Techniques to Model and Derive a Cyber-Attacker’s Intelligence

2013 ◽  
pp. 162-180 ◽  
Author(s):  
Peter J. Hawrylak ◽  
Chris Hartney ◽  
Michael Haney ◽  
Jonathan Hamm ◽  
John Hale
Keyword(s):  
Intrusion Detection ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Cyber Attack ◽  
Computationally Efficient ◽  
Attack Graph ◽  
Detection Systems ◽  
Specialized Hardware ◽  
Time Required

Identifying the level of intelligence of a cyber-attacker is critical to detecting cyber-attacks and determining the next targets or steps of the adversary. This chapter explores intrusion detection systems (IDSs) which are the traditional tool for cyber-attack detection, and attack graphs which are a formalism used to model cyber-attacks. The time required to detect an attack can be reduced by classifying the attacker’s knowledge about the system to determine the traces or signatures for the IDS to look for in the audit logs. The adversary’s knowledge of the system can then be used to identify their most likely next steps from the attack graph. A computationally efficient technique to compute the likelihood and impact of each step of an attack is presented. The chapter concludes with a discussion describing the next steps for implementation of these processes in specialized hardware to achieve real-time attack detection.

scholarly journals Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

2021 ◽  
Vol 11 (4) ◽  
pp. 1674
Author(s):  
Nuno Oliveira ◽  
Isabel Praça ◽  
Eva Maia ◽  
Orlando Sousa
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Information And Communication Technologies ◽  
Network Traffic ◽  
Short Term Memory ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Detection Systems

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are shared continuously across the network, making it susceptible to an attack that can compromise data confidentiality, integrity, and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform the timely detection of malicious events through the inspection of network traffic or host-based logs. Many machine learning techniques have proven to be successful at conducting anomaly detection throughout the years, but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, which only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes suggest that anomaly detection can be better addressed from a sequential perspective. The LSTM is a highly reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and an f1-score of 91.66%.

scholarly journals Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems

2020 ◽  
Author(s):  
Nuno Oliveira ◽  
Isabel Praça ◽  
Eva Maia ◽  
Orlando Sousa
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Information And Communication Technologies ◽  
Network Traffic ◽  
Short Term Memory ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Detection Systems

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review

2019 ◽  
Author(s):  
Mingtao Wu ◽  
Young B. Moon
Keyword(s):  
Intrusion Detection ◽  
Manufacturing Systems ◽  
High Volume ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
False Alarms ◽  
Alert Correlation ◽  
Acoustic Image ◽  
Detection Systems

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.

Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems

2016 ◽  
Vol 66 (6) ◽  
pp. 612 ◽  
Author(s):  
M.R. Gauthama Raman ◽  
K. Kannan ◽  
S.K. Pal ◽  
V. S. Shankar Sriram
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Rough Set ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Detection Systems ◽  
Helly Property

Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.

scholarly journals Network Attacks Detection by Hierarchical Neural Network

2015 ◽  
Vol 4 (2) ◽  
pp. 119-132
Author(s):  
Mohammad Masoud Javidi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Single Agent ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Training Dataset ◽  
Detection Systems ◽  
Combination Of Classifiers

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier

scholarly journals Black Hole attack Detection using Fuzzy based Intrusion Detection Systems in MANET

2019 ◽  
Vol 151 ◽  
pp. 1176-1181 ◽  
Author(s):  
Houda Moudni ◽  
Mohamed Er-rouidi ◽  
Hicham Mouncif ◽  
Benachir El Hadadi
Keyword(s):  
Black Hole ◽  
Intrusion Detection ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Black Hole Attack ◽  
Detection Systems

scholarly journals A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1177
Author(s):  
Javed Asharf ◽  
Nour Moustafa ◽  
Hasnat Khurshid ◽  
Essam Debie ◽  
Waqas Haider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Cyber Attacks ◽  
The Internet ◽  
Cyber Attack ◽  
Control Approach ◽  
Detection Systems ◽  
Desktop Computers ◽  
Iot Devices

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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