Proc. of Int. Conf. on Advances in Recent Technologies in Communication and Computing 2011 SURVEY ON INTRUSION DETECTION METHODS

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1 SURVEY ON INTRUSION DETECTION METHODS Sanoop Mallissery 1, Jeevan Prabhu 2, Raghavendra Ganiga 3 1,2,3 Department of Information and Communication Technology, MIT, Manipal, India sanoop.m@manipal.edu,jeevan.prabhu@manipal.edu,raghavendra.n@manipal.edu Abstract: Intrusions in an information system are the activities that violate the security policy of the system, and intrusion detection is the process used to identify intrusions. Intrusion detection has been studied for approximately 20 years. It is based on the beliefs that an intruder s behavior will be noticeably different from that of a legitimate user and that many unauthorized actions will be detectable. Intrusion detection systems (IDSs) are usually deployed along with other preventive security mechanisms, such as access control and authentication, as a second line of defense that protects information systems. There are several reasons that make intrusion detection a necessary part of the entire defense system. This paper describes various Intrusion Detection methods like pattern matching, state full pattern matching, protocol decode-based analysis etc and how fuzzy clustering can apply in IDS. Keywords: Intrusion, Protocol Decode-Based Analysis, Fuzzy Clustering, Integrated Service Checker. INTRODUCTION Intrusion detection is very important aspects of protecting the cyber infrastructure from terrorist attack or from hackers. Intrusion prevention technique such as firewall, filtering router policies fails to stop much type of attacks. Therefore, no matter how secure you try to make your system. Intrusion still happens and so they must be detected. Intrusion detection systems are becoming an important part of your computer system, and security [1]. An intrusion detection system is used to detect several types of malicious behaviors that can compromise the security and trust of a computer system. This includes network attacks against vulnerable services, data driven attacks on applications, host based attacks such as privilege escalation, unauthorized logins and access to sensitive files, and malware (viruses, trojan horses and worms). IDS can be composed of several components: Sensors which generate security events, a Console to 251 monitor events and alerts and control the sensors, and a central Engine that records events logged by the sensors in a database and use a system of rules to generate alerts from security events received. There are several ways to categorize IDS depending on the type and location of the sensors and the methodology used by the engine to generate alerts. In many simple IDS implementations all three components are combined in a single device or appliance. Intrusion detection can allow for the prevention of certainty, attacks severity relative to different type of attack and vulnerability of components under attack the response may be kill the connection, install filtering rules, and disable user account. INTRUSION DETECTION SYSTEM In order to properly response to an asset-attack it must first be detected and then all of its characteristics uncovered and documented. Once the attack or misuse recognized the response can be automatic or manual and can be include termination of connection, or host of response aimed at apprehending the attacker. Any technique such as firewalls, identification, authentication procedures and encryption are not design with any of above security issues. This particular security issues are the province of Intrusion Detection System. Network-Based IDS Network based IDS (Fig. 1) are best suited for alert generation of intrusion from outside the perimeter of the enterprise. The network based IDS are inserted at various points on LAN and observe packets traffic on the Network information is assembled into packets and transmitted on LAN or Internet. N-B IDS are valuable if they placed just outside the firewalls, thereby altering personal to incoming packets that might circumvent the firewall [2].

2 Some Network-Based IDS take or allows taking IDS scenarios and the benefit of implementing both a input of Custom signatures taken from user s security Network and a Host Based Solution. policy, which permits limited detection security policy violation. Fig. 1 Network-Based Ids Host-Based IDS Fig. 2 Host-Based IDS Host-based IDS (Fig. 2) placed monitoring Sensors also known as agents on network resources nodes to monitor audit logs that are generated by Network Operating System or application program. Audit logs contain records for events and activities taking place at individual Network resources. Because this Host-Based IDS can detect attacks that cannot be seen by Networkbased IDS. Such as Intrusion and misuse and misuse by trusted insider. Host-Based can overcome the problems associated with N/W based IDS immediately after alarming the security personnel can locate the source provided by site security policy [2]. THE NEED FOR BOTH TYPES As you can clearly see both network and host-based IDS solutions have unique strengths and benefits over one another and that is why the next generation IDS must evolve to include a tightly integrated host and network component. There are no Silver Bullets when it comes to network security but adding these two required components will greatly enhance your resistance to attack. Fig. 3 is a quick graphical representation that helps represent the independent network and host-based 252 Fig. 3 Intruder and victim METHODS FOR ID There are different methods for Intrusion Detection. Some of the methods adopted are the following: Pattern Matching Pattern matching is based on looking for a fixed sequence of bytes in a single packet. As its name suggests, it is an approach that is fairly rigid but simple to employ. In most cases the pattern is matched against only if the suspect packet is associated with a particular

3 service or, more precisely, destined to/from a particular port [3, 4]. The structure of a signature based on the simple pattern-matching approach might be as follows if the packet is IPv4 and TCP and the destination port is 2222 and the payload contains the string foo, fire an alarm. This example of a pattern match, of course, is a very simple one, but the variations from this point are also simplistic. State Full Pattern Matching A more sophisticated method is state full pattern matching-based analysis. This method of signature development adds to the pattern match the concept that because a network stream comprises more than single atomic packets, matches should be made in context within the state of the stream. This means that systems that perform this type of signature analysis must consider arrival order of packets in a TCP stream and should handle matching patterns across packet boundaries [8, 12]. Protocol Decode-Based Analysis Protocol decode-based signatures are in many ways intelligent extensions to state full pattern matches. This class of signature is implemented by decoding the various elements in the same manner as the client or server in the conversation would. When the elements of the protocol are identified, the IDS apply rules defined by the RFCs to look for violations. In some instances, these violations are found with pattern matches within a specific protocol field, and some require more advanced techniques that account for such variables as the length of a field or the number of arguments. Anomaly-Based Analysis Anomaly-based signatures are typically geared to looking for network traffic that deviates from what is seen normally. The biggest problem with this methodology is to first define what normal is. Some systems have hard-coded definitions of normal, and in this case they could be considered heuristic-based systems. Some systems are built to learn normal, but the challenge with these systems is to eliminate the possibility of improperly classifying abnormal behavior as normal [11]. FUZZY CLUSTERING FOR IDS The underline premise of our intrusion detection model is to describe attacks as instances of an ontology using a semantically rich language like DAML. This ontology capture information attacks such as the system component it affects, the consequences the attacks the mean of attack the location of attacker. Such target centric ontology has been developed by under conferral, hence our intrusion detection model consist of two phases. The initial phase s data mining techniques to analyze data stream that capture process, system and network states and detect anomalous behavior and the second or high level phase reasons over data that is representative of the anomaly defined as instance of ontology. One way to build the models from these data streams is to use fuzzy clustering in which dissimilar matrix of object to be cluster as input. The objective function are based on selecting, representative objects from the features set in such a way that total fuzzy dissimilarity within each cluster is minimized [5, 7]. Heuristic Based Analysis Heuristic-based signatures use some type of algorithmic logic on which to base their alarm decisions. These algorithms are often statistical evaluations of the type of traffic being presented. A good example of this type of signature is a signature that would be used to detect a port sweep. This signature looks for the presence of a threshold number of unique ports being touched on a particular machine. The signature may further restrict itself through the specification of the types of packets that it is interested in (that is, SYN packets). 253 Data Mining and ID According to Grossman Data mining as being concerned with uncovering patterns, associations, changes, anomalies and statistically significant structures and event in data Data mining is the process of selecting, exploring and modeling large amount of data to uncovers previously unknown pattern of business advantages [6]. Data Collection and Modeling

4 The data collected at server level (Apache web server) member of cluster or not. In fuzzy clustering the data and at system (Operating system) level (LINUX kernel) membership of data point in cluster is fuzzy decision. A which is at low level data process system network level data point is considered to be the member of every process data include number of child process forked by cluster with given possibility membership value that parent process, the amount of time spent by the process ranges from 0 to 1 [5]. in user and system modes, attributes describing memory The FCMdd algorithm produces multiple objects usage, and attributes describing signal information. The for cluster for given best data point, if distance from any collector network data is comprised of statistical randomly selected object is only consideration; it might information regenerating the IP, ICMP, UDP, and TCP wrongly classify the data point as member or layers of network protocol stack [10]. nonmember. To solve this issue we use two-outlier At the system level we collect information detection. Both take all object of cluster into account. regarding memory, CPU load etc. See Fig. 4. Here Hence fuzzy clustering with IDS model call for output stream samples are taken from host under sampling low level kernel data at close intervals and observation and preprocessor using data mining using outlier detection to capture anomalous behavior technique. within those data streams. Principal Component Analysis According to PCA construct new representation of feature set where in the maximally variants dimensions of the data is captured once applying PCA, dimensionality of feature set is reduced while the maximum amount of information and pattern in the data are preserved. INTEGRATED SERVICE CHECKER Network Intrusion Detection System There are several ways to do filtering for NIDS alerts which is needed due to that NIDS produce lot of false positive alerts as it cannot have usually no way of knowing if they attack actually succeeded or failed. So additional elements should be added on the top of NIDS system to check the targeted host, which is integrated service checker (ISC) many current NIDS system are unable to test the remote operating system due to which more false positive alerts are produced. Fig. 4: Data collection and modelling Fuzzy Clustering and Detection Once the dimensionality of data set is reducing, we use FCMdd algorithm [9] to cluster the data points In clustering approaches membership of data point is the cluster is binary decision, either the data point is a 254 System Layout Sensors are running on operating system (Red hat LINUX system) and NID element is the short system. Sensors are placed in such a way that it can see all outgoing and incoming traffic from network. Data Collection and Analysis Data collection set up so that it cannot interface with data collected. This is achieved by setting the interface is such mode with operating system that cannot send any packet. For collecting data fiber link on gigabyte speed is used. However, this short system rule can still lead to false. A possibility, when it records OFF site web traffic and the string is matched for some reason. The checker system is needed especially when it is identified what attacker is looking for and record IP address of the hackers.

5 CONCLUSION At this stage we have study the types of ID and methods of ID. It is appear that using low level kernel data stream to model the quiescent state of the system is a viable Intrusion Detection system like fuzzy clustering appears to be effective for outlier detection within this low level system, process and network data stream. NIDS system Integrated with automatic checking could be included in our security operation and can be used as bases for follow up which shows advantages of automatic checking NIDS system alone is most of times not enough further checks needs to be applied before follow up with system administrator. But for better efficiency, our system must be real time processing which should be achieved in future. In future alerts generated by IDS are sending to oracle database and actual is generated and information about administrator and local user is added to the . REFERENCES [4] Sandeep Kumar, Eugene H.Spafford, A Pattern Matching Model for Misuse Intrusion Detection, The COAST Project, Department of Computer Sciences, Purdue University, West Lafayette, IN [1] Aly EI Semary, Jamica Edmonds, Jesus Gonzalez- Pino, Mauricio Papa, Applying Data mining of Fuzzy Association Rules to Network Intrusion Detection, IEEE Proc. On Information Assurance, West Point, New York, 2006, pp [2] H. Debar, M. Dacier, and A. Wespi, Towards a taxonomy of intrusion detection system, Computer Networks, 31(8): , [3] Sandeep Kumar, Eugene H.Spafford, An Application of Pattern Matching in Intrusion Detection, Technical Report , Department of Computer Sciences, Purdue University, West Lafayette, March [5] John E. Dickerson, Jukka Juslin, Ourania Koukousoula, Julie A. Dickerson, Fuzzy Intrusion Detection, Electrical and Computer Engineering Department, Iowa State University, Ames, IA, USA. [6] Pietraszek, T. and Tanner A, Data mining and machine learning -Towards reducing false positives in intrusion detection, Information security technical report [ ] 10(3) pp , [7]K.C.C.Chan and W.H.Au, Mining Fuzzy Association Rules, Proc.Of ACM CIKM, 1997, pp [8] Gonzalo Navarro, Mathieu Raffinot, Flexible Pattern Matching in Strings, Cambridge University Press 2002, ISBN [9] Krishnapuram R, Joshi A,Yi L, A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering In IEEE International Conference on Fuzzy Systems, Korea, August [10] Fayyad U, Uthurusamy R, Data mining and knowledge discovery indatabases: Introduction to the special issue, Communications of the ACM, 39(11), November, [11] Matthias Vallentin, Robin Sommer, Jason Lee, Craig Leres, The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware, International Computer Science Institute, Lawrence Berkeley National Laboratory, TUM unchen,

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