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Physical Layer: digital transmission, multiplexing, transmission media. Datagram networks. Data link layer: Introduction, Block coding, cyclic codes, chccksum, framing, flow and error control, Noiseless channels, noisy channels, HDLC, point to point protocols.
Medium Access sub layer: Random access, controlled access, channelization. IEEE standards. QoS in switched networks. In R13 ,8-units of R09 syllabus are combined into 5-units in r13 syllabus. A1: A computer network is a group of computers linked to each other that enables the computer to communicate with another computer and share their resources, data, and applications.
It is used to connect two or more personal computers through a communication medium such as coaxial cable, twisted pair, etc. LAN is not very costly as it is built with inexpensive hardware such as network adapters, hubs and ethernet cables. That gap in time is known as high risk period and majority of failures and damages happens in that time frame. Anomaly detection system use activity measures and they should decide is some activity probable intrusion.
This includes setting up profiles of behavior: normal profile and abnormal profile. Many misuse and anomaly intrusion detection systems IDSs are based on the general model proposed by Denning This model is independent of the platform, system vulnerability, and type of intrusion.
It maintains a set of historical profiles for users, matches an audit record with the appropriate profile, updates the profile whenever necessary, and reports any anomalies detected. Another component, a rule set, is used for detecting misuse. An Intrusion Detection System IDS inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system.
Host-based attacks usually gain access to privileged services or resources on a machine. Network-based attacks usually make it difficult for legitimate users to access various network services 3. Also they watch system state. They report activity to intrusion detection system. They could be placed in routers or other network components. That means that they are stealthy and operating-system independent. They are usually based on policy. A drawback is that it requires application involvement some applications will not report.
Benefits are that application understands the objects and entities to which policies apply. Usually they can leverage the thousands of vulnerability- and exploit- based signatures in its threat libraries as a basis for network control and threat defense.
Usually, intrusions those are possible to happen in wired networks can be used in wireless networks. In addition to those, there are numerous possible intrusions that are wireless specific.
In wired networks there is network security perimeter. In other hand, in wireless network intruder can stay anywhere in area covered by signal and intrude unseen. This causes losing exact classification to insider and outsider attacks what is one of important concepts in classic IDSs. Sometimes people assume that: host based systems prevent insider attacks and network based systems prevent outsider tasks. We may not agree with this in practice, but it is case that, as soon as you add a Wi-Fi signal, the border of defense becomes unclear and not sharply defined.
We need WIDS Wireless Intrusion Detection System or at least necessary extensions to current system in order to cover wireless specific threats, attacks and vulnerabilities. Who will benefit from increased wireless security? Using neural network and fuzzy logic in intrusion detection systems has been considered as holly grail for long time. With current development in both areas we are coming to possibility to consider this unity again and to create feasible system that combines these technologies in order to give more powerful solution for wireless networks protection.
It protects computer or computerized. It has embedded logic for detecting intrusions and alerting stations and servers about it. Position of application is area of wireless computer network. It can communicate with CERT centers and similar. Position of application is in corporate network or remote for more mutually linked networks. They are installed on Server, but could collect and show data from various components of WIDS system. Single utilities could reside on Agent and Sensor devices and hey provide remote access and configuration capability too.
By this practice, possibility that some could fall between gaps is significantly decreased. Also, in previous sections some other aspects have been mentioned and described, such as: self decision and cooperative decision with other agents, relation to Sensor, Server and Console etc… This system requires mathematical approach which defines axiom: regular access vs.
Also define conclusion mechanism i. Goal of this approach is to make an efficient system to defend the wireless network. System should have self-learning system and anticipation prediction. Even, if we fail to make a fully intelligent system we can accept some weaker decision points to get the system functional. It is possible to implement attack recognition and launch response to defend system or network. Neural networks and fuzzy logic are concepts that become available as technology tools with required capabilities including self learning system AI - artificial intelligence, neural networks, fuzzy logic….
Automatic answer to intrusions, defending system threaten or attacked by new discovered intrusion types previously unknown or similar but different is also one of important features that should be inherent to this system. This includes also: local and global answer on attack intrusion especially wireless specific attacks detection. Generally speaking, wireless intrusion detection system is superset of intrusion detection system as all attacks known in wired networks are possible i.
This approach is intended to recognize more attacks including new ones. System is multilayered and multidimensional. Its components are autonomous, independent and intelligent, but have capabilities of mutual cooperation of components. If we say that 0 is proven intrusion, 1 is proven legal access, we can make limits in next way: A is highest limit of decision that access is automatically classified as intrusion B is lowest limit of decision that access is automatically classified as legal access.
System will consider as intrusion everything in range [] and deny access. System will allow access for []. Good visual presentation of this is slider with two movable notches representing low and high thresholds.
Anyway, some of this approach could be used to create base principles for building wireless intrusion detection systems. Collecting training data: Example: Obtain the audit logs for each user for a period of several days.
For each day and user, form a vector that represents how often the user executed each command. Training: Example: Train the neural network to identify the user based on these command distribution vectors. Performance: Example: Let the network identify the user for each new command distribution vector. These steps can be reused upon the time for future systems and knowledge base shared among many servers that are used in various networks.
As result, we will have fast growing base and systems with better efficiency and performances. WIDS as proposed here is good solution to protect network wireless and wired from various threats and attacks.
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