Mobile Firewall System For Distributed Denial Of Service Defense In Internet Of Things Networks

Internet of Things (IoT) has seen unprecedented growth in the consumer space over the past ten years. The majority of IoT device manufacturers do not, however, build their products with cybersecurity in mind. The goal of the mobile firewall system is to move mitigation of network-diffused attacks closer to their source. Attack detection and mitigation is enforced using a machine that physically traverses the area. This machine uses a suite of security tools to protect the network. Our system provides advantages over current network attack mitigation techniques. Mobile firewalls can be deployed when there is no access to the network gateway or when no gateway exists, such as in IoT mesh networks. The focus of this thesis is to refine an explicit implementation for the mobile firewall system and evaluate its effectiveness. Evaluation of the mobile firewall system is analyzed using three simulated distributed denial of service case studies. Mobility is shown to be a great benefit when defending against physically distant attackers – the system takes no more than 131 seconds to fully nullify a worst-case attack

Active networks: an evolution of the internet

Active Networks can be seen as an evolution of the classical model of packet-switched networks. The traditional and ”passive” network model is based on a static definition of the network node behaviour. Active Networks propose an “active” model where the intermediate nodes (switches and routers) can load and execute user code contained in the data units (packets). Active Networks are a programmable network model, where bandwidth and computation are both considered shared network resources. This approach opens up new interesting research fields. This paper gives a short introduction of Active Networks, discusses the advantages they introduce and presents the research advances in this field

Defending Against IoT-Enabled DDoS Attacks at Critical Vantage Points on the Internet

The number of Internet of Things (IoT) devices continues to grow every year. Unfortunately, with the rise of IoT devices, the Internet is also witnessing a rise in the number and scale of IoT-enabled distributed denial-of-service (DDoS) attacks. However, there is a lack of network-based solutions targeted directly for IoT networks to address the problem of IoT-enabled DDoS. Unlike most security approaches for IoT which focus on hardening device security through hardware and/or software modification, which in many cases is infeasible, we introduce network-based approaches for addressing IoT-enabled DDoS attacks. We argue that in order to effectively defend the Internet against IoT-enabled DDoS attacks, it is necessary to consider network-wide defense at critical vantage points on the Internet. This dissertation is focused on three inherently connected and complimentary components: (1) preventing IoT devices from being turned into DDoS bots by inspecting traffic towards IoT networks at an upstream ISP/IXP, (2) detecting DDoS traffic leaving an IoT network by inspecting traffic at its gateway, and (3) mitigating attacks as close to the devices in an IoT network originating DDoS traffic. To this end, we present three security solutions to address the three aforementioned components to defend against IoT-enabled DDoS attacks

Mobile Malware and Smart Device Security: Trends, Challenges and Solutions

This work is part of the research to study trends and challenges of cyber security to smart devices in smart homes. We have seen the development and demand for seamless interconnectivity of smart devices to provide various functionality and abilities to users. While these devices provide more features and functionality, they also introduce new risks and threats. Subsequently, current cyber security issues related to smart devices are discussed and analyzed. The paper begins with related background and motivation. We identified mobile malware as one of the main issue in the smart devices' security. In the near future, mobile smart device users can expect to see a striking increase in malware and notable advancements in malware-related attacks, particularly on the Android platform as the user base has grown exponentially. We discuss and analyzed mobile malware in details and identified challenges and future trends in this area. Then we propose and discuss an integrated security solution for cyber security in smart devices to tackle the issue

Behaviour based anomaly detection system for smartphones using machine learning algorithm

In this research, we propose a novel, platform independent behaviour-based anomaly detection system for smartphones. The fundamental premise of this system is that every smartphone user has unique usage patterns. By modelling these patterns into a profile we can uniquely identify users. To evaluate this hypothesis, we conducted an experiment in which a data collection application was developed to accumulate real-life dataset consisting of application usage statistics, various system metrics and contextual information from smartphones. Descriptive statistical analysis was performed on our dataset to identify patterns of dissimilarity in smartphone usage of the participants of our experiment. Following this analysis, a Machine Learning algorithm was applied on the dataset to create a baseline usage profile for each participant. These profiles were compared to monitor deviations from baseline in a series of tests that we conducted, to determine the profiling accuracy. In the first test, seven day smartphone usage data consisting of eight features and an observation interval of one hour was used and an accuracy range of 73.41% to 100% was achieved. In this test, 8 out 10 user profiles were more than 95% accurate. The second test, utilised the entire dataset and achieved average accuracy of 44.50% to 95.48%. Not only these results are very promising in differentiating participants based on their usage, the implications of this research are far reaching as our system can also be extended to provide transparent, continuous user authentication on smartphones or work as a risk scoring engine for other Intrusion Detection System

Behaviour Profiling for Mobile Devices

With more than 5 billion users globally, mobile devices have become ubiquitous in our daily life. The modern mobile handheld device is capable of providing many multimedia services through a wide range of applications over multiple networks as well as on the handheld device itself. These services are predominantly driven by data, which is increasingly associated with sensitive information. Such a trend raises the security requirement for reliable and robust verification techniques of users.This thesis explores the end-user verification requirements of mobile devices and proposes a novel Behaviour Profiling security framework for mobile devices. The research starts with a critical review of existing mobile technologies, security threats and mechanisms, and highlights a broad range of weaknesses. Therefore, attention is given to biometric verification techniques which have the ability to offer better security. Despite a large number of biometric works carried out in the area of transparent authentication systems (TAS) and Intrusion Detection Systems (IDS), each have a set of weaknesses that fail to provide a comprehensive solution. They are either reliant upon a specific behaviour to enable the system to function or only capable of providing security for network based services. To this end, the behaviour profiling technique is identified as a potential candidate to provide high level security from both authentication and IDS aspects, operating in a continuous and transparent manner within the mobile host environment.This research examines the feasibility of a behaviour profiling technique through mobile users general applications usage, telephone, text message and multi-instance application usage with the best experimental results Equal Error Rates (EER) of 13.5%, 5.4%, 2.2% and 10% respectively. Based upon this information, a novel architecture of Behaviour Profiling on mobile devices is proposed. The framework is able to provide a robust, continuous and non-intrusive verification mechanism in standalone, TAS or IDS modes, regardless of device hardware configuration. The framework is able to utilise user behaviour to continuously evaluate the system security status of the device. With a high system security level, users are granted with instant access to sensitive services and data, while with lower system security levels, users are required to reassure their identity before accessing sensitive services.The core functions of the novel framework are validated through the implementation of a simulation system. A series of security scenarios are designed to demonstrate the effectiveness of the novel framework to verify legitimate and imposter activities. By employing the smoothing function of three applications, verification time of 3 minutes and a time period of 60 minutes of the degradation function, the Behaviour Profiling framework achieved the best performance with False Rejection Rate (FRR) rates of 7.57%, 77% and 11.24% for the normal, protected and overall applications respectively and with False Acceptance Rate (FAR) rates of 3.42%, 15.29% and 4.09% for their counterparts

Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

Malware attack predictive analytics in a cyber supply chain context using machine learning

Due to the invincibility nature of cyber attacks on the cyber supply chain (CSC), and the cascading effects ofmalware infections, we use machine learning to predictattacks. As organizations have become more reliant on CSC systems for business continuity, so are the increase invulnerabilities and the threat landscapes. Some traditionalapproach to detecting and defending malware attack haslargely been antimalware or antivirus software such as spam filters, firewall, and IDS/IPS. These tools largelysucceed, however, as threat actors get more intelligent, theyare able to circumvent and affect nodes on systems which then propagates. In our previous work, we characterizedthreat actor activities, including presumed intent and historically observed behaviour, for the purpose of ascertaining the current threats that could be exploited. Inthis paper, we use ML techniques to learn dataset and predict which CSC nodes have detection or no detection. The purpose is to predict which modes are venerable to cyberattacks and for predicting future trends. Todemonstrate the applicability of our approach, we used adataset from Microsoft Malware Prediction website. Further, an ensemble is used to link Logistic Regression, and Decision Tree and SVM algorithms in Majority Votingand run on the training data and then use 10-fold crossvalidation to test the parameter estimation, accurate results and predictions. The results show that ML algorithms in Decision Trees methods can be used in cyber supply chainpredict analytics to detect and predict future cyber attacktrends

Classification of malware attacks using machine learning in decision tree

Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends