Anomaly detection of CAN bus messages through analysis of ID sequences

This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. The proposed algorithm identifies anomalies in the sequence of messages that flow in the CAN bus and is characterized by small memory and computational footprints, that make it applicable to current ECUs. Its detection performance are demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle

Detecting attacks to internal vehicle networks through Hamming distance

Analysis of in-vehicle networks is an open research area that gained relevance after recent reports of cyber attacks against connected vehicles. After those attacks gained international media attention, many security researchers started to propose different algorithms that are capable to model the normal behaviour of the CAN bus to detect the injection of malicious messages. However, despite the automotive area has different constraint than classical IT security, many security research have been conducted by applying sophisticated algorithm used in IT anomaly detection, thus proposing solutions that are not applicable on current Electronic Control Units (ECUs). This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. Moreover, the proposed algorithm has been designed and implemented with the very strict constraint of low-end ECUs, having low computational complexity and small memory footprints. The proposed algorithm identifies anomalies in the sequence of the payloads of different classes of IDs by computing the Hamming distance between consecutive payloads. Its detection performance are evaluated through experiments carried out using real CAN traffic gathered from an unmodified licensed vehicle

Verifiable Delegated Authorization for User-Centric Architectures and an OAuth2 Implementation

Delegated authorization protocols have become wide-spread to implement Web applications and services, where some popular providers managing people identity information and personal data allow their users to delegate third party Web services to access their data. In this paper, we analyze the risks related to untrusted providers not behaving correctly, and we solve this problem by proposing the first verifiable delegated authorization protocol that allows third party services to verify the correctness of users data returned by the provider. The contribution of the paper is twofold: we show how delegated authorization can be cryptographically enforced through authenticated data structures protocols, we extend the standard OAuth2 protocol by supporting efficient and verifiable delegated authorization including database updates and privileges revocation

A symmetric cryptographic scheme for data integrity verification in cloud databases

Cloud database services represent a great opportunity for companies and organizations in terms of management and cost savings. However, outsourcing private data to external providers leads to risks of confidentiality and integrity violations. We propose an original solution based on encrypted Bloom filters that addresses the latter problem by allowing a cloud service user to detect unauthorized modifications to his outsourced data. Moreover, we propose an original analytical model that can be used to minimize storage and network overhead depending on the database structure and workload. We assess the effectiveness of the proposal as well as its performance improvements with respect to existing solutions by evaluating storage and network costs through micro-benchmarks and the TPC-C workload standard

Detection and Threat Prioritization of Pivoting Attacks in Large Networks

Several advanced cyber attacks adopt the technique of "pivoting" through which attackers create a command propagation tunnel through two or more hosts in order to reach their final target. Identifying such malicious activities is one of the most tough research problems because of several challenges: command propagation is a rare event that cannot be detected through signatures, the huge amount of internal communications facilitates attackers evasion, timely pivoting discovery is computationally demanding. This paper describes the first pivoting detection algorithm that is based on network flows analyses, does not rely on any a-priori assumption on protocols and hosts, and leverages an original problem formalization in terms of temporal graph analytics. We also introduce a prioritization algorithm that ranks the detected paths on the basis of a threat score thus letting security analysts investigate just the most suspicious pivoting tunnels. Feasibility and effectiveness of our proposal are assessed through a broad set of experiments that demonstrate its higher accuracy and performance against related algorithms

Vehicle Safe-Mode, Limp-Mode in the Service of Cyber Security

This paper describes a concept for vehicle safe-mode, that may help reduce the potential damage of an identified cyber-attack. Unlike other defense mechanisms, that try to block the attack or simply notify of its existence, our mechanism responds to the detected breach, by limiting the vehicle\u2019s functionality to relatively safe operations, and optionally activating additional security counter-measures. This is done by adopting the already existing mechanism of Limp-mode, that was originally designed to limit the potential damage of either a mechanical or an electrical malfunction and let the vehicle \u201climp back home\u201d in relative safety. We further introduce two modes of safe-modemoperation: In Transparent-mode, when a cyber-attack is detected the vehicle enters its pre-configured Limp-mode; In Extended-mode we suggest to use custom messages that offer additional flexibility to both the reaction and the recovery plans. While Extended-mode requires modifications to the participating ECUs, Transparent-mode may be applicable to existing vehicles since it does not require any changes in the vehicle\u2019s systems\u2014in other words, it may even be deployed as an external component connected through the OBD-II port. We suggest an architectural design for the given modes, and include guidelines for a safe-mode manager, its clients, possible reactions, and recovery plans. We note that our system can rely upon any deployed anomaly-detection system to identify the potential attack

Scalable architecture for online prioritization of cyber threats

This paper proposes an innovative framework for the early detection of several cyber attacks, where the main component is an analytics core that gathers streams of raw data generated by network probes, builds several layer models representing different activities of internal hosts, analyzes intra-layer and inter-layer information. The online analysis of internal network activities at different levels distinguishes our approach with respect to most detection tools and algorithms focusing on separate network levels or interactions between internal and external hosts. Moreover, the integrated multi-layer analysis carried out through parallel processing reduces false positives and guarantees scalability with respect to the size of the network and the number of layers. As a further contribution, the proposed framework executes autonomous triage by assigning a risk score to each internal host. This key feature allows security experts to focus their attention on the few hosts with higher scores rather than wasting time on thousands of daily alerts and false alarms

Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models

Identifying malicious hosts involved in periodic communications

After many research efforts, Network Intrusion Detection Systems still have much room for improvement. This paper proposes a novel method for automatic and timely analysis of traffic generated by large networks, which is able to identify malicious external hosts even if their activities do not raise any alert by existing defensive systems. Our proposal focuses on periodic communications, since our experimental evaluation shows that they are more related to malicious activities, and it can be easily integrated with other detection systems. We highlight that periodic network activities can occur at very different intervals ranging from seconds to hours, hence a timely analysis of long time-windows of the traffic generated by large organizations is a challenging task in itself. Existing work is primarily focused on identifying botnets, whereas the method proposed in this paper has a broader target and aims to detect external hosts that are likely involved in any malicious operation. Since malware-related network activities can be considered as rare events in the overall traffic, the output of the proposed method is a manageable graylist of external hosts that are characterized by a considerably higher likelihood of being malicious compared to the entire set of external hosts contacted by the monitored large network. A thorough evaluation on a real large network traffic demonstrates the effectiveness of our proposal, which is capable of automatically selecting only dozens of suspicious hosts from hundreds of thousands, thus allowing security operators to focus their analyses on few likely malicious targets

Scalable architecture for multi-user encrypted SQL operations on cloud database services

Abstract-The success of the cloud database paradigm is strictly related to strong guarantees in terms of service availability, scalability and security, but also of data confidentiality. Any cloud provider assures the security and availability of its platform, while the implementation of scalable solutions to guarantee confidentiality of the information stored in cloud databases is an open problem left to the tenant. Existing solutions address some preliminary issues through SQL operations on encrypted data. We propose the first complete architecture that combines data encryption, key management, authentication and authorization solutions, and that addresses the issues related to typical threat scenarios for cloud database services. Formal models describe the proposed solutions for enforcing access control and for guaranteeing confidentiality of data and metadata. Experimental evaluations based on standard benchmarks and real Internet scenarios show that the proposed architecture satisfies also scalability and performance requirements