📝 Security Association in IPSec (19 MCQs)

📖 From Data Communication and Networks • 32. Internet Security • 19 questions available

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What is Security Association in IPSec?

A Security Association (SA) is a one-way logical connection between two devices that defines the specific security parameters, such as encryption algorithms, keys, and lifetimes, required to protect traffic flowing in that direction.

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📝 All Security Association in IPSec MCQs

Q1. What does the symbol $\Delta$ represent in delta modulation?

A.step size ✅

B.frequency

C.amplitude

D.phase

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: The symbol $\Delta$ denotes the quantization step size used to approximate the slope of the input signal. In delta modulation the step size determines how far the reconstructed staircase moves at each sample, directly affecting tracking accuracy and error types.

Q2. In DPCM, which option correctly identifies the role of the coefficient $a_i$ ?

A.quantization level

B.sampling rate

C.predictor weight ✅

D.filter order

💡 Difficulty: easy | ✅ Correct: C

📖 Explanation: The coefficient $a_i$ is the weight applied to a past reconstructed sample in the predictor sum. It determines how much influence each delayed sample has on the predicted value, making it the predictor’s coefficient.

Q3. If $\Delta$ is too small relative to the signal slope, which distortion becomes dominant?

A.granular noise

B.slope overload ✅

C.aliasing

D.quantization error

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: When the step size cannot keep up with a rapidly changing slope, the staircase lags behind the true signal, producing slope‑overload distortion. The error grows because each step is insufficient to follow the steep portion of the waveform.

Q4. Why does adaptive DM adjust $\Delta$ from one step to the next?

A.to balance errors

B.to eliminate noise

C.to maximize compression

D.to simplify hardware ✅

💡 Difficulty: medium | ✅ Correct: D

📖 Explanation: Adaptive DM changes $\Delta$ in response to recent bit patterns so that the system can trade off slope‑overload distortion against granular noise. By balancing the two error sources, the overall reconstruction error is minimized.

Q5. Given $\Delta_n = M_n \Delta_{n-1}$ and $M_n$ increases by 10 % when $q_n$ stays the same, what is the approximate increase after three identical bits?

A.doubles

B.remains the same

C.increases by ~33 % ✅

D.decreases

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Each identical bit multiplies $\Delta$ by 1.10. After three steps the factor is $1.1^3 \approx 1.331$ , meaning the step size grows by roughly 33 %, which matches the third option.

Q6. When a region’s predictor error variance is high, which adaptation best reduces overall distortion?

A.decrease step size

B.increase predictor order ✅

C.lower coefficient magnitude

D.introduce dead zone

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: A higher predictor order incorporates more past samples, improving the model’s ability to follow rapid changes. This reduces the variance of the prediction error, leading to lower overall distortion despite the increased computational load.

Q7. Which statement best describes the error behavior of DPCM compared with DM for signals containing both high‑ and low‑slope segments?

A.DPCM reduces error ✅

B.DM has constant error

C.DM adapts

D.DPCM cannot handle low slopes

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: DPCM uses a higher‑order predictor, allowing it to better follow both steep and flat portions of the waveform. Consequently it generally yields lower reconstruction error than simple DM, which relies on a single‑sample predictor.

Q8. How does delaying the coding process in ADM to include future samples affect the stability of $\Delta$ adaptation?

A.causes instability

B.improves stability ✅

C.has no effect

D.reduces latency

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: By looking ahead, the algorithm can anticipate rapid slope changes and adjust $\Delta$ more accurately, smoothing abrupt variations. This predictive insight reduces oscillations in the step‑size sequence, thereby enhancing stability.

Q9. Which relationship correctly describes granular noise and slope overload distortion with respect to $\Delta$ ?

A.both increase with larger

\Delta

B.both decrease with larger

\Delta

C.granular noise ↑ with larger

\Delta

, slope overload ↓ ✅

D.unrelated

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: A larger step size makes the staircase jump farther, which raises granular noise because the quantizer overshoots flat regions. Conversely, a larger $\Delta$ helps the staircase keep up with steep slopes, reducing slope‑overload distortion.

Q10. Why does minimizing cumulative squared error lead to optimal predictor coefficients in LPC?

A.penalizes large deviations

B.linearizes the system ✅

C.simplifies computation

D.matches human perception

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: The squared‑error criterion gives a least‑squares formulation that heavily penalizes large prediction errors. Solving the resulting normal equations yields coefficients that minimize the overall error energy, providing the optimal linear predictor under this metric.

Q11. In ADPCM, how does varying $\Delta$ achieve higher compression for speech signals?

A.larger

\Delta

in quiet parts

B.smaller

\Delta

in voiced parts

C.both increase bitrate

D.none of the above ✅

💡 Difficulty: easy | ✅ Correct: D

📖 Explanation: Quiet portions contain low‑amplitude variations; using a larger step size there reduces the number of bits needed to represent the signal while preserving intelligibility, thereby increasing compression efficiency.

Q12. Given a predictor of order $N=2$ with coefficients $a_1=0.6$ and $a_2=-0.2$ , which equation correctly predicts the current sample?

\\hat{y}_n = 0.6 y_{n-1} - 0.2 y_{n-2}

✅

\\hat{y}_n = 0.2 y_{n-1} + 0.6 y_{n-2}

\\hat{y}_n = y_{n-1} + y_{n-2}

\\hat{y}_n = 0.6 y_{n-1} + 0.2 y_{n-2}

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: The predictor sum uses each coefficient multiplied by its corresponding delayed sample. Substituting the given values yields $\\hat{y}_n = 0.6 y_{n-1} - 0.2 y_{n-2}$ , which is the first option.

Q13. What is the primary benefit of jointly optimizing adaptive step‑size $M_n$ and predictor coefficients?

A.higher latency

B.increased complexity only

C.lower distortion ✅

D.no benefit

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: When both the quantizer step size and the predictor are tuned to the local signal characteristics, the codec can simultaneously reduce granular noise and slope‑overload errors, resulting in noticeably lower overall distortion compared with adapting only one component.

Q14. Why is the method of square error still relevant to predictor design even if its derivation is omitted?

A.requires iterative algorithms

B.provides closed‑form solution ✅

C.uses Fourier analysis

D.is unrelated

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: The least‑squares approach yields analytical expressions for the optimal coefficients, allowing designers to compute them directly from sample statistics without resorting to costly iterative methods.

Q15. In a signal with alternating steep and flat sections, which adaptation rule for $M_n$ best minimizes total error?

A.decrease after flat, increase after steep

B.increase after flat, decrease after steep

C.both increase

D.random changes ✅

💡 Difficulty: medium | ✅ Correct: D

📖 Explanation: Increasing the step‑size multiplier after detecting a flat region (to reduce granular noise) and decreasing it after a steep region (to avoid slope overload) directly addresses the two dominant error sources, yielding the lowest combined error.

Q16. The statement \In DM, the predictor order is always 1\ is:

A.true ✅

B.false

C.Depends on implementation

D.NOT APPLICABLE

💡 Difficulty: easy | ✅ Correct: A

Q17. How does the computational complexity of ADPCM compare with that of DPCM?

A.ADPCM higher

B.DPCM higher ✅

C.equal

D.ADPCM lower

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: ADPCM must update both the predictor coefficients and the step‑size multiplier at runtime, requiring additional arithmetic and memory accesses beyond the fixed‑coefficient operations of standard DPCM, thus increasing computational load.

Q18. What trade‑off arises when the percentage change applied to $M_n$ is increased in ADM?

A.larger percentage yields faster convergence but risk instability ✅

B.smaller percentage yields slower but stable convergence

C.no effect on convergence

D.opposite of above

💡 Difficulty: hard | ✅ Correct: A

📖 Explanation: A larger adjustment factor lets the step size react quickly to changing signal dynamics, shortening the adaptation period. However, aggressive changes can cause overshoot and oscillations, potentially destabilizing the modulation process.

Q19. Why are adaptive quantizer $\Delta$ and predictor coefficients often jointly encoded in speech codecs?

A.to exploit correlation

B.to reduce bitrate ✅

C.to simplify decoding

D.to increase robustness

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: By adapting both the quantizer and the predictor to the same signal characteristics, the codec can represent the signal more efficiently, allowing fewer bits to convey the same perceptual quality, which directly reduces the overall bitrate.

🔗 Related Topics (MCQs)

📝 IPSec network layer security MCQs 📝 IPSec transport and tunnel modes: Two Modes MCQs 📝 IPSec AH and ESP Two Security Protocols MCQs 📝 IPSec security services MCQs 📝 IKE Internet Key Exchange protocol MCQs

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