scholarly journals Strategy and Knowledge-Based XML Attack Detection Systems using Ontology

2020 ◽  
Vol 8 (5) ◽  
pp. 798-801
Keyword(s):  
Probabilistic Approach ◽  
Attack Detection ◽  
Data Detection ◽  
Sensitive Data ◽  
Detection Systems ◽  
Knowledge Based ◽  
Target Network ◽  
Attack Path ◽  
Original Size ◽  
Degree Of Severity

Today's highly skilled attackers exploit many of the vulnerabilities in their network. On the other hand, the risk of data leakage has increased dramatically because software or application vulnerability is always left without a fix. Such vulnerability using the (Zero Day), hackers will receive the target network, and can steal sensitive data. Detection of zero day traditional defenses is difficult, because the signature information zero-day attacks are unknown. Therefore, the need for new security solutions, that detect zero-day attacks, and evaluate the identified zero day vulnerability severity. The paper proposed an approach for detecting unknown vulnerabilities. The system is a framework that is a comprehensive approach for detecting and prioritizing zero-day attacks and removes these detected attacks. The proposed framework is based on probabilistic approach, to determine the Zero-Day attack path, and the subsequent degree of severity of the identified zero-day vulnerability. It is a hybrid of detection and removal method based on the detection of unknown defects present in the network, which is not yet detected. The system also shows the file with original size and with an attacked size

scholarly journals Big knowledge-based semantic correlation for detecting slow and low-level advanced persistent threats

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Amir Mohammadzade Lajevardi ◽  
Morteza Amini
Keyword(s):  
Time Window ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Knowledge Based System ◽  
Low Level ◽  
Semantic Correlation ◽  
Detection Systems ◽  
Knowledge Based ◽  
Advanced Persistent Threats ◽  
Short Time Window

AbstractTargeted cyber attacks, which today are known as Advanced Persistent Threats (APTs), use low and slow patterns to bypass intrusion detection and alert correlation systems. Since most of the attack detection approaches use a short time-window, the slow APTs abuse this weakness to escape from the detection systems. In these situations, the intruders increase the time of attacks and move as slowly as possible by some tricks such as using sleeper and wake up functions and make detection difficult for such detection systems. In addition, low APTs use trusted subjects or agents to conceal any footprint and abnormalities in the victim system by some tricks such as code injection and stealing digital certificates. In this paper, a new solution is proposed for detecting both low and slow APTs. The proposed approach uses low-level interception, knowledge-based system, system ontology, and semantic correlation to detect low-level attacks. Since using semantic-based correlation is not applicable for detecting slow attacks due to its significant processing overhead, we propose a scalable knowledge-based system that uses three different concepts and approaches to reduce the time complexity including (1) flexible sliding window called Vermiform window to analyze and correlate system events instead of using fixed-size time-window, (2) effective inference using a scalable inference engine called SANSA, and (3) data reduction by ontology-based data abstraction. We can detect the slow APTs whose attack duration is about several months. Evaluation of the proposed approach on a dataset containing many APT scenarios shows 84.21% of sensitivity and 82.16% of specificity.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

2021 ◽  
Vol 20 (5) ◽  
pp. 1-17
Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan
Keyword(s):  
Neural Network ◽  
Language Modeling ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Sensitive Data ◽  
Phase Sequence ◽  
Smartphone Technology ◽  
Conventional Machine ◽  
Sequence Generator

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

scholarly journals Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

2021 ◽  
Vol 11 (12) ◽  
pp. 5685
Author(s):  
Hosam Aljihani ◽  
Fathy Eassa ◽  
Khalid Almarhabi ◽  
Abdullah Algarni ◽  
Abdulaziz Attaallah
Keyword(s):  
Attack Detection ◽  
Detection Methods ◽  
Software Systems ◽  
Distributed Software ◽  
Detection Techniques ◽  
Detection Systems ◽  
Novel Approach ◽  
Validation Experiment ◽  
Critical Issues ◽  
Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

scholarly journals Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

2021 ◽  
Author(s):  
Rémi Thiéblemont ◽  
Gonéri Le Cozannet ◽  
Jérémy Rohmer ◽  
Alexandra Toimil ◽  
Moisés Álvarez-Cuesta ◽  
...  
Keyword(s):  
Sea Level ◽  
Probabilistic Approach ◽  
Shoreline Change ◽  
Sandy Beaches ◽  
Climate Mitigation ◽  
Deep Uncertainty ◽  
Mean Sea Level ◽  
Uncertain Variables ◽  
Knowledge Based ◽  
Global Mean Sea Level

Abstract. Global mean sea-level rise and its acceleration are projected to aggravate coastal erosion over the 21st century, which constitutes a major challenge for coastal adaptation. Projections of shoreline retreat are highly uncertain, however, namely due to deeply uncertain mean sea-level projections and the absence of consensus on a coastal impact model. An improved understanding and a better quantification of these sources of deep uncertainty are hence required to improve coastal risk management and inform adaptation decisions. In this work we present and apply a new extra-probabilistic framework to develop shoreline change projections of sandy coasts that allows considering intrinsic (or aleatory) and knowledge-based (or epistemic) uncertainties exhaustively and transparently. This framework builds upon an empirical shoreline change model to which we ascribe possibility functions to represent deeply uncertain variables. The model is applied to two local sites in Aquitaine (France) and Castellón (Spain). First, we validate the framework against historical shoreline observations and then develop shoreline change projections that account for possible (although unlikely) low-end and high-end mean sea-level scenarios. Our high-end projections show for instance that shoreline retreats of up to 200 m in Aquitaine and 130 m in Castellón are plausible by 2100, while low-end projections revealed that 58 m and 37 m modest shoreline retreats, respectively, are also plausible. Such extended intervals of possible future shoreline changes reflect an ambiguity in the probabilistic description of shoreline change projections, which could be substantially reduced by better constraining SLR projections and improving coastal impact models. We found for instance that if mean sea-level by 2100 does not exceed 1 m, the ambiguity can be reduced by more than 50 %. This could be achieved through an ambitious climate mitigation policy and improved knowledge on ice-sheets.

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