems provide a deeper layer of protection beyond the capabilities of these other systems because they evaluate patterns of computer activity rather than specific files. 

It is worth noting that attacks may come from either outside or within the system (i.e., from an insider), and that network intrusion detection systems may be more applicable for detecting patterns of suspicious activity from inside a facility (i.e., accessing sensitive data, etc.) than are other information technology solutions. 

Network intrusion detection systems employ a variety of mechanisms to evaluate potential threats. The types of search and detection mechanisms are dependent upon the level of sophistication of the system. Some of the available detection methods include the following ... 

Protocol analysis. Protocol analysis is the process of capturing, decoding, and interpreting electronic traffic. The protocol analysis method of network intrusion detection involves the analysis of data captured during transactions between two or more systems or devices, and the evaluation of these data to identify unusual activity and potential problems. Once a problem is isolated and recorded, problems or potential threats can be linked to pieces of hardware or software. Sophisticated protocol analysis will also provide statistics and trend information on the captured traffic. 

Data enrichment is presented at the lower left comer of Figure 1. Data enrichment occurs when heterogeneous contextual information is entered in the event database. The same object can be represented by heterogeneous information depending on the data source. For example, the Windows NT event log can give us a host name, the network intrusion detection system a host IP address, and the wireless access point a MAC address, this for the same machine. The data enrichment functions attempt to complete and reconcile heterogeneous information entered in the database. 

Keywords neural networks intrusion detection systems PCA network attacks attack recognition and identification... 

To assess the effectiveness of the proposed intrusion detection approach, the experiments were conducted on the KDD Cup network intrusion detection data set [14]. We have used training data sets for anomaly detection made up of 400-700 randomly selected normal samples for each service. Training data sets for identification of attack made up of normal samples and attacks (Table 4) for each service. 

Successful network intrusion from external environment 19.0%... 

Successful network intrusion from within the supply network or organization 44.9%... 

Keywords Wireless Sensor Network, Intrusion Detection System, flooding attack. 

In a pore system composed of isolated pores of ink-bottle shape, the intrusion curve leads to the size distribution of the necks and the extrusion curve to the size distribution of the bodies of the pores. In the majority of solids, however, the pores are present as a network, and the interpretation of the mercury porosimetry results is complicated by pore blocking effects. 

Intruder alarms are designed to give a warning of the presence of an intruder within or attempting to enter the protected area. Alarm systems may act as a deterrent to the casual or opportunist thief but they will do little or nothing to prevent a determined intrusion, and to be effective they must provoke an early response from the appropriate authority (in most cases the police). The warning may be a local audible device, but normally the alarm signal is transmitted by the telephone network to a central station operated by a security company on a 24-hour basis. 

The pore geometry described in the above section plays a dominant role in the fluid transport through the media. For example, Katz and Thompson [64] reported a strong correlation between permeability and the size of the pore throat determined from Hg intrusion experiments. This is often understood in terms of a capillary model for porous media in which the main contribution to the single phase flow is the smallest restriction in the pore network, i.e., the pore throat. On the other hand, understanding multiphase flow in porous media requires a more complete picture of the pore network, including pore body and pore throat. For example, in a capillary model, complete displacement of both phases can be achieved. However, in real porous media, one finds that displacement of one or both phases can be hindered, giving rise to the concept of residue saturation. In the production of crude oil, this often dictates the fraction of oil that will not flow. 

Keywords wireless sensor network detection theory Kalman filtering target intrusion detection false alarm. 

To be able to effectively respond to cyber attacks, establish an intrusion detection strategy that includes alerting network administrators of malicious network activity originating from internal or external sources. Intrusion detection system monitoring is essential twenty-four hours a day this capability can be easily set up through a pager. Additionally, incident response procedures must be in place to allow an... 

The differences of the intrusion and extrusion mechanisms are the main factors, leading to the different pathways (hysteresis) of the branches in Fig. 1.16A. Furthermore, this effect causes the pore size distribution obtained from the intrusion curve to be incorrectly shifted towards smaller pore sizes. Unlike some inorganic materials of very regular pore structure (e.g. zeolites), permanently porous organic polymers consist of a very complex network of pores of different sizes connected to each other. Correction of these falsifications in the results described above is virtually impossible, since it implies a detailed understanding of the network. 

Instead of the actual network of irregular channels, the interpretation of this experiment is based on a model that imagines the plug to consist of a bundle of cylindrical pores of radius Rc. The model is represented by Figure 6.16c. The intrusion of the liquid into the cylindrical pores in response to the applied pressure follows the same mathematical description as the rise of a liquid in a capillary. In view of the approximate nature of the model, it is adequate to use the Laplace equation in the form given by Equation (3) to describe this situation ... 

Figure 2 shows the cumulative pore volume vs. pore radius for AC-ref SC-100 and SC-155 obtained by mercury intrusion technique. The curve corresponding to AC-ref shows a wide pore radius distribution instead, the curves assigned to SC-100 and SC-155 showed sharpened zones with maximum slope in 459A and 524A respectively, denoting a small increase of these values with the increase of the synthesis temperature. This phenomenon is probably produced by the growing of the big pores of the silica network at the expense of the... 

One way to detect computer attacks is via an intrusion detection system (IDS). Such systems are characterized by two basic functions including the automatic detection of attacks, and the reporting of information about attacks to the administrator. An IDS works in real-time and monitors network traffic comparing it with templates from a database. If some template coincides with a template from a database, it means that the system has been attacked. 

Software can not be used for the detection of network attack and correspondent reaction. This function must be fulfilled by the system of intrusion (attacks) detection. Meantime the IDS has to push the software... 

The proposed method is universal to different attack types, and it enables to detect the status of remote attacking computer in the real time. The software module allows to promote the efficiency of corporate networks defense systems functioning, using its aggregation with the program complexes of the Intrusion Detection System. 

Roesch, M., Snort—Lightweight Intrusion Detection for Networks, in Proceedings of LISA 99, Seattle, Washington, USA, 1999. 

SOME ASPECTS OF NEURAL NETWORK APPROACH FOR INTRUSION DETECTION... 

Intrusion detection systems (IDS) are used as a computer network security tool and permit to alert an administrator in case of attack. The main goal of IDS is to detect and recognize network attacks in real time. Nowadays there exist different approaches for intrusion detection. It is signature analysis, rule-based method, embedded sensors, neural networks, artificial immune systems [1]—[6] and so on. The most of these IDS can detect the known attacks and have poor ability to detect new attacks. 

In last years a neural network techniques have been applied and investigated for intrusion detection [7]-[10], Such approaches are based on different strategies. So, one of them for anomaly detection use analysis of the audit records, produced by the operating system [8]. The other one is based on network protocol analysis [9],... 

Among the most wide-spread neural networks are feedforward networks, namely multilayer perceptron (MLP). This network type has been proven to be universal function approximators [11], Another important feature of MLP is the ability to generalization. Therefore MLP can be powerful tool for design of intrusion detection systems. 

This paper presents applying of neural networks for intrusion detection through an examination of network traffic data. It has been shown that denial of service and other network-based attacks are presented in the network traffic data. Therefore using neural networks permits to extract nonlinear relationships between variables from network traffic and to design real-time intrusion detection systems. 

We describe the intrusion detection system, which consists of two different neural networks. The first neural network is nonlinear PCA (principal component analysis) network, which permits to identify normal or anomalous system behavior. The second one is multilayer perceptron (MLP), which can recognize type of attack. 

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