Security: Internet of Things
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1 Security: Internet of Things Based on Trusted Flows Kyle Haefner
2 Background - What is IoT Security? Security of the Internet of Things is just security at a larger scale -- Steve Lovaas Colorado State University IT Manager This is not the whole picture Single purpose devices a. Less complex to model 2. Effects of poor security implementations more profound. a. Pacemaker - stop you heart b. Thermostat - burn down your house
3 Background - IOT DDOS Mirai Botnet Repear Hajime 1.1 Tbps peak attack 400,000 devices offered for rent 2.5 Million infected devices 9 different exploits attacks Checkpoint security says it found the malware on 60% of networks it monitors. Whitehat vigilante botnet Blocks ports other botnets use as vectors TCP/23 (telnet), TCP/7547,TCP/5555,TCP/5358 Signed message, "just a white hat, securing some systems." DDOS
4 Top 10 IoT Vulnerabilities Insecure Web Interface 2. Insufficient Authentication/Authorization 3. Insecure Network Services 4. Lack of Transport Encryption/Integrity Verification 5. Privacy Concerns 6. Insecure Cloud Interface 7. Insecure Mobile Interface 8. Insufficient Security Configurability 9. Insecure Software/Firmware 10. Poor Physical Security Open Web Application Security Project OWASP Internet of Things Project
5 The IoT Security Triad Access Control Hardware Software Network
6 Hardwares Encryption Elliptic Curve (Smaller Keys, lower power) Verifiable Firmware Firmware must be signed Running firmware must be verified.
7 Software Vulnerabilities on device Insecure web interface Buffer overflows Vulnerabilities in protocols Unencrypted protocols Protocol Vulnerabilities Vulnerabilities in provider's cloud Weak Encryption Weak Authentication
8 Network Fingerprinting of device Clock Skew - Every clock source is unique (unique!= identity) Identify a device based on scans (ports, TCP windows etc) Behavior of Device Network trends and patterns of a device on a network over time. Theory: It is more important defining what a device does on the network that what a device is.
9 Trusted Flows - Introduction Stage 1: Stage 2: Learn device behavior on the network Develop a trust model based on this behavior (and a few other attributes) Leverage software defined networking to enforce trust model Use behavior as a way to classify device type Use semi-supervised machine learning to apply access controls to devices based on device type.
10 Trusted Flows - Network Flow RFC RFC 3697 defines traffic flow as a sequence of packets sent from a particular source to a particular unicast, anycast, or multicast destination that the source desires to label as a flow.
11 Trusted Flows - Defining Tuple of 5 IP header features (ip_src,ip_dst,ip_protocol,src_port,dst_port) Flow statistics (number of packets, bytes, packets per second, bytes per second,duration) Direction (Packets A->B, Bytes A->B, bps A->B)
12 Example of Network Stats
13 Trusted Flows - Device Behavior One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: RBF (Gaussian Kernel) Classifies new data as similar or different to the training set. Classifies new data as similar or different to the training set. Training set will be previous t-n windows of recent history (data) plus the exponential decay history Testing set will be t-1 windows of data.
14 Trusted Flows - One-Class SVM Online learning with exponential decay sampling. =w = flow stats for given time t Test Recent Train Samples Exponential Decay Train Samples -t
![Shodan[6] presence (present) or (none) = [0,1] or [1,0] Use of encryption Past behavior of device (B) How many outliers outside](/docs-images/90/102664527/images/15-2.jpg "of boundary over time? m is a factor to adjust the influence of outliers on the trust model.")
15 Trusted Flows - Trust Trust score based on: What we know about the device (K) Binary Values Certificate (valid) or (none/invalid) = [1,0] or [0,1] Common Vulnerability Scoring System[5] (CVSS) (CVSS = 0) = [1,0] or (CVSS > 0) = [0,1] Shodan[6] presence (present) or (none) = [0,1] or [1,0] Use of encryption Past behavior of device (B) How many outliers outside of boundary over time? m is a factor to adjust the influence of outliers on the trust model. F T F U netflow is allowed otherwise netflow is denied
16 Questions
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