A Strategy for Structuring and Formalising Attack Patterns

Cyberpatterns ◽  
2014 ◽  
pp. 111-123 ◽  
Author(s):  
Clive Blackwell
Keyword(s):  
Attack Patterns

Coevolutionary modeling of cyber attack patterns and mitigations using public datasets

2021 ◽  
Author(s):  
Michal Shlapentokh-Rothman ◽  
Jonathan Kelly ◽  
Avital Baral ◽  
Erik Hemberg ◽  
Una-May O'Reilly
Keyword(s):  
Cyber Attack ◽  
Attack Patterns ◽  
Public Datasets

scholarly journals Within-Tree Distribution and Survival of the Eucalyptus Longhorned Borer Phoracantha semipunctata (Coleoptera: Cerambycidae) in a Mediterranean-Type Ecosystem

Insects ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 225
Author(s):  
Stephen Seaton ◽  
George Matusick ◽  
Giles Hardy
Keyword(s):  
Larval Development ◽  
Larval Survival ◽  
Severe Drought ◽  
Eucalyptus Marginata ◽  
Phoracantha Semipunctata ◽  
Tree Distribution ◽  
Eucalypt Forest ◽  
Attack Patterns ◽  
Mediterranean Type Ecosystem ◽  
Tree Characteristics

The attack patterns, infestation success and larval development of woodborers within living trees are complex and are largely shaped by host tree characteristics. Following a severe drought in a native eucalypt forest where outbreak densities of a native Australian beetle, the eucalyptus longhorned borer (Phoracantha semipunctata), occurred, a tree dissection study was conducted in Australia. This involved felling 40 trees each of jarrah (Eucalyptus marginata) and marri (Corymbia calophylla) that were cut into 1-m sections and neonate larval galleries, larvae in pupal cells and adult borer emergence were measured and added to give total numbers per tree to determine the within-tree distribution and survival of P. semipunctata. There was a significant impact on larval survival in both species, in contrast, pupal survival remained high. Within-tree distribution of P. semipunctata was directional with borer emergence and incidence of larval galleries both negatively associated with tree section height above the ground and positively associated with section diameter and bark thickness, reaching a maximum towards the base of trees. High incidence and survival in lower thicker tree sections indicate a more conducive environment for larval development, in contrast to poor larval survival in smaller thinner sections at the top of trees. The dependence of larval survival on tree characteristics controlling the within-tree distribution of borer emergence is emphasized, and needs to be considered when estimating the spread of borer populations during outbreaks.

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

scholarly journals RECONFIGURABLE SELF-ADDRESSABLE MEMORY-BASED FSM A SCALABLE INTRUSION DETECTION ENGINE

2012 ◽  
pp. 144-151
Author(s):  
B. SRILATHA ◽  
KRISHNA KISHORE
Keyword(s):  
High Speed ◽  
Finite State Machine ◽  
State Machine ◽  
Network Attack ◽  
Memory Efficiency ◽  
Attack Pattern ◽  
Current State ◽  
Incoming Packet ◽  
Finite State ◽  
Attack Patterns

One way to detect and thwart a network attack is to compare each incoming packet with predefined patterns, also Called an attack pattern database, and raise an alert upon detecting a match. This article presents a novel pattern-matching Engine that exploits a memory-based, programmable state machine to achieve deterministic processing rates that are Independent of packet and pattern characteristics. Our engine is a self addressable memory based finite state machine (samFsm), whose current state coding exhibits all its possible next states. Moreover, it is fully reconfigurable in that new attack Patterns can be updated easily. A methodology was developed to program the memory and logic. Specifically, we merge “non-equivalent” states by introducing “super characters” on their inputs to further enhance memory efficiency without Adding labels. This is the most high speed self addressable memory based fsm.sam-fsm is one of the most storage-Efficient machines and reduces the memory requirement by 60 times. Experimental results are presented to demonstrate the Validity of sam-fsm.

scholarly journals Honeypots for Internet of Things Research: An Effective Mitigation Tool

2021 ◽  
Author(s):  
Shen Xin En ◽  
Liu Si Ling ◽  
Fan Cheng Hao
Keyword(s):  
Internet Of Things ◽  
Operating Systems ◽  
Network Resources ◽  
Iot Security ◽  
Frequent Use ◽  
Attack Patterns ◽  
Iot Devices

In recent years, due to their frequent use and widespread use, IoT (Internet of Things) devices have become an attractive target for hackers. As a result of their limited network resources and complex operating systems, they are vulnerable to attacks. Using a honeypot can, therefore, be a very effective way of detecting malicious requests and capturing samples of exploits. The purpose of this article is to introduce honeypots, the rise of IoT devices, and how they can be exploited by attackers. Various honeypot ecosystems will be investigated further for capturing and analyzing information from attacks against these IoT devices. As well as how to leverage proactive strategies in terms of IoT security, it will provide insights on the attack vectors present in most IoT systems, along with understanding attack patterns.

scholarly journals Autoencoder Based Anomaly Detection for SCADA Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 83-99
Author(s):  
Sajid Nazir ◽  
Shushma Patel ◽  
Dilip Patel
Keyword(s):  
Cyber Attacks ◽  
Traffic Patterns ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Physical Systems ◽  
Anomalous Data ◽  
National Importance ◽  
Attack Patterns ◽  
Attack Surface ◽  
Scada Networks

Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.

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