PhishDef: URL Names Say It All

Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by attackers. Second, we evaluate the classification accuracy when using only lexical features, both automatically and hand-selected, vs. when using additional features. We show that lexical features are sufficient for all practical purposes. Third, we thoroughly compare several classification algorithms, and we propose to use an online method (AROW) that is able to overcome noisy training data. Based on the insights gained from our analysis, we propose PhishDef, a phishing detection system that uses only URL names and combines the above three elements. PhishDef is a highly accurate method (when compared to state-of-the-art approaches over real datasets), lightweight (thus appropriate for online and client-side deployment), proactive (based on online classification rather than blacklists), and resilient to training data inaccuracies (thus enabling the use of large noisy training data).Comment: 9 pages, submitted to IEEE INFOCOM 201

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Tactics, Techniques and Procedures (TTPs) to Augment Cyber Threat Intelligence (CTI): A Comprehensive Study

Sharing Threat Intelligence is now one of the biggest trends in cyber security industry. Today, no one can deny the necessity for information sharing to fight the cyber battle. The massive production of raw and redundant data coupled with the increasingly innovative attack vectors of the perpetrators demands an ecosystem to scrutinize the information, detect and react to take a defensive stance. Having enough sources for threat intelligence or having too many security tools are the least of our problems. The main challenge lies in threat knowledge management, interoperability between different security tools and then converting these filtered data into actionable items across multiple devices. Large datasets may help filtering the massive information gathering, open standards may somewhat facilitate the interoperability issues, and machine learning may partly aid the learning of malicious traits and features of attack, but how do we coordinate the actionable responses across devices, networks, and other ecosystems to be proactive rather than reactive? This paper presents a study of current threat intelligence landscape (Tactic), information sources, basic Indicators of Compromise (IOCs) (Technique) and STIX and TAXII standard as open source frameworks (Procedure) to augment Cyber Threat Intelligence (CTI) sharing

A Cloud-based Intrusion Detection and Prevention System for Mobile Voting in South Africa

Publishe ThesisInformation and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational resources of mobile phones, more work needs to be done to alleviate the false-negative alerts caused by the anomaly detection method. This study adopted Suricata as a suitable cloud-based analysis engine to protect a mobile voting application like XaP

A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s)

Anomaly detection through User Behaviour Analysis

The rise in cyber-attacks and cyber-crime is causing more and more organizations and individuals to consider the correct implementation of their security systems. The consequences of a security breach can be devastating, ranging from loss of public confidence to bankruptcy. Traditional techniques for detecting and stopping malware rely on building a database of known signatures using known samples of malware. However, these techniques are not very effective at detecting zero-day exploits because there are no samples in their malware signature databases. The limitation of not being able to detect zero-day exploits leaves organisations vulnerable to new and evolving malware threats. To address this challenge, this thesis proposes a novel approach to malware detection using machine learning techniques. The proposed approach creates a user profile that trains a machine learning model using only normal user behaviour data, and detects malware by identifying deviations from this profile. In this way, the proposed approach can detect zero-day malware and other previously unknown threats without having a specific database of malware signatures. The proposed approach is evaluated using real-world datasets, and different machine learning algorithms are compared to evaluate their performance in detecting unknown threats. The results show that the proposed approach is effective in detecting malware, achieving high accuracy and low false positive rates. This thesis contributes to the field of malware detection by providing a new perspective and approach that complements existing methods, and has the potential to improve the overall security of organisations and individuals in the face of evolving cybersecurity threats

A Lightweight Attribute-Based Access Control System for IoT.

The evolution of the Internet of things (IoT) has made a significant impact on our daily and professional life. Home and office automation are now even easier with the implementation of IoT. Multiple sensors are connected to monitor the production line, or to control an unmanned environment is now a reality. Sensors are now smart enough to sense an environment and also communicate over the Internet. That is why, implementing an IoT system within the production line, hospitals, office space, or at home could be beneficial as a human can interact over the Internet at any time to know the environment. 61% of International Data Corporation (IDC) surveyed organizations are actively pursuing IoT initiatives, and 6.8% of the average IT budgets is also being allocated to IoT initiatives. However, the security risks are still unknown, and 34% of respondents pointed out that data safety is their primary concern [1]. IoT sensors are being open to the users with portable/mobile devices. These mobile devices have enough computational power and make it di cult to track down who is using the data or resources. That is why this research focuses on proposing a dynamic access control system for portable devices in IoT environment. The proposed architecture evaluates user context information from mobile devices and calculates trust value by matching with de ned policies to mitigate IoT risks. The cloud application acts as a trust module or gatekeeper that provides the authorization access to READ, WRITE, and control the IoT sensor. The goal of this thesis is to offer an access control system that is dynamic, flexible, and lightweight. This proposed access control architecture can secure IoT sensors as well as protect sensor data. A prototype of the working model of the cloud, mobile application, and sensors is developed to prove the concept and evaluated against automated generated web requests to measure the response time and performance overhead. The results show that the proposed system requires less interaction time than the state-of-the-art methods

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Endpoints and Interdependencies in Internet of Things Residual Artifacts: Measurements, Analyses, and Insights into Defenses

The usage of Internet of Things (IoT) devices is growing fast. Moreover, the lack of security measures among the IoT devices and their persistent online connection give adversaries an opportunity to exploit them for multiple types of attacks, such as distributed denial-of-service (DDoS). To understand the risks of IoT devices, we analyze IoT malware from an endpoint standpoint. We investigate the relationship between endpoints infected and attacked by IoT malware, and gain insights into the underlying dynamics in the malware ecosystem. We observe the affinities and different patterns among endpoints. Towards this, we reverse-engineer 2,423 IoT malware samples and extract IP addresses from them. We further gather information about these endpoints from Internet-wide scans. For masked IP addresses, we examine their network distribution, with networks accumulating more than 100 million endpoints. Moreover, we conduct a network penetration analysis, leveraging information such as active ports, vulnerabilities, and organizations. We discover the possibility of ports being an entry point of attack and observe the low presence of vulnerable services in dropzones. Our analysis shows the tolerance of organizations towards endpoints with malicious intent. To understand the dependencies among malware, we highlight dropzone characteristics including spatial, network, and organizational affinities. Towards the analysis of dropzones\u27 interdependencies and dynamics, we identify dropzones chains. In particular, we identify 56 unique chains, which unveil coordination among different malware families. Our further analysis of chains suggests a centrality-based defense and monitoring mechanism to limit malware propagation. Finally, we propose a defense based on the observed measures, such as the blocked/blacklisted IP addresses or ports. In particular, we investigate network-level and country-level defenses, by blocking a list of ports that are not commonly used by benign applications, and study the underlying issues and possible solutions of such a defense

RAIDER: Reinforcement-aided Spear Phishing Detector

Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from zero-day attacks; unseen attacks unaccounted for in the training data. As new attacks emerge, classifiers trained on older data are unable to detect these new varieties of attacks resulting in increasingly inaccurate predictions. Spear Phishing detection also faces scalability challenges due to the growth of the required features which is proportional to the number of the senders within a receiver mailbox. This differs from traditional phishing attacks which typically perform only a binary classification between phishing and benign emails. Therefore, we devise a possible solution to these problems, named RAIDER: Reinforcement AIded Spear Phishing DEtectoR. A reinforcement-learning based feature evaluation system that can automatically find the optimum features for detecting different types of attacks. By leveraging a reward and penalty system, RAIDER allows for autonomous features selection. RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear-phishing attacks. After extensive evaluation of RAIDER over 11,000 emails and across 3 attack scenarios, our results suggest that using reinforcement learning to automatically identify the significant features could reduce the dimensions of the required features by 55% in comparison to existing ML-based systems. It also improves the accuracy of detecting spoofing attacks by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection accuracy even against a sophisticated attack named Known Sender in which spear-phishing emails greatly resemble those of the impersonated sender.Comment: 16 page