Biblio

Concurrency vulnerabilities are extremely harmful and can be frequently exploited to launch severe attacks. Due to the non-determinism of multithreaded executions, it is very difficult to detect them. Recently, data race detectors and techniques based on maximal casual model have been applied to detect concurrency vulnerabilities. However, the former are ineffective and the latter report many false negatives. In this paper, we present CONVUL, an effective tool for concurrency vulnerability detection. CONVUL is based on exchangeable events, and adopts novel algorithms to detect three major kinds of concurrency vulnerabilities. In our experiments, CONVUL detected 9 of 10 known vulnerabilities, while other tools only detected at most 2 out of these 10 vulnerabilities. The 10 vulnerabilities are available at https://github.com/mryancai/ConVul.

The trending technological research platform is Internet of Things (IoT)and most probably it will stay that way for a while. One of the main application areas of IoT is Cyber-Physical Systems (CPSs), in which IoT devices can be leveraged as actuators and sensors in accordance with the system needs. The public acceptance and adoption of CPS services and applications will create a huge amount of privacy issues related to the processing, storage and disclosure of the user location information. As a remedy, our paper proposes a methodology to provide location privacy for the users of CPSs. Our proposal takes advantage of concepts such as mix-zone, context-awareness, and location-obfuscation. According to our best knowledge, the proposed methodology is the first privacy-preserving location service for CPSs that offers adaptable privacy levels related to the current context of the user.

Software has become an integral part of every industry and organization. Due to improvement in technology and lack of expertise in coding techniques, software vulnerabilities are increasing day-by-day in the software development sector. The time gap between the identification of the vulnerabilities and their automated exploit attack is decreasing. This gives rise to the need for detection and prevention of security risks and development of secure software. Earlier the security risk is identified and corrected the better it is. Developers needs a framework which can report the security flaws in their system and reduce the chances of exploitation of these flaws by some malicious user. Common Vector Scoring System (CVSS) is a De facto metrics system used to assess the exploitability of vulnerabilities. CVSS exploitability measures use subjective values based on the views of experts. It considers mainly two factors, Access Vector (AV) and Authentication (AU). CVSS does not specify on what basis the third-factor Access Complexity (AC) is measured, whether or not it considers software properties. Our objective is to come up with a framework that automates the process of identifying vulnerabilities using software structural properties. These properties could be attack entry points, vulnerability locations, presence of dangerous system calls, and reachability analysis. This framework has been tested on two open source softwares - Apache HTTP server and Mozilla Firefox.

Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.

BLE is used to transmit and receive data between sensors and devices. Most of the IOT devices employ BLE for wireless communication because it suits their requirements such as less energy constraints. The major security vulnerabilities in BLE protocol can be used by attacker to perform MITM attacks and hence violating confidentiality and integrity of data. Although BLE 4.2 prevents most of the attacks by employing elliptic-curve diffie-Hellman to generate LTK and encrypt the data, still there are many devices in the market that are using BLE 4.0, 4.1 which are vulnerable to attacks. This paper shows the simple demonstration of possible attacks on BLE devices that use various existing tools to perform spoofing, MITM and firmware attacks. We also discussed the security, privacy and its importance in BLE devices.

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

SDN is new networking concept which has revolutionized the network architecture in recent years. It decouples control plane from data plane. Architectural change provides re-programmability and centralized control management of the network. At the same time it also increases the complexity of underlying physical infrastructure of the network. Unfortunately, the centralized control of the network introduces new vulnerabilities and attacks. Attackers can exploit the limitation of centralized control by DDoS attack on control plane. The entire network can be compromised by DDoS attack. Based on packet entropy, a solution for mitigation of DDoS attack provided in the proposed scheme.

We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.

In this paper we propose a two-level hybrid anomalous activity detection model for intrusion detection in IoT networks. The level-1 model uses flow-based anomaly detection, which is capable of classifying the network traffic as normal or anomalous. The flow-based features are extracted from the CICIDS2017 and UNSW-15 datasets. If an anomaly activity is detected then the flow is forwarded to the level-2 model to find the category of the anomaly by deeply examining the contents of the packet. The level-2 model uses Recursive Feature Elimination (RFE) to select significant features and Synthetic Minority Over-Sampling Technique (SMOTE) for oversampling and Edited Nearest Neighbors (ENN) for cleaning the CICIDS2017 and UNSW-15 datasets. Our proposed model precision, recall and F score for level-1 were measured 100% for the CICIDS2017 dataset and 99% for the UNSW-15 dataset, while the level-2 model precision, recall, and F score were measured at 100 % for the CICIDS2017 dataset and 97 % for the UNSW-15 dataset. The predictor we introduce in this paper provides a solid framework for the development of malicious activity detection in IoT networks.

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.

Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.

Software diversity promises to invert the current balance of power in cybersecurity by preventing exploit reuse. Nevertheless, the comparative evaluation of diversity techniques has received scant attention. In ongoing work, we use the DARPA Cyber Grand Challenge (CGC) environment to assess the effectiveness of diversifying compilers in mitigating exploits. Our approach provides a quantitative comparison of diversity strategies and demonstrates wide variation in their effectiveness.

Live VM migration is the most vulnerable process in cloud federations for DDOS attacks, loss of data integrity, confidentiality, unauthorized access and injection of malicious viruses on VM disk images. We have scrutinized following set of crucial security features which are; authorization, confidentiality, replay protection (accountability), integrity, mutual authentication and source non-repudiation (availability) to cater different threats and vulnerabilities during live VM migration. The investigated threats and vulnerabilities are catered and implemented in a proposed solution, presented in this paper. Six security features-authorization, confidentiality, replay protection, integrity, mutual authentication and source non-repudiation are focused and modular implementation has been done. Solution is validated in AVISPA tool in modules for threats for all the notorious security requirements and no outbreak were seen.

This paper presents a survey on the main security problems that affect the communication protocols in the context of Internet of Things, in order to identify possible threats and vulnerabilities. The protocols RFID, NFC, 6LoWPAN, 6TiSCH, DTSL, CoAP and MQTT, for a better organization, were explored and categorized in layers according to the TCP / IP reference model. At the end, a summary is presented in tabular form with the security modes used for each protocol is used.

This paper provides a Common Vulnerability Scoring System (CVSS) metric-based technique for classifying and analysing the prevailing Computer Network Security Vulnerabilities and Threats (CNSVT). The problem that is addressed in this paper, is that, at the time of writing this paper, there existed no effective approaches for analysing and classifying CNSVT for purposes of assessments based on CVSS metrics. The authors of this paper have achieved this by generating a CVSS metric-based dynamic Vulnerability Analysis Classification Countermeasure (VACC) criterion that is able to rank vulnerabilities. The CVSS metric-based VACC has allowed the computation of vulnerability Similarity Measure (VSM) using the Hamming and Euclidean distance metric functions. Nevertheless, the CVSS-metric based on VACC also enabled the random measuring of the VSM for a selected number of vulnerabilities based on the [Ma-Ma], [Ma-Mi], [Mi-Ci], [Ma-Ci] ranking score. This is a technique that is aimed at allowing security experts to be able to conduct proper vulnerability detection and assessments across computer-based networks based on the perceived occurrence by checking the probability that given threats will occur or not. The authors have also proposed high-level countermeasures of the vulnerabilities that have been listed. The authors have evaluated the CVSS-metric based VACC and the results are promising. Based on this technique, it is worth noting that these propositions can help in the development of stronger computer and network security tools.

A Cyber Physical Sensor System (CPSS) consists of a computing platform equipped with wireless access points, sensors, and actuators. In a Cyber Physical System, CPSS constantly collects data from a physical object that is under process and performs local real-time control activities based on the process algorithm. The collected data is then transmitted through the network layer to the enterprise command and control center or to the cloud computing services for further processing and analysis. This paper investigates the CPSS' most common cyber security threats and vulnerabilities and provides countermeasures. Furthermore, the paper addresses how the CPSS are attacked, what are the leading consequences of the attacks, and the possible remedies to prevent them. Detailed case studies are presented to help the readers understand the CPSS threats, vulnerabilities, and possible solutions.

SQL injection is well known a method of executing SQL queries and retrieving sensitive information from a website connected database. This process poses a threat to those applications which are poorly coded in the today's world. SQL is considered as one of the top 10 vulnerabilities even in 2018. To keep a track of the vulnerabilities that each of the websites are facing, we employ a tool called Acunetix which allows us to find the vulnerabilities of a specific website. This tool also suggests measures on how to ensure preventive measures. Using this implementation, we discover vulnerabilities in an actual website. Such a real-world implementation would be useful for instructional use in a foundational cybersecurity course.

This research aims to identify some vulnerabilities of advanced persistent threat (APT) attacks using multiple simulated attacks in a virtualized environment. Our experimental study shows that while updating the antivirus software and the operating system with the latest patches may help in mitigating APTs, APT threat vectors could still infiltrate the strongest defenses. Accordingly, we highlight some critical areas of security concern that need to be addressed.

Wireless Sensor Network is the combination of small devices called sensor nodes, gateways and software. These nodes use wireless medium for transmission and are capable to sense and transmit the data to other nodes. Generally, WSN composed of two types of nodes i.e. generic nodes and gateway nodes. Generic nodes having the ability to sense while gateway nodes are used to route that information. IoT now extended to IoET (internet of Everything) to cover all electronics exist around, like a body sensor networks, VANET's, smart grid stations, smartphone, PDA's, autonomous cars, refrigerators and smart toasters that can communicate and share information using existing network technologies. The sensor nodes in WSN have very limited transmission range as well as limited processing speed, storage capacities and low battery power. Despite a wide range of applications using WSN, its resource constrained nature given birth to a number severe security attacks e.g. Selective Forwarding attack, Jamming-attack, Sinkhole attack, Wormhole attack, Sybil attack, hello Flood attacks, Grey Hole, and the most dangerous BlackHole Attacks. Attackers can easily exploit these vulnerabilities to compromise the WSN network.

If, as most experts agree, the mathematical basis of major blockchain systems is (probably if not provably) sound, why do they have a bad reputation? Human misbehavior (such as failed Bitcoin exchanges) accounts for some of the issues, but there are also deeper and more interesting vulnerabilities here. These include design faults and code-level implementation defects, ecosystem issues (such as wallets), as well as approaches such as the "51% attack" all of which can compromise the integrity of blockchain systems. With particular attention to the emerging non-financial applications of blockchain technology, this paper demonstrates the kinds of attacks that are possible and provides suggestions for minimizing the risks involved.

Cyber-attacks and intrusions in cyber-physical control systems are, currently, difficult to reliably prevent. Knowing a system's vulnerabilities and implementing static mitigations is not enough, since threats are advancing faster than the pace at which static cyber solutions can counteract. Accordingly, the practice of cybersecurity needs to ensure that intrusion and compromise do not result in system or environment damage or loss. In a previous paper [2], we described the Cyberspace Security Econometrics System (CSES), which is a stakeholder-aware and economics-based risk assessment method for cybersecurity. CSES allows an analyst to assess a system in terms of estimated loss resulting from security breakdowns. In this paper, we describe two new related contributions: 1) We map the Cyberspace Security Econometrics System (CSES) method to the evaluation and mitigation steps described by the NIST Guide to Industrial Control Systems (ICS) Security, Special Publication 800-82r2. Hence, presenting an economics-based and stakeholder-aware risk evaluation method for the implementation of the NIST-SP-800-82 guide; and 2) We describe the application of this tailored method through the use of a fictitious example of a critical infrastructure system of an electric and gas utility.

The discussion of threats and vulnerabilities in Industrial Control Systems has gained popularity during the last decade due to the increase in interest and growing concern to secure these systems. In order to provide an overview of the complete landscape of these threats and vulnerabilities this contribution provides a tiered security analysis of the assets that constitute Industrial Control Systems. The identification of assets is obtained from a generalization of the system's architecture. Additionally, the security analysis is complemented by discussing security countermeasures and solutions that can be used to counteract the vulnerabilities and increase the security of control systems.

Current threat models typically consider all possible ways an attacker can penetrate a system and assign probabilities to each path according to some metric (e.g. time-to-compromise). In this paper we discuss how this view hinders the realness of both technical (e.g. attack graphs) and strategic (e.g. game theory) approaches of current threat modeling, and propose to steer away by looking more carefully at attack characteristics and attacker environment. We use a toy threat model for ICS attacks to show how a realistic view of attack instances can emerge from a simple analysis of attack phases and attacker limitations.

Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.

In the production process of embedded device, due to the frequent reuse of third-party libraries or development kits, there are large number of same vulnerabilities that appear in more than one firmware. Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis, the widely used methods are mainly Binary difference analysis, Normalized compression distance, String feature matching and Fuzz hash. But when we use these methods for homology analysis, we found that the detection result is not ideal and there is a high false positive rate. Focusing on this problem, we analyzed the application scenarios of these four methods and their limitations by combining different methods and different types of files and the experiments show that the combination of methods and files have a better performance in homology analysis.