5,579 research outputs found
Hierarchical Design Based Intrusion Detection System For Wireless Ad hoc Network
In recent years, wireless ad hoc sensor network becomes popular both in civil
and military jobs. However, security is one of the significant challenges for
sensor network because of their deployment in open and unprotected environment.
As cryptographic mechanism is not enough to protect sensor network from
external attacks, intrusion detection system needs to be introduced. Though
intrusion prevention mechanism is one of the major and efficient methods
against attacks, but there might be some attacks for which prevention method is
not known. Besides preventing the system from some known attacks, intrusion
detection system gather necessary information related to attack technique and
help in the development of intrusion prevention system. In addition to
reviewing the present attacks available in wireless sensor network this paper
examines the current efforts to intrusion detection system against wireless
sensor network. In this paper we propose a hierarchical architectural design
based intrusion detection system that fits the current demands and restrictions
of wireless ad hoc sensor network. In this proposed intrusion detection system
architecture we followed clustering mechanism to build a four level
hierarchical network which enhances network scalability to large geographical
area and use both anomaly and misuse detection techniques for intrusion
detection. We introduce policy based detection mechanism as well as intrusion
response together with GSM cell concept for intrusion detection architecture.Comment: 16 pages, International Journal of Network Security & Its
Applications (IJNSA), Vol.2, No.3, July 2010. arXiv admin note: text overlap
with arXiv:1111.1933 by other author
Towards automated incident handling: how to select an appropriate response against a network-based attack?
The increasing amount of network-based attacks evolved to one of the top concerns responsible for network infrastructure and service outages. In order to counteract these threats, computer networks are monitored to detect malicious traffic and initiate suitable reactions. However, initiating a suitable reaction is a process of selecting an appropriate response related to the identified network-based attack. The process of selecting a response requires to take into account the economics of an reaction e.g., risks and benefits. The literature describes several response selection models, but they are not widely adopted. In addition, these models and their evaluation are often not reproducible due to closed testing data. In this paper, we introduce a new response selection model, called REASSESS, that allows to mitigate network-based attacks by incorporating an intuitive response selection process that evaluates negative and positive impacts associated with each countermeasure. We compare REASSESS with the response selection models of IE-IRS, ADEPTS, CS-IRS, and TVA and show that REASSESS is able to select the most appropriate response to an attack in consideration of the positive and negative impacts and thus reduces the effects caused by an network-based attack. Further, we show that REASSESS is aligned to the NIST incident life cycle. We expect REASSESS to help organizations to select the most appropriate response measure against a detected network-based attack, and hence contribute to mitigate them
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Advanced attack tree based intrusion detection
Computer network systems are constantly under attack or have to deal with attack
attempts. The first step in any network’s ability to fight against intrusive attacks
is to be able to detect intrusions when they are occurring. Intrusion Detection
Systems (IDS) are therefore vital in any kind of network, just as antivirus is a
vital part of a computer system. With the increasing computer network intrusion
sophistication and complexity, most of the victim systems are compromised by
sophisticated multi-step attacks. In order to provide advanced intrusion detection
capability against the multi-step attacks, it makes sense to adopt a rigorous and
generalising view to tackling intrusion attacks. One direction towards achieving
this goal is via modelling and consequently, modelling based detection.
An IDS is required that has good quality of detection capability, not only to
be able to detect higher-level attacks and describe the state of ongoing multi-step
attacks, but also to be able to determine the achievement of high-level attack
detection even if any of the modelled low-level attacks are missed by the detector,
because no alert being generated may represent that the corresponding low-level
attack is either not being conducted by the adversary or being conducted by the
adversary but evades the detection.
This thesis presents an attack tree based intrusion detection to detect multistep
attacks. An advanced attack tree modelling technique, Attack Detection Tree,
is proposed to model the multi-step attacks and facilitate intrusion detection. In
addition, the notion of Quality of Detectability is proposed to describe the ongoing
states of both intrusion and intrusion detection. Moreover, a detection uncertainty
assessment mechanism is proposed to apply the measured evidence to deal with
the uncertainty issues during the assessment process to determine the achievement
of high-level attacks even if any modelled low-level incidents may be missing
Intrusion detection and response for system and network attacks
This work focuses on Intrusion Detection System (IDS) and Intrusion Response System (IRS) model for system and network attacks. For decades, IDS has evolved tremendously and has become highly sophisticated. However, the response to an attack is still manually triggered by an administrator who relies on static mapping to counteract the intrusion. The speed of attack-spread and its increased complexities in recent years have shown that it is highly critical to develop an automatic IRS. Moreover, manual responses are not flexible and effective in distributed environment without infrastructure.
This work presents a cost based response model that is tightly coupled with multi-source IDS. It is a known fact that any system can be broken down into smaller granules of services and resources. A dependency graph is employed to describe the relations between services and resources in a system. This dependency graph is also used to propagate the total value of the system down to the service and resource levels. The damage cost of the intrusion and the response cost of the responses are evaluated using the dependency graph. Using several performance metrics, a response which brings the most benefit to the system is deployed.
We demonstrate the abilities of our model by using buffer overflow attack caused by a computer worm on Optimized Link State Routing (OLSR) protocol on a wireless ad-hoc network environment. Experimental results show that our model is effective and is highly practical
Attack Graph Generation and Analysis Techniques
As computer networks are emerging in everyday life, network security has become an important issue. Simultaneously, attacks are becoming more sophisticated, making the defense of computer networks increasingly difficult. Attack graph is a modeling tool used in the assessment of security of enterprise networks. Since its introduction a considerable amount of research effort has been spent in the development of theory and practices around the idea of attack graph. This paper presents a consolidated view of major attack graph generation and analysis techniques
Malware in the Future? Forecasting of Analyst Detection of Cyber Events
There have been extensive efforts in government, academia, and industry to
anticipate, forecast, and mitigate cyber attacks. A common approach is
time-series forecasting of cyber attacks based on data from network telescopes,
honeypots, and automated intrusion detection/prevention systems. This research
has uncovered key insights such as systematicity in cyber attacks. Here, we
propose an alternate perspective of this problem by performing forecasting of
attacks that are analyst-detected and -verified occurrences of malware. We call
these instances of malware cyber event data. Specifically, our dataset was
analyst-detected incidents from a large operational Computer Security Service
Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on
automated systems. Our data set consists of weekly counts of cyber events over
approximately seven years. Since all cyber events were validated by analysts,
our dataset is unlikely to have false positives which are often endemic in
other sources of data. Further, the higher-quality data could be used for a
number for resource allocation, estimation of security resources, and the
development of effective risk-management strategies. We used a Bayesian State
Space Model for forecasting and found that events one week ahead could be
predicted. To quantify bursts, we used a Markov model. Our findings of
systematicity in analyst-detected cyber attacks are consistent with previous
work using other sources. The advanced information provided by a forecast may
help with threat awareness by providing a probable value and range for future
cyber events one week ahead. Other potential applications for cyber event
forecasting include proactive allocation of resources and capabilities for
cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs.
Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa
- …